Data Scientist: Free Skill Evaluation

What Is Data Science? Data Science Lifecycle Types of Analytics Data Scientist Responsibilities Converting Business Problems into Data Problems Defining Success Metrics Analytical Questions Avoiding Wrong Problems Python Basics Conditions & Loops Data Structures Functions File Handling Error Handling OOP Essentials Python Environments NumPy Arrays Vectorized Operations Statistical Operations Matrix Concepts Series & DataFrames Loading Data Selecting Data Cleaning Data Transformation GroupBy Merge & Join Pivot Tables Date/Time Analysis SQL Fundamentals Aggregations GROUP BY HAVING Joins CASE Subqueries CTE Window Functions Date Functions SQL Analytics Patterns Sources of Data APIs Data Extraction Data Sampling Missing Data Duplicate Records Outliers Inconsistent Categories Schema Validation Data Quality Report EDA Framework Univariate Analysis Bivariate Analysis Multivariate Analysis Segmentation Outlier Analysis Correlation Analysis Visualization Principles Matplotlib Advanced Visualization Avoiding Misleading Charts Insight vs Observation Business Story Structure Executive Communication Data Presentation Probability Fundamentals Conditional Probability Bayes Theorem Random Variables Probability Distributions Descriptive Statistics Percentiles Sampling Central Limit Theorem Confidence Intervals Correlation Covariance Hypothesis Framework Statistical Significance P-Values Type I Error Type II Error T-Test Chi-Square Test ANOVA Concepts Why Experiments? Control & Treatment Randomization Experiment Metrics Sample Size Concepts Statistical Power Concepts Experiment Duration Multiple Testing Problem Experiment Analysis Correlation vs Causation Confounding Selection Bias Simpson's Paradox Observational Studies Causal Inference Concepts What Is Machine Learning? Supervised Learning Unsupervised Learning Features & Labels Training vs Inference Train/Validation/Test Cross Validation Data Leakage Linear Regression Regression Assumptions Regularization Regression Metrics Logistic Regression Decision Trees Random Forest KNN SVM Concepts Naive Bayes Classification Metrics Confusion Matrix Threshold Optimization Bagging Boosting Random Forest Deep Dive Gradient Boosting XGBoost LightGBM Concepts CatBoost Concepts Missing Value Features Categorical Encoding Scaling Transformations Date Features Interaction Features Aggregated Features Domain Features Correlation-Based Selection Feature Importance Recursive Feature Elimination Concepts Regularization-Based Selection Underfitting Overfitting Bias-Variance Tradeoff Grid Search Random Search Cross-Validation Tuning K-Means Choosing K Hierarchical Clustering DBSCAN Cluster Interpretation Curse of Dimensionality PCA Visualizing High-Dimensional Data Outliers vs Anomalies Isolation Forest Business Applications Time-Series Components Date Index Lag Features Rolling Features Time-Series Validation Forecasting Fundamentals Classical Forecasting Concepts ML Forecasting Recommendation Basics Popularity-Based Recommendations Content-Based Filtering Collaborative Filtering Concepts Cold Start Recommendation Evaluation Text Data Tokenization Cleaning Text Bag of Words TF-IDF Sentiment Analysis Text Classification Topic Exploration Concepts Embeddings Sentence Embeddings Transformer Fundamentals Hugging Face Concepts Modern NLP Workflow Generative AI Fundamentals LLMs Prompt Engineering for Data Analysis Structured Extraction Natural Language to SQL Concepts RAG Fundamentals AI-Assisted Research & Analytics Product Metrics Funnels Retention Cohort Analysis Customer Lifetime Value Concepts North-Star Metrics Acquisition Metrics CAC Conversion Attribution Concepts Campaign Evaluation Customer Segmentation Risk Modeling Default Probability Fraud Analytics Imbalanced Datasets Cost-Sensitive Evaluation Forecasting Demand Capacity Analytics Operational KPIs Anomaly Detection Optimization Concepts Why Explainability Matters Partial Dependence Concepts SHAP Concepts Explaining Individual Predictions Data Ethics Bias Fairness Privacy Sensitive Features Responsible Experimentation Human Review Notebook vs Production Model Serialization FastAPI Request Validation Response Design Docker Containerizing a Data Science Model Environment Variables AWS Concepts Deploying Prediction Services Data Storage Cost Awareness Experiment Tracking Model Versions Model Registry Concepts Model Monitoring Data Drift Retraining Concepts Designing a Data Science Solution Design Churn Prediction Design Recommendation System Design Fraud Detection Design Customer Segmentation Design Demand Forecasting Asking Business Questions Requirements Gathering Explaining Uncertainty Communicating Model Limitations Presenting Recommendations Professional Data Science Portfolio Enterprise Data Science Decision Platform