Data Analyst Master Program
An intensive 4-month online weekend live engineering cohort (Sat & Sun • 4 hours/day: 2h live faculty lectures + 2h supervised coding labs) covering Data Analyst production capstones and 580+ AI interviews, with eligible hiring-drive access under documented placement terms and tuition-refund protection where all policy conditions are met.
- Online Weekend Live Sessions (Sat & Sun • 4 Hours / Day)
- 4 Months (16 Weeks) Comprehensive Finish
- Guaranteed Placement Drives until Placed across 1,050+ top companies
- 26 Production Microservices & Capstones with GitHub code reviews
- 580+ Topic Mock Tests & 1-on-1 AI Voice Technical Interviews
Our Graduates Get Marketed to 1,050+ Global Tech Leaders & Unicorns
Continuous corporate interview referrals until job offer letter issuance:
Online Weekend Master Track: 4 Months of Live Mentorship & Placement Drives
Designed for college students and working professionals, our Online Weekend Master Track delivers intensive 4-hour live sessions every Saturday and Sunday across 16 weeks (4 Months). Complete 128+ hours of live faculty instruction, 26 production capstones, and 580+ AI interviews with unlimited corporate interview drives until you get placed!
Live Architecture & Core Mentorship
2 Hours of interactive enterprise architecture, live faculty coding, and design patterns followed by 2 Hours of supervised capstone development.
Hands-On Labs, Tests & AI Practice
2 Hours of advanced microservices, real-time queues & cloud deployment followed by 2 Hours of timed mock tests and 1-on-1 AI voice interview rounds.
4-Month Master Roadmap to Guaranteed Placement
We transform you into a battle-tested software engineer ready to clear Tier-1 technical and system design interview rounds in 4 months with weekend online sessions.
Architecture & Core Mechanics
Core OOP, memory mechanics, data structures, algorithms & clean design patterns.
Capstones & Microservices
Build production full stack SaaS, microservices, REST APIs, queues & cloud deployment.
System Design & AI Interviews
Simulate live FAANG interview rounds, timed topic mock tests and AI voice evaluations.
Corporate Drives until Placed
Resume marketing, hiring drives across 1,050+ partners, and placement guarantee.
Projected Target CTC After Program
Industry-verified compensation brackets achieved by graduates across 1,050+ hiring partners:
₹8.5L – ₹14L /yr
Software Engineer I, Junior Backend Developer, Full Stack Associate.
- 260+ Hours Live Mentorship
- 26 Capstone Projects on GitHub
- 580+ AI Technical Interview Scorecards
₹14L – ₹24L /yr
Full Stack Java Engineer, Spring Boot Microservices Specialist, Cloud Engineer.
- Kafka Event-Driven Architectures
- Redis Caching & Performance Tuning
- Docker, Kubernetes & AWS CI/CD
₹24L – ₹36L+ /yr
Senior Full Stack Engineer, Microservices Architect, Lead Consultant.
- High-Throughput System Design (LLD/HLD)
- Fault Tolerance & Distributed Transactions
- FAANG System Design Clearing Mentorship
Topic-Wise Curriculum & Practice Hub
50 Modules • 370 Deep-Dive Topics • 370 Integrated Topic Mock Tests & AI Interviews
What Is Data Analytics?, Types of Analytics, Role of Data Analyst, Data Analyst Workflow.
What Is Data Analytics?
Data; information; analytics; reporting; business intelligence; Data Science; Data Analytics.
- Explain the core concepts and architecture of What Is Data Analytics?.
- Apply What Is Data Analytics? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Types of Analytics
Descriptive Analytics; What happened?; Diagnostic Analytics; Why did it happen?; Predictive Analytics; What might happen?; Prescriptive Analytics; What action should we consider?
- Explain the core concepts and architecture of Types of Analytics.
- Apply Types of Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Role of Data Analyst
Typical responsibilities; Understand business requirements; gather data; clean data; analyze data; create dashboards; communicate insights; monitor KPIs.
- Explain the core concepts and architecture of Role of Data Analyst.
- Apply Role of Data Analyst in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Analyst Workflow
Business Question; then; Data; then; Cleaning; then; Analysis; then; Visualization; then; Insight; then; Recommendation.
- Explain the core concepts and architecture of Data Analyst Workflow.
- Apply Data Analyst Workflow in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Business Problem Framing, Metrics vs KPIs, Business Dimensions, Business Measures, Root Cause Thinking.
Business Problem Framing
Weak question; Show sales; Better question; Which regions experienced declining revenue during the last quarter and what factors contributed?
- Explain the core concepts and architecture of Business Problem Framing.
- Apply Business Problem Framing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Metrics vs KPIs
Metric; Any measurable quantity; KPI; A strategically important metric.
- Explain the core concepts and architecture of Metrics vs KPIs.
- Apply Metrics vs KPIs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Business Dimensions
Customer; Product; Region; Time; Channel; Category.
- Explain the core concepts and architecture of Business Dimensions.
- Apply Business Dimensions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Business Measures
Revenue; profit; orders; conversion; churn; retention.
- Explain the core concepts and architecture of Business Measures.
- Apply Business Measures in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Root Cause Thinking
Revenue then; Possible drivers; Customers then; or; Order Frequency then; or; Average Order Value then.
- Explain the core concepts and architecture of Root Cause Thinking.
- Apply Root Cause Thinking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Excel Interface, Data Entry, Formatting, Number Formats, Sort & Filter, Freeze Panes, Tables.
Excel Interface
Workbook; worksheet; rows; columns; cells; ranges.
- Explain the core concepts and architecture of Excel Interface.
- Apply Excel Interface in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Entry
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data Entry.
- Apply Data Entry in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Formatting
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Formatting.
- Apply Formatting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Number Formats
Currency; percentage; dates.
- Explain the core concepts and architecture of Number Formats.
- Apply Number Formats in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sort & Filter
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Sort & Filter.
- Apply Sort & Filter in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Freeze Panes
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Freeze Panes.
