Data Scientist Master Program

Translate business concerns into measurable data problems, targets, KPIs, hypotheses, segments and analysis plans while avoiding technically correct solutions to the wrong problem. Focus module: Converting Business Problems into Data Problems.

Translate business concerns into measurable data problems, targets, KPIs, hypotheses, segments and analysis plans while avoiding technically correct solutions to the wrong problem. Focus module: Defining Success Metrics.

Translate business concerns into measurable data problems, targets, KPIs, hypotheses, segments and analysis plans while avoiding technically correct solutions to the wrong problem. Focus module: Analytical Questions.

Translate business concerns into measurable data problems, targets, KPIs, hypotheses, segments and analysis plans while avoiding technically correct solutions to the wrong problem. Focus module: Avoiding Wrong Problems.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Python Basics.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Conditions & Loops.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Data Structures.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Functions.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: File Handling.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Error Handling.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: OOP Essentials.

Python syntax, control flow, collections, functions, files, validation, object-oriented essentials, environments, Jupyter and VS Code. Focus module: Python Environments.

Array creation, dimensions, indexing, slicing, vectorization, broadcasting, aggregation, statistical operations and matrix concepts for machine learning. Focus module: NumPy Arrays.

Array creation, dimensions, indexing, slicing, vectorization, broadcasting, aggregation, statistical operations and matrix concepts for machine learning. Focus module: Vectorized Operations.

Array creation, dimensions, indexing, slicing, vectorization, broadcasting, aggregation, statistical operations and matrix concepts for machine learning. Focus module: Statistical Operations.

Array creation, dimensions, indexing, slicing, vectorization, broadcasting, aggregation, statistical operations and matrix concepts for machine learning. Focus module: Matrix Concepts.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Series & DataFrames.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Loading Data.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Selecting Data.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Cleaning Data.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Transformation.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: GroupBy.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Merge & Join.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Pivot Tables.

Series, DataFrames, CSV/Excel/JSON/SQL loading, selection, cleaning, transformation, grouping, joins, pivots and date/time analysis. Focus module: Date/Time Analysis.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: SQL Fundamentals.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: Aggregations.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: GROUP BY.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: HAVING.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: Joins.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: CASE.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: Subqueries.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: CTE.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: Window Functions.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: Date Functions.

Analytical SQL from filtering and aggregations through joins, CASE, subqueries, CTEs, windows, dates, cohorts, funnels, retention and rolling metrics. Focus module: SQL Analytics Patterns.

Collect appropriately scoped data from databases, files, APIs and analytics systems using reusable extraction and sampling approaches. Focus module: Sources of Data.

Collect appropriately scoped data from databases, files, APIs and analytics systems using reusable extraction and sampling approaches. Focus module: APIs.

Collect appropriately scoped data from databases, files, APIs and analytics systems using reusable extraction and sampling approaches. Focus module: Data Extraction.

Collect appropriately scoped data from databases, files, APIs and analytics systems using reusable extraction and sampling approaches. Focus module: Data Sampling.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Missing Data.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Duplicate Records.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Outliers.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Inconsistent Categories.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Schema Validation.

Diagnose missing values, duplicates, outliers, inconsistent categories and schema problems and produce automated quality reports. Focus module: Data Quality Report.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: EDA Framework.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Univariate Analysis.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Bivariate Analysis.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Multivariate Analysis.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Segmentation.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Outlier Analysis.

Use a repeatable EDA framework for structure, missingness, distributions, relationships, anomalies, segmentation, outliers and correlation limitations. Focus module: Correlation Analysis.

Select suitable charts, create Matplotlib visualizations, show multivariate patterns and avoid misleading scales, truncation, cherry-picking and clutter. Focus module: Visualization Principles.

Select suitable charts, create Matplotlib visualizations, show multivariate patterns and avoid misleading scales, truncation, cherry-picking and clutter. Focus module: Matplotlib.

Select suitable charts, create Matplotlib visualizations, show multivariate patterns and avoid misleading scales, truncation, cherry-picking and clutter. Focus module: Advanced Visualization.

Select suitable charts, create Matplotlib visualizations, show multivariate patterns and avoid misleading scales, truncation, cherry-picking and clutter. Focus module: Avoiding Misleading Charts.

Turn observations into business insights using situation, finding, cause, impact and recommendation, then communicate clearly to executives and stakeholders. Focus module: Insight vs Observation.