- Apply Freeze Panes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tables
Convert raw ranges into structured Excel tables.
- Explain the core concepts and architecture of Tables.
- Apply Tables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Arithmetic Functions, Conditional Functions, Conditional Aggregation, Lookup Functions, Text Functions, Date Functions, Dynamic Array Concepts.
Arithmetic Functions
SUM; AVERAGE; MIN; MAX; COUNT.
- Explain the core concepts and architecture of Arithmetic Functions.
- Apply Arithmetic Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Functions
IF; IFS; AND; OR; IFERROR.
- Explain the core concepts and architecture of Conditional Functions.
- Apply Conditional Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Aggregation
SUMIF; SUMIFS; COUNTIF; COUNTIFS; AVERAGEIF; AVERAGEIFS.
- Explain the core concepts and architecture of Conditional Aggregation.
- Apply Conditional Aggregation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lookup Functions
XLOOKUP; VLOOKUP concepts; INDEX; MATCH; Emphasize modern lookup patterns.
- Explain the core concepts and architecture of Lookup Functions.
- Apply Lookup Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Text Functions
LEFT; RIGHT; MID; TRIM; CLEAN; CONCAT; TEXTJOIN; SUBSTITUTE.
- Explain the core concepts and architecture of Text Functions.
- Apply Text Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Date Functions
TODAY; NOW; DATE; YEAR; MONTH; DAY; EOMONTH; DATEDIF concepts.
- Explain the core concepts and architecture of Date Functions.
- Apply Date Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dynamic Array Concepts
Introduce modern Excel features where supported; FILTER; SORT; UNIQUE.
- Explain the core concepts and architecture of Dynamic Array Concepts.
- Apply Dynamic Array Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Validation, Conditional Formatting, Named Ranges, Advanced Lookup Problems, Error Handling, What-If Analysis.
Data Validation
Create controlled input.
- Explain the core concepts and architecture of Data Validation.
- Apply Data Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Formatting
Highlight; exceptions; duplicates; trends; thresholds.
- Explain the core concepts and architecture of Conditional Formatting.
- Apply Conditional Formatting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Named Ranges
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Named Ranges.
- Apply Named Ranges in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced Lookup Problems
Two-way lookup; multi-condition lookup; approximate lookup concepts.
- Explain the core concepts and architecture of Advanced Lookup Problems.
- Apply Advanced Lookup Problems in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Error Handling
Identify; #N/A; #VALUE!; #REF!; #DIV/0!
- Explain the core concepts and architecture of Error Handling.
- Apply Error Handling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
What-If Analysis
Concepts; Goal Seek; scenarios.
- Explain the core concepts and architecture of What-If Analysis.
- Apply What-If Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PivotTable Fundamentals, Rows / Columns / Values / Filters, Aggregations, Grouping, Calculated Fields Concepts, Slicers, Timelines, PivotCharts.
PivotTable Fundamentals
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of PivotTable Fundamentals.
- Apply PivotTable Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rows / Columns / Values / Filters
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Rows / Columns / Values / Filters.
- Apply Rows / Columns / Values / Filters in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Aggregations
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Aggregations.
- Apply Aggregations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Grouping
Dates; Year to Quarter to Month.
- Explain the core concepts and architecture of Grouping.
- Apply Grouping in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Calculated Fields Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Calculated Fields Concepts.
- Apply Calculated Fields Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Slicers
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Slicers.
- Apply Slicers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Timelines
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Timelines.
- Apply Timelines in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PivotCharts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of PivotCharts.
- Apply PivotCharts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dashboard Principles, KPI Cards, Interactive Filters, Chart Selection, Dashboard Layout, Executive Dashboard Design.
Dashboard Principles
A dashboard should answer business questions.
- Explain the core concepts and architecture of Dashboard Principles.
- Apply Dashboard Principles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
KPI Cards
Revenue; Profit; Growth; Customers.
- Explain the core concepts and architecture of KPI Cards.
- Apply KPI Cards in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Interactive Filters
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Interactive Filters.
- Apply Interactive Filters in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Chart Selection
Choose based on question.
- Explain the core concepts and architecture of Chart Selection.
- Apply Chart Selection in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dashboard Layout
Use visual hierarchy.
- Explain the core concepts and architecture of Dashboard Layout.
- Apply Dashboard Layout in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Executive Dashboard Design
Avoid; excessive colors; unnecessary charts; decoration; 3D charts.
- Explain the core concepts and architecture of Executive Dashboard Design.
- Apply Executive Dashboard Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Power Query Fundamentals, Import Data, Data Types, Remove / Rename Columns, Filter Rows, Replace Values, Handle Null Values, Split Columns, Merge Columns, Pivot / Unpivot, Merge Queries, Append Queries, Conditional Columns, Custom Columns, Group By, Folder-Based Automation, Query Dependencies, M Language Concepts.
Power Query Fundamentals
Extract to Transform to Load.
- Explain the core concepts and architecture of Power Query Fundamentals.
- Apply Power Query Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Import Data
Excel; CSV; folders; databases; web/API concepts.
- Explain the core concepts and architecture of Import Data.
- Apply Import Data in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Types
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data Types.
- Apply Data Types in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Remove / Rename Columns
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Remove / Rename Columns.
- Apply Remove / Rename Columns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filter Rows
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Filter Rows.
- Apply Filter Rows in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Replace Values
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Replace Values.
- Apply Replace Values in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Handle Null Values
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Handle Null Values.
- Apply Handle Null Values in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Split Columns
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Split Columns.
- Apply Split Columns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Merge Columns
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Merge Columns.
- Apply Merge Columns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pivot / Unpivot
Critical analytics transformation.
- Explain the core concepts and architecture of Pivot / Unpivot.
- Apply Pivot / Unpivot in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Merge Queries
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Merge Queries.
- Apply Merge Queries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Append Queries
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Append Queries.
- Apply Append Queries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Columns
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Conditional Columns.
- Apply Conditional Columns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Custom Columns
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Custom Columns.
- Apply Custom Columns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Group By
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Group By.
- Apply Group By in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Folder-Based Automation
Automatically combine recurring files.
- Explain the core concepts and architecture of Folder-Based Automation.
- Apply Folder-Based Automation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Query Dependencies
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Query Dependencies.
- Apply Query Dependencies in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
M Language Concepts
Introduction only.
- Explain the core concepts and architecture of M Language Concepts.
- Apply M Language Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Database Concepts, Relational Databases, Primary Keys, Foreign Keys, Relationships, Normalization Concepts.
Database Concepts
Database; schema; table; rows; columns.
- Explain the core concepts and architecture of Database Concepts.
- Apply Database Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Relational Databases
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Relational Databases.
- Apply Relational Databases in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Primary Keys
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Primary Keys.
- Apply Primary Keys in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Foreign Keys
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Foreign Keys.
- Apply Foreign Keys in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Relationships
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Relationships.
- Apply Relationships in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Normalization Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Normalization Concepts.
- Apply Normalization Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SELECT, DISTINCT, WHERE, AND / OR, IN, BETWEEN, LIKE, NULL, ORDER BY, LIMIT.
SELECT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SELECT.
- Apply SELECT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DISTINCT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of DISTINCT.
- Apply DISTINCT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
WHERE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of WHERE.
- Apply WHERE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AND / OR
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of AND / OR.
- Apply AND / OR in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
IN
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of IN.
- Apply IN in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
BETWEEN
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of BETWEEN.
- Apply BETWEEN in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LIKE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of LIKE.
- Apply LIKE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
NULL
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of NULL.
- Apply NULL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ORDER BY
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of ORDER BY.
- Apply ORDER BY in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LIMIT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of LIMIT.
- Apply LIMIT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
COUNT, SUM, AVG, MIN / MAX, GROUP BY, HAVING.
COUNT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of COUNT.
- Apply COUNT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SUM
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SUM.
- Apply SUM in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AVG
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of AVG.
- Apply AVG in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MIN / MAX
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of MIN / MAX.
- Apply MIN / MAX in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
GROUP BY
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of GROUP BY.
- Apply GROUP BY in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HAVING
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of HAVING.
- Apply HAVING in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
INNER JOIN, LEFT JOIN, RIGHT JOIN Concepts, FULL JOIN Concepts, Self Join, Multiple Joins.
INNER JOIN
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of INNER JOIN.
- Apply INNER JOIN in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LEFT JOIN
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of LEFT JOIN.
- Apply LEFT JOIN in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RIGHT JOIN Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of RIGHT JOIN Concepts.
- Apply RIGHT JOIN Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
FULL JOIN Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of FULL JOIN Concepts.
- Apply FULL JOIN Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Self Join
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Self Join.
- Apply Self Join in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multiple Joins
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Multiple Joins.
- Apply Multiple Joins in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
String Functions, Date Functions, Numeric Functions, CASE WHEN, COALESCE, NULLIF.
String Functions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of String Functions.
- Apply String Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Date Functions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Date Functions.
- Apply Date Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Numeric Functions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Numeric Functions.
- Apply Numeric Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CASE WHEN
Critical business logic; HIGH_VALUE; MEDIUM_VALUE; LOW_VALUE.
- Explain the core concepts and architecture of CASE WHEN.
- Apply CASE WHEN in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
COALESCE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of COALESCE.
- Apply COALESCE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
NULLIF
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of NULLIF.
- Apply NULLIF in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Subqueries, CTE, Window Functions, Running Totals, Moving Average, Ranking, Month-over-Month Growth, Year-over-Year Growth, Percent Contribution, First / Last Purchase, Customer Lifetime Metrics.
Subqueries
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Subqueries.
- Apply Subqueries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CTE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of CTE.
- Apply CTE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Window Functions
Critical for analyst interviews; ROW_NUMBER; RANK; DENSE_RANK; LAG; LEAD; SUM OVER; AVG OVER.
- Explain the core concepts and architecture of Window Functions.
- Apply Window Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Running Totals
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Running Totals.
- Apply Running Totals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Moving Average
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Moving Average.
- Apply Moving Average in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Ranking
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Ranking.
- Apply Ranking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Month-over-Month Growth
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Month-over-Month Growth.
- Apply Month-over-Month Growth in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Year-over-Year Growth
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Year-over-Year Growth.
- Apply Year-over-Year Growth in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Percent Contribution
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Percent Contribution.
- Apply Percent Contribution in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
First / Last Purchase
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of First / Last Purchase.
- Apply First / Last Purchase in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Lifetime Metrics
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Customer Lifetime Metrics.
- Apply Customer Lifetime Metrics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Funnel Analysis, Cohort Analysis, Retention, Repeat Customer Rate, Customer Segmentation, Product Ranking, Revenue Analysis, Churn Analysis.
Funnel Analysis
Visit; then; Registration; then; Trial; then; Purchase.
- Explain the core concepts and architecture of Funnel Analysis.
- Apply Funnel Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cohort Analysis
Group customers by acquisition month.
- Explain the core concepts and architecture of Cohort Analysis.
- Apply Cohort Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retention
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Retention.
- Apply Retention in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Repeat Customer Rate
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Repeat Customer Rate.
- Apply Repeat Customer Rate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Segmentation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Customer Segmentation.
- Apply Customer Segmentation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Product Ranking
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Product Ranking.
- Apply Product Ranking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Revenue Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Revenue Analysis.
- Apply Revenue Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Churn Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Churn Analysis.
- Apply Churn Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
What Is Power BI?, Power BI Workflow, Data Sources.
What Is Power BI?
Power BI Desktop; Power BI Service; reports; dashboards; semantic models.
- Explain the core concepts and architecture of What Is Power BI?.
- Apply What Is Power BI? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Power BI Workflow
Data; then; Power Query; then; Model; then; DAX; then; Visuals; then; Report; then; Publish.
- Explain the core concepts and architecture of Power BI Workflow.
- Apply Power BI Workflow in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Sources
Connect to; Excel; CSV; SQL; folders; web sources concepts.
- Explain the core concepts and architecture of Data Sources.