Turn observations into business insights using situation, finding, cause, impact and recommendation, then communicate clearly to executives and stakeholders. Focus module: Business Story Structure.

Turn observations into business insights using situation, finding, cause, impact and recommendation, then communicate clearly to executives and stakeholders. Focus module: Executive Communication.

Turn observations into business insights using situation, finding, cause, impact and recommendation, then communicate clearly to executives and stakeholders. Focus module: Data Presentation.

Events, sample spaces, conditional probability, Bayes theorem, random variables and normal, Bernoulli, binomial and Poisson distributions. Focus module: Probability Fundamentals.

Events, sample spaces, conditional probability, Bayes theorem, random variables and normal, Bernoulli, binomial and Poisson distributions. Focus module: Conditional Probability.

Events, sample spaces, conditional probability, Bayes theorem, random variables and normal, Bernoulli, binomial and Poisson distributions. Focus module: Bayes Theorem.

Events, sample spaces, conditional probability, Bayes theorem, random variables and normal, Bernoulli, binomial and Poisson distributions. Focus module: Random Variables.

Events, sample spaces, conditional probability, Bayes theorem, random variables and normal, Bernoulli, binomial and Poisson distributions. Focus module: Probability Distributions.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Descriptive Statistics.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Percentiles.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Sampling.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Central Limit Theorem.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Confidence Intervals.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Correlation.

Descriptive measures, percentiles, populations and samples, sampling bias, the central limit theorem, confidence intervals, correlation and covariance. Focus module: Covariance.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: Hypothesis Framework.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: Statistical Significance.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: P-Values.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: Type I Error.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: Type II Error.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: T-Test.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: Chi-Square Test.

Null and alternative hypotheses, statistical significance, p-values, Type I/II errors, t-tests, chi-square tests and ANOVA concepts with correct interpretation. Focus module: ANOVA Concepts.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Why Experiments?.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Control & Treatment.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Randomization.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Experiment Metrics.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Sample Size Concepts.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Statistical Power Concepts.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Experiment Duration.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Multiple Testing Problem.

Design and analyze randomized experiments with control/treatment groups, primary and guardrail metrics, sample size, power, duration, multiple testing, lift and business impact. Focus module: Experiment Analysis.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Correlation vs Causation.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Confounding.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Selection Bias.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Simpson's Paradox.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Observational Studies.

Separate correlation from causation and reason about confounding, selection bias, Simpson's paradox, observational limitations and introductory causal-inference concepts. Focus module: Causal Inference Concepts.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: What Is Machine Learning?.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Supervised Learning.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Unsupervised Learning.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Features & Labels.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Training vs Inference.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Train/Validation/Test.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Cross Validation.

Supervised and unsupervised learning, features, labels, training, inference, train/validation/test splits, cross-validation and data leakage. Focus module: Data Leakage.

Linear regression, assumptions, Ridge/Lasso regularization and MAE, MSE, RMSE and R-squared evaluation. Focus module: Linear Regression.

Linear regression, assumptions, Ridge/Lasso regularization and MAE, MSE, RMSE and R-squared evaluation. Focus module: Regression Assumptions.

Linear regression, assumptions, Ridge/Lasso regularization and MAE, MSE, RMSE and R-squared evaluation. Focus module: Regularization.

Linear regression, assumptions, Ridge/Lasso regularization and MAE, MSE, RMSE and R-squared evaluation. Focus module: Regression Metrics.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Logistic Regression.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Decision Trees.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Random Forest.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: KNN.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: SVM Concepts.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Naive Bayes.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Classification Metrics.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Confusion Matrix.

Logistic regression, trees, random forests, KNN, SVM and Naive Bayes plus accuracy, precision, recall, F1, ROC/PR concepts, confusion matrices and threshold optimization. Focus module: Threshold Optimization.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: Bagging.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: Boosting.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: Random Forest Deep Dive.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: Gradient Boosting.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: XGBoost.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: LightGBM Concepts.

Bagging, boosting, random-forest depth, gradient boosting, hands-on XGBoost and LightGBM/CatBoost concepts. Focus module: CatBoost Concepts.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Missing Value Features.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Categorical Encoding.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Scaling.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Transformations.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Date Features.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Interaction Features.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Aggregated Features.