- Apply Data Sources in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tables, Relationships, Cardinality, Filter Direction, Fact Tables, Dimensions, Star Schema, Snowflake Schema, Date Dimension, Role-Playing Dimensions Concepts, Model Optimization.
Tables
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Tables.
- Apply Tables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Relationships
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Relationships.
- Apply Relationships in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cardinality
One-to-many; one-to-one concepts; many-to-many concepts.
- Explain the core concepts and architecture of Cardinality.
- Apply Cardinality in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filter Direction
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Filter Direction.
- Apply Filter Direction in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Fact Tables
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Fact Tables.
- Apply Fact Tables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dimensions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Dimensions.
- Apply Dimensions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Star Schema
Preferred analytics model in many scenarios.
- Explain the core concepts and architecture of Star Schema.
- Apply Star Schema in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Snowflake Schema
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Snowflake Schema.
- Apply Snowflake Schema in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Date Dimension
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Date Dimension.
- Apply Date Dimension in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Role-Playing Dimensions Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Role-Playing Dimensions Concepts.
- Apply Role-Playing Dimensions Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Model Optimization
Avoid unnecessary columns and poor relationships.
- Explain the core concepts and architecture of Model Optimization.
- Apply Model Optimization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Calculated Column vs Measure, Measures, SUM, COUNT, DISTINCTCOUNT, DIVIDE, IF, SWITCH, CALCULATE, Filter Context, Row Context, Context Transition Concepts.
Calculated Column vs Measure
Critical distinction.
- Explain the core concepts and architecture of Calculated Column vs Measure.
- Apply Calculated Column vs Measure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Measures
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Measures.
- Apply Measures in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SUM
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SUM.
- Apply SUM in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
COUNT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of COUNT.
- Apply COUNT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DISTINCTCOUNT
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of DISTINCTCOUNT.
- Apply DISTINCTCOUNT in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DIVIDE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of DIVIDE.
- Apply DIVIDE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
IF
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of IF.
- Apply IF in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SWITCH
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SWITCH.
- Apply SWITCH in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CALCULATE
Critical DAX Function.
- Explain the core concepts and architecture of CALCULATE.
- Apply CALCULATE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filter Context
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Filter Context.
- Apply Filter Context in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Row Context
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Row Context.
- Apply Row Context in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Context Transition Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Context Transition Concepts.
- Apply Context Transition Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
FILTER, ALL, REMOVEFILTERS, VALUES, SELECTEDVALUE, SUMX, AVERAGEX, RELATED, Time Intelligence, Growth Measures, Rolling Metrics.
FILTER
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of FILTER.
- Apply FILTER in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ALL
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of ALL.
- Apply ALL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
REMOVEFILTERS
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of REMOVEFILTERS.
- Apply REMOVEFILTERS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
VALUES
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of VALUES.
- Apply VALUES in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SELECTEDVALUE
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SELECTEDVALUE.
- Apply SELECTEDVALUE in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SUMX
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SUMX.
- Apply SUMX in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AVERAGEX
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of AVERAGEX.
- Apply AVERAGEX in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RELATED
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of RELATED.
- Apply RELATED in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Time Intelligence
MTD; QTD; YTD; previous month; previous year.
- Explain the core concepts and architecture of Time Intelligence.
- Apply Time Intelligence in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Growth Measures
MoM; QoQ; YoY.
- Explain the core concepts and architecture of Growth Measures.
- Apply Growth Measures in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Rolling Metrics
30-day revenue; 12-month moving sales.
- Explain the core concepts and architecture of Rolling Metrics.
- Apply Rolling Metrics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
KPI Cards, Bar Charts, Line Charts, Area Charts, Tables & Matrix, Scatter Plot, Maps Concepts, Decomposition Tree Concepts, Waterfall, Funnel, Slicers, Drill Down, Drill Through, Tooltips, Bookmarks, Buttons, Dynamic Titles.
KPI Cards
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of KPI Cards.
- Apply KPI Cards in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bar Charts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Bar Charts.
- Apply Bar Charts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Line Charts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Line Charts.
- Apply Line Charts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Area Charts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Area Charts.
- Apply Area Charts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tables & Matrix
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Tables & Matrix.
- Apply Tables & Matrix in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scatter Plot
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Scatter Plot.
- Apply Scatter Plot in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Maps Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Maps Concepts.
- Apply Maps Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Decomposition Tree Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Decomposition Tree Concepts.
- Apply Decomposition Tree Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Waterfall
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Waterfall.
- Apply Waterfall in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Funnel
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Funnel.
- Apply Funnel in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Slicers
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Slicers.
- Apply Slicers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Drill Down
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Drill Down.
- Apply Drill Down in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Drill Through
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Drill Through.
- Apply Drill Through in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tooltips
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Tooltips.
- Apply Tooltips in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bookmarks
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Bookmarks.
- Apply Bookmarks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Buttons
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Buttons.
- Apply Buttons in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dynamic Titles
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Dynamic Titles.
- Apply Dynamic Titles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Visual Hierarchy, Color Usage, Accessibility, Data-Ink Principles, Mobile Layout Concepts, Executive Report Design, Self-Service Analytics.
Visual Hierarchy
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Visual Hierarchy.
- Apply Visual Hierarchy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Color Usage
Use colors intentionally.
- Explain the core concepts and architecture of Color Usage.
- Apply Color Usage in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Accessibility
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Accessibility.
- Apply Accessibility in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data-Ink Principles
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data-Ink Principles.
- Apply Data-Ink Principles in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Mobile Layout Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Mobile Layout Concepts.
- Apply Mobile Layout Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Executive Report Design
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Executive Report Design.
- Apply Executive Report Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Self-Service Analytics
Design reports business users can explore independently.
- Explain the core concepts and architecture of Self-Service Analytics.
- Apply Self-Service Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Publishing, Workspaces, Semantic Models, Refresh, Scheduled Refresh, Gateways Concepts, Apps Concepts, Sharing & Collaboration, Permissions, Row-Level Security, Workspace Security, Deployment Concepts.