Create missingness, categorical, scaled, transformed, date, interaction, aggregate and domain-driven features grounded in business understanding. Focus module: Domain Features.

Use correlation, feature importance, recursive elimination and regularization to select useful features responsibly. Focus module: Correlation-Based Selection.

Use correlation, feature importance, recursive elimination and regularization to select useful features responsibly. Focus module: Feature Importance.

Use correlation, feature importance, recursive elimination and regularization to select useful features responsibly. Focus module: Recursive Feature Elimination Concepts.

Use correlation, feature importance, recursive elimination and regularization to select useful features responsibly. Focus module: Regularization-Based Selection.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Underfitting.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Overfitting.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Bias-Variance Tradeoff.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Grid Search.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Random Search.

Diagnose underfitting and overfitting, reason about bias and variance, and tune with grid search, random search and cross-validation. Focus module: Cross-Validation Tuning.

Apply K-Means, choose K, understand hierarchical clustering and DBSCAN, and translate technical clusters into business segments. Focus module: K-Means.

Apply K-Means, choose K, understand hierarchical clustering and DBSCAN, and translate technical clusters into business segments. Focus module: Choosing K.

Apply K-Means, choose K, understand hierarchical clustering and DBSCAN, and translate technical clusters into business segments. Focus module: Hierarchical Clustering.

Apply K-Means, choose K, understand hierarchical clustering and DBSCAN, and translate technical clusters into business segments. Focus module: DBSCAN.

Apply K-Means, choose K, understand hierarchical clustering and DBSCAN, and translate technical clusters into business segments. Focus module: Cluster Interpretation.

Understand high-dimensional challenges, apply PCA and visualize compressed representations. Focus module: Curse of Dimensionality.

Understand high-dimensional challenges, apply PCA and visualize compressed representations. Focus module: PCA.

Understand high-dimensional challenges, apply PCA and visualize compressed representations. Focus module: Visualizing High-Dimensional Data.

Distinguish outliers from operational anomalies, use Isolation Forest and connect anomaly detection to fraud, manufacturing, security and operations. Focus module: Outliers vs Anomalies.

Distinguish outliers from operational anomalies, use Isolation Forest and connect anomaly detection to fraud, manufacturing, security and operations. Focus module: Isolation Forest.

Distinguish outliers from operational anomalies, use Isolation Forest and connect anomaly detection to fraud, manufacturing, security and operations. Focus module: Business Applications.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Time-Series Components.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Date Index.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Lag Features.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Rolling Features.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Time-Series Validation.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Forecasting Fundamentals.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: Classical Forecasting Concepts.

Model trend, seasonality and noise using date indexes, lag and rolling features, temporal validation, classical concepts and supervised-ML forecasting. Focus module: ML Forecasting.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Recommendation Basics.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Popularity-Based Recommendations.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Content-Based Filtering.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Collaborative Filtering Concepts.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Cold Start.

Build popularity, content and collaborative recommendation approaches, handle cold start and evaluate recommendation quality. Focus module: Recommendation Evaluation.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Text Data.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Tokenization.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Cleaning Text.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Bag of Words.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: TF-IDF.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Sentiment Analysis.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Text Classification.

Prepare and model text using tokenization, cleaning, bag-of-words, TF-IDF, sentiment, classification and exploratory topic concepts. Focus module: Topic Exploration Concepts.

Use semantic embeddings, sentence embeddings, transformer and Hugging Face concepts in a modern text-to-prediction-to-business-insight workflow. Focus module: Embeddings.

Use semantic embeddings, sentence embeddings, transformer and Hugging Face concepts in a modern text-to-prediction-to-business-insight workflow. Focus module: Sentence Embeddings.

Use semantic embeddings, sentence embeddings, transformer and Hugging Face concepts in a modern text-to-prediction-to-business-insight workflow. Focus module: Transformer Fundamentals.

Use semantic embeddings, sentence embeddings, transformer and Hugging Face concepts in a modern text-to-prediction-to-business-insight workflow. Focus module: Hugging Face Concepts.

Use semantic embeddings, sentence embeddings, transformer and Hugging Face concepts in a modern text-to-prediction-to-business-insight workflow. Focus module: Modern NLP Workflow.

Use LLMs carefully for analysis, prompts, structured extraction, validated natural-language-to-SQL, RAG fundamentals and AI-assisted research without replacing statistical rigor. Focus module: Generative AI Fundamentals.