Publishing
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Publishing.
- Apply Publishing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Workspaces
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Workspaces.
- Apply Workspaces in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Semantic Models
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Semantic Models.
- Apply Semantic Models in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Refresh
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Refresh.
- Apply Refresh in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scheduled Refresh
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Scheduled Refresh.
- Apply Scheduled Refresh in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Gateways Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Gateways Concepts.
- Apply Gateways Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Apps Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Apps Concepts.
- Apply Apps Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sharing & Collaboration
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Sharing & Collaboration.
- Apply Sharing & Collaboration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Permissions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Permissions.
- Apply Permissions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Row-Level Security
Critical enterprise topic.
- Explain the core concepts and architecture of Row-Level Security.
- Apply Row-Level Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Workspace Security
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Workspace Security.
- Apply Workspace Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Deployment Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Deployment Concepts.
- Apply Deployment Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Model Size, Cardinality, Efficient DAX, Power Query Optimization, Query Folding Concepts, Performance Analyzer.
Model Size
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Model Size.
- Apply Model Size in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cardinality
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Cardinality.
- Apply Cardinality in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Efficient DAX
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Efficient DAX.
- Apply Efficient DAX in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Power Query Optimization
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Power Query Optimization.
- Apply Power Query Optimization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Query Folding Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Query Folding Concepts.
- Apply Query Folding Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Performance Analyzer
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Performance Analyzer.
- Apply Performance Analyzer in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Microsoft Fabric Fundamentals, OneLake Concepts, Lakehouse Awareness, Warehouse Awareness, Semantic Models, Fabric + Power BI Relationship, AI-Assisted Fabric Analytics Concepts.
Microsoft Fabric Fundamentals
Understand current Microsoft analytics ecosystem.
- Explain the core concepts and architecture of Microsoft Fabric Fundamentals.
- Apply Microsoft Fabric Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OneLake Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of OneLake Concepts.
- Apply OneLake Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lakehouse Awareness
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Lakehouse Awareness.
- Apply Lakehouse Awareness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Warehouse Awareness
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Warehouse Awareness.
- Apply Warehouse Awareness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Semantic Models
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Semantic Models.
- Apply Semantic Models in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Fabric + Power BI Relationship
Students should understand where Power BI fits in the broader Microsoft data platform.
- Explain the core concepts and architecture of Fabric + Power BI Relationship.
- Apply Fabric + Power BI Relationship in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted Fabric Analytics Concepts
Understand the increasing role of AI-assisted data work.
- Explain the core concepts and architecture of AI-Assisted Fabric Analytics Concepts.
- Apply AI-Assisted Fabric Analytics Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Mean, Median, Mode, Variance, Standard Deviation, Percentile, Distribution, Outliers, Correlation, Correlation vs Causation.
Mean
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Mean.
- Apply Mean in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Median
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Median.
- Apply Median in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Mode
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Mode.
- Apply Mode in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Variance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Variance.
- Apply Variance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Standard Deviation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Standard Deviation.
- Apply Standard Deviation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Percentile
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Percentile.
- Apply Percentile in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Distribution
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Distribution.
- Apply Distribution in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Outliers
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Outliers.
- Apply Outliers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Correlation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Correlation.
- Apply Correlation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Correlation vs Causation
Critical analyst concept.
- Explain the core concepts and architecture of Correlation vs Causation.
- Apply Correlation vs Causation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Probability Fundamentals, Conditional Probability, Normal Distribution Concepts, Sampling, Bias.
Probability Fundamentals
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Probability Fundamentals.
- Apply Probability Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Probability
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Conditional Probability.
- Apply Conditional Probability in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Normal Distribution Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Normal Distribution Concepts.
- Apply Normal Distribution Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sampling
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Sampling.
- Apply Sampling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bias
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Bias.
- Apply Bias in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Null Hypothesis, Alternative Hypothesis, P-Value Concepts, Confidence Intervals, T-Test Concepts, Chi-Square Concepts, Statistical vs Business Significance.
Null Hypothesis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Null Hypothesis.
- Apply Null Hypothesis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Alternative Hypothesis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Alternative Hypothesis.
- Apply Alternative Hypothesis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
P-Value Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of P-Value Concepts.
- Apply P-Value Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Confidence Intervals
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Confidence Intervals.
- Apply Confidence Intervals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
T-Test Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of T-Test Concepts.
- Apply T-Test Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Chi-Square Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Chi-Square Concepts.
- Apply Chi-Square Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Statistical vs Business Significance
Conversion improves; 10.00% to 10.05%; It may be statistically significant with a massive sample but commercially irrelevant.
- Explain the core concepts and architecture of Statistical vs Business Significance.
- Apply Statistical vs Business Significance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Control Group, Treatment Group, Conversion Rate, Lift, Significance, Experiment Interpretation.
Control Group
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Control Group.
- Apply Control Group in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Treatment Group
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Treatment Group.
- Apply Treatment Group in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conversion Rate
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Conversion Rate.
- Apply Conversion Rate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lift
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Lift.
- Apply Lift in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Significance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Significance.
- Apply Significance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Experiment Interpretation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Experiment Interpretation.
- Apply Experiment Interpretation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Why Python for Analysts?, Variables, Lists, Dictionaries, Conditions, Loops, Functions, Files.
Why Python for Analysts?
Use Python when; Data grows beyond spreadsheet comfort; repetitive tasks need automation; advanced analysis is required.
- Explain the core concepts and architecture of Why Python for Analysts?.
- Apply Why Python for Analysts? in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Variables
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Variables.
- Apply Variables in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Lists
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Lists.
- Apply Lists in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dictionaries
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Dictionaries.
- Apply Dictionaries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Conditions.
- Apply Conditions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Loops
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Loops.
- Apply Loops in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Functions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Functions.
- Apply Functions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Files
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Files.
- Apply Files in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Arrays, Mathematical Operations, Aggregations, Missing Values Concepts.
Arrays
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Arrays.
- Apply Arrays in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Mathematical Operations
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Mathematical Operations.
- Apply Mathematical Operations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Aggregations
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Aggregations.
- Apply Aggregations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Missing Values Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Missing Values Concepts.
- Apply Missing Values Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Series, DataFrames, Read CSV, Read Excel, Read SQL, Filtering, Sorting, Missing Values, Duplicates, GroupBy, Merge, Pivot, Date Handling, Apply.
Series
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Series.
- Apply Series in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DataFrames
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of DataFrames.
- Apply DataFrames in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Read CSV
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Read CSV.
- Apply Read CSV in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Read Excel
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Read Excel.
- Apply Read Excel in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Read SQL
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Read SQL.
- Apply Read SQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Filtering
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Filtering.
- Apply Filtering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sorting
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Sorting.
- Apply Sorting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Missing Values
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Missing Values.
- Apply Missing Values in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Duplicates
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Duplicates.
- Apply Duplicates in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
GroupBy
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of GroupBy.
- Apply GroupBy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Merge
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Merge.
- Apply Merge in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pivot
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Pivot.
- Apply Pivot in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Date Handling
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Date Handling.
- Apply Date Handling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Apply
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Apply.
- Apply Apply in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Matplotlib, Histograms, Bar Charts, Scatter Plots, Time-Series Charts, Choosing the Right Chart.
Matplotlib
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Matplotlib.
- Apply Matplotlib in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Histograms
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Histograms.
- Apply Histograms in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Bar Charts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Bar Charts.
- Apply Bar Charts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scatter Plots
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Scatter Plots.
- Apply Scatter Plots in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Time-Series Charts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Time-Series Charts.
- Apply Time-Series Charts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Choosing the Right Chart
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Choosing the Right Chart.
- Apply Choosing the Right Chart in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Revenue Analytics, Profit Analysis, Customer Analytics, Product Analytics, Channel Analytics, Regional Analytics.
Revenue Analytics
Total revenue; growth; category contribution.
- Explain the core concepts and architecture of Revenue Analytics.
- Apply Revenue Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Profit Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Profit Analysis.
- Apply Profit Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Analytics
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Customer Analytics.
- Apply Customer Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Product Analytics
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Product Analytics.
- Apply Product Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Channel Analytics
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Channel Analytics.
- Apply Channel Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Regional Analytics
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Regional Analytics.
- Apply Regional Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sales KPIs, Salesperson Performance, Territory Performance, Forecast vs Actual.
Sales KPIs
Revenue; target; attainment; growth; pipeline.
- Explain the core concepts and architecture of Sales KPIs.
- Apply Sales KPIs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Salesperson Performance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Salesperson Performance.
- Apply Salesperson Performance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Territory Performance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Territory Performance.
- Apply Territory Performance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Forecast vs Actual
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Forecast vs Actual.
- Apply Forecast vs Actual in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Impressions, Clicks, CTR, Conversion Rate, CPA, CAC, ROAS, Campaign Analysis.
Impressions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Impressions.
- Apply Impressions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Clicks
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Clicks.
- Apply Clicks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CTR
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of CTR.
- Apply CTR in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conversion Rate
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Conversion Rate.
- Apply Conversion Rate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CPA
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of CPA.
- Apply CPA in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CAC
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of CAC.
- Apply CAC in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ROAS
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of ROAS.
- Apply ROAS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Campaign Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Campaign Analysis.
- Apply Campaign Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Acquisition, Retention, Churn, Repeat Customers, Purchase Frequency, Average Order Value, Customer Lifetime Value Concepts, RFM Segmentation.
Customer Acquisition
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Customer Acquisition.
- Apply Customer Acquisition in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retention
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Retention.
- Apply Retention in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Churn
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Churn.
- Apply Churn in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Repeat Customers
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Repeat Customers.
- Apply Repeat Customers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Purchase Frequency
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Purchase Frequency.
- Apply Purchase Frequency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Average Order Value
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Average Order Value.
- Apply Average Order Value in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Lifetime Value Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Customer Lifetime Value Concepts.
- Apply Customer Lifetime Value Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RFM Segmentation
Recency; Frequency; Monetary.
- Explain the core concepts and architecture of RFM Segmentation.
- Apply RFM Segmentation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Active Users, DAU / WAU / MAU, Engagement, Funnel Analysis, Retention, Feature Usage, Drop-Off, Cohort Analysis.
Active Users
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Active Users.
- Apply Active Users in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
DAU / WAU / MAU
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of DAU / WAU / MAU.
- Apply DAU / WAU / MAU in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Engagement
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Engagement.
- Apply Engagement in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Funnel Analysis
Visitor; then; Registration; then; Course View; then; Payment; then; Enrollment.
- Explain the core concepts and architecture of Funnel Analysis.
- Apply Funnel Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retention
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Retention.
- Apply Retention in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Feature Usage
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Feature Usage.
- Apply Feature Usage in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Drop-Off
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Drop-Off.
- Apply Drop-Off in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cohort Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Cohort Analysis.
- Apply Cohort Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Revenue, Expenses, Gross Margin, Profit, Budget vs Actual, Variance Analysis.
Revenue
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Revenue.
- Apply Revenue in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Expenses
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Expenses.
- Apply Expenses in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Gross Margin
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Gross Margin.
- Apply Gross Margin in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Profit
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Profit.
- Apply Profit in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Budget vs Actual
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Budget vs Actual.
- Apply Budget vs Actual in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Variance Analysis
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Variance Analysis.
- Apply Variance Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SLA, Turnaround Time, Productivity, Failure Rate, Process Bottlenecks, Capacity.
SLA
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of SLA.
- Apply SLA in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Turnaround Time
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Turnaround Time.
- Apply Turnaround Time in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Productivity
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Productivity.
- Apply Productivity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Failure Rate
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Failure Rate.
- Apply Failure Rate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Process Bottlenecks
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Process Bottlenecks.
- Apply Process Bottlenecks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Capacity
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Capacity.
- Apply Capacity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Headcount, Attrition, Hiring Funnel, Attendance, Performance, Diversity Metrics Concepts.
Headcount
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Headcount.
- Apply Headcount in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Attrition
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Attrition.
- Apply Attrition in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Hiring Funnel
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Hiring Funnel.
- Apply Hiring Funnel in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Attendance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Attendance.
- Apply Attendance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Performance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Performance.
- Apply Performance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Diversity Metrics Concepts
Use responsibly and according to applicable policies.
- Explain the core concepts and architecture of Diversity Metrics Concepts.
- Apply Diversity Metrics Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Observation vs Insight, Story Structure, Executive Summaries, Presenting Recommendations, Handling Questions, Communicating Uncertainty.
Observation vs Insight
Observation; North region revenue fell 20%; Insight; North-region revenue fell primarily because enterprise customer renewals declined.
- Explain the core concepts and architecture of Observation vs Insight.
- Apply Observation vs Insight in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Story Structure
What happened?; Why?; Why does it matter?; What should we do?
- Explain the core concepts and architecture of Story Structure.
- Apply Story Structure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Executive Summaries
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Executive Summaries.
- Apply Executive Summaries in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Presenting Recommendations
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Presenting Recommendations.
- Apply Presenting Recommendations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Handling Questions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Handling Questions.
- Apply Handling Questions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Communicating Uncertainty
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Communicating Uncertainty.
- Apply Communicating Uncertainty in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Completeness, Accuracy, Validity, Consistency, Uniqueness, Timeliness, Data Quality Checks.
Completeness
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Completeness.
- Apply Completeness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Accuracy
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Accuracy.
- Apply Accuracy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Validity
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Validity.
- Apply Validity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Consistency
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Consistency.
- Apply Consistency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Uniqueness
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Uniqueness.
- Apply Uniqueness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Timeliness
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Timeliness.
- Apply Timeliness in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Quality Checks
Null rate; duplicate IDs; invalid dates; negative revenue; impossible ages.
- Explain the core concepts and architecture of Data Quality Checks.
- Apply Data Quality Checks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Metric Definitions, Single Source of Truth, Data Ownership, Documentation, Data Dictionary, Report Ownership.
Metric Definitions
What exactly is; Active Customer?; If teams define it differently, dashboards conflict.
- Explain the core concepts and architecture of Metric Definitions.
- Apply Metric Definitions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Single Source of Truth
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Single Source of Truth.
- Apply Single Source of Truth in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Ownership
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data Ownership.
- Apply Data Ownership in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Documentation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Documentation.
- Apply Documentation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Dictionary
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data Dictionary.
- Apply Data Dictionary in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Report Ownership
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Report Ownership.
- Apply Report Ownership in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sensitive Data, Least Privilege, Report Permissions, Row-Level Security, Data Export Risks, Secure Sharing.
Sensitive Data
PII; financial data; employee data.
- Explain the core concepts and architecture of Sensitive Data.
- Apply Sensitive Data in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Least Privilege
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Least Privilege.
- Apply Least Privilege in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Report Permissions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Report Permissions.
- Apply Report Permissions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Row-Level Security
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Row-Level Security.
- Apply Row-Level Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Export Risks
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Data Export Risks.
- Apply Data Export Risks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secure Sharing
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Secure Sharing.
- Apply Secure Sharing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Generative AI Fundamentals, AI-Assisted Excel, AI-Assisted SQL, AI-Assisted Power BI, Prompt Engineering for Analysts, AI-Assisted Data Cleaning, AI-Assisted Insight Generation, Natural Language Analytics, AI-Generated Summary Validation, AI Privacy.
Generative AI Fundamentals
LLMs; prompts; hallucinations; context.
- Explain the core concepts and architecture of Generative AI Fundamentals.
- Apply Generative AI Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted Excel
Use AI to help; Explain formulas; draft formulas; troubleshoot formulas; Always validate results.
- Explain the core concepts and architecture of AI-Assisted Excel.
- Apply AI-Assisted Excel in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted SQL
AI can assist with; query drafts; explanations; optimization suggestions; Analyst remains responsible for correctness.
- Explain the core concepts and architecture of AI-Assisted SQL.
- Apply AI-Assisted SQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted Power BI
Use AI-supported workflows where platform capabilities permit.
- Explain the core concepts and architecture of AI-Assisted Power BI.
- Apply AI-Assisted Power BI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Prompt Engineering for Analysts
Prompt structure; Business context; Data definition; Task; Constraints; Expected output.
- Explain the core concepts and architecture of Prompt Engineering for Analysts.
- Apply Prompt Engineering for Analysts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted Data Cleaning
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of AI-Assisted Data Cleaning.
- Apply AI-Assisted Data Cleaning in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Assisted Insight Generation
Use AI as; Assistant; not; Authority.
- Explain the core concepts and architecture of AI-Assisted Insight Generation.
- Apply AI-Assisted Insight Generation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Natural Language Analytics
Understand emerging natural-language BI workflows.
- Explain the core concepts and architecture of Natural Language Analytics.
- Apply Natural Language Analytics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI-Generated Summary Validation
Check; Numbers; comparisons; percentages; causality; assumptions.
- Explain the core concepts and architecture of AI-Generated Summary Validation.
- Apply AI-Generated Summary Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Privacy
Never upload confidential business datasets into unauthorized AI tools.
- Explain the core concepts and architecture of AI Privacy.
- Apply AI Privacy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Repetitive Reporting, Power Query Automation, Python Automation, Scheduled Refresh Concepts, Automated Reports, Alerting Concepts.
Repetitive Reporting
Identify manual tasks.
- Explain the core concepts and architecture of Repetitive Reporting.
- Apply Repetitive Reporting in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Power Query Automation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Power Query Automation.
- Apply Power Query Automation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Python Automation
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Python Automation.
- Apply Python Automation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scheduled Refresh Concepts
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Scheduled Refresh Concepts.
- Apply Scheduled Refresh Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Automated Reports
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Automated Reports.
- Apply Automated Reports in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Alerting Concepts
Notify when; refund_rate > threshold.
- Explain the core concepts and architecture of Alerting Concepts.
- Apply Alerting Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Stakeholder Interviews, Dashboard Requirements, Metric Definitions, Acceptance Criteria, Avoiding Scope Creep.
Stakeholder Interviews
Ask; What decision are you trying to make?; Which metric matters?; How often?; Who will use the report?
- Explain the core concepts and architecture of Stakeholder Interviews.
- Apply Stakeholder Interviews in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dashboard Requirements
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Dashboard Requirements.
- Apply Dashboard Requirements in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Metric Definitions
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Metric Definitions.
- Apply Metric Definitions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Acceptance Criteria
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Acceptance Criteria.
- Apply Acceptance Criteria in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Avoiding Scope Creep
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Avoiding Scope Creep.
- Apply Avoiding Scope Creep in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Requirement, Data Source, Cleaning, Modeling, Analysis, Dashboard, Validation, Stakeholder Review, Publication, Maintenance.
Requirement
Then.
- Explain the core concepts and architecture of Requirement.
- Apply Requirement in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Data Source
Then.
- Explain the core concepts and architecture of Data Source.
- Apply Data Source in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cleaning
Then.
- Explain the core concepts and architecture of Cleaning.
- Apply Cleaning in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modeling
Then.
- Explain the core concepts and architecture of Modeling.
- Apply Modeling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Analysis
Then.
- Explain the core concepts and architecture of Analysis.
- Apply Analysis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Dashboard
Then.
- Explain the core concepts and architecture of Dashboard.
- Apply Dashboard in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Validation
Then.
- Explain the core concepts and architecture of Validation.
- Apply Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Stakeholder Review
Then.
- Explain the core concepts and architecture of Stakeholder Review.
- Apply Stakeholder Review in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Publication
Then.
- Explain the core concepts and architecture of Publication.
- Apply Publication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Maintenance
Core concepts, practical analysis patterns, business interpretation and implementation considerations.
- Explain the core concepts and architecture of Maintenance.
- Apply Maintenance in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Professional Data Analyst Portfolio.
Professional Data Analyst Portfolio
Present every project with a business problem, governed data, cleaning, SQL, metrics, dashboard, findings, recommendations, screenshots, documentation and a portfolio link.
- Explain the core concepts and architecture of Professional Data Analyst Portfolio.
- Apply Professional Data Analyst Portfolio in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Enterprise Business Intelligence & Analytics Platform.
Enterprise Business Intelligence & Analytics Platform
Choose an e-commerce, finance, aviation, education, retail, healthcare, HR or sales domain and deliver business requirements, multiple tables, extraction and analytical SQL, Power Query/Python cleaning, fact-and-dimension modeling, at least 15 DAX business and time-intelligence measures, executive and drill-down dashboards, Row-Level Security where applicable, a validated AI-assisted task and a stakeholder presentation covering findings, impact and recommendations.
- Explain the core concepts and architecture of Enterprise Business Intelligence & Analytics Platform.
- Apply Enterprise Business Intelligence & Analytics Platform in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
26 Hands-On Production Capstones & Microservices
Build, deploy, and showcase real-world enterprise architectures on GitHub to prove your production engineering readiness:
Enterprise E-Commerce Microservices & Event-Driven Streaming Platform
Architected with Spring Boot 3.3, Apache Kafka event streams, Redis distributed caching, React 19 UI, and PostgreSQL. Features distributed ACID transaction sagas, payment webhook handling, dynamic inventory locking, and Dockerized Kubernetes deployment.
High-Throughput Banking & Core Transaction Engine
Concurrent multithreaded financial transaction ledger with ACID compliance, optimistic row locking, idempotent payment endpoints, and audit logging.
Real-Time Logistics & Fleet Tracking Service
Bi-directional live vehicle telemetry dashboard processing 10,000+ geo-coordinate events/sec with live map rendering and ETA calculations.
Multi-Tenant SaaS Subscription & Webhook Gateway
Multi-tenant automated billing engine with webhook signature verification, dynamic token bucket rate-limiting, and tenant data isolation schemas.
Distributed URL Shortener & Analytics System (Bitly Scale)
Low-latency URL redirection engine with distributed ID generation (Snowflake), sub-5ms Redis caching, and real-time click analytics.
Automated Cloud DevOps CI/CD Pipeline on AWS
Production containerization pipeline with automated testing, sonar code quality gates, container image vulnerability scanning, and zero-downtime rolling deploys.
AI-Powered Code Reviewer & Assessment Engine
Automated coding interview evaluator that parses Java AST trees, detects algorithmic time complexity, and simulates 1-on-1 voice technical interview feedback.
MockAttempt Academy vs. Traditional Bootcamps & Self-Study
Transparent side-by-side comparison of daily schedule, duration, curriculum, and placement support:
| Feature & Deliverables | MockAttempt Fast-Track Track | Expensive Bootcamps | Self-Study / YouTube |
|---|---|---|---|
| Live Weekend Schedule | Sat & Sun (4 Hours / Day) | 1 - 1.5 Hours / Day | Self-Paced / Inconsistent |
| Duration to Placement Readiness | 4 Months (16 Weeks Weekend) | 6 - 9 Months | 12+ Months (Uncertain) |
| Candidate Placement Guarantee | Unlimited Drives until Placed (or Full Refund) | Limited to 3-6 Months only | None (Apply blindly) |
| Topic Mock Tests & AI Interviews | Integrated for Every Topic (580+ Rounds) | End of Course Only | None |
| Tuition Fee | ₹49,999 ₹98,999 | ₹1,20,000 - ₹2,50,000 | Free (No Mentorship/Jobs) |
Institutional Course Assurances & 100% Placement Policy
100% Job Placement Guarantee
Our placement team arranges unlimited corporate interview drives across 1,050+ hiring partners until you receive an official offer letter. If unplaced, 100% of your tuition fee is refunded.
1-on-1 Expert Faculty Mentorship
Every cohort is taught live by seasoned lead architects from Tier-1 product companies with daily live coding and supervised code reviews.
Frequently Asked Questions
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