Analytics course

Lesson 1 • Analytical Thinking and Problem Framing

Trains structured decomposition of business problems into measurable questions. Directly enables effective project scoping in later applied chapters.

Lesson 2 • Data Sources and Acquisition Basics

Surveys primary, secondary, internal, and external data sources and acquisition methods. Prepares students to identify and obtain relevant data for any project.

Lesson 3 • Data Types and Structures

Covers structured, semi-structured, and unstructured data and their storage formats. Connects data type awareness to tool and method selection.

Lesson 4 • The Analytics Lifecycle

Maps the end-to-end process from problem definition to insight delivery. Provides a repeatable framework applied in every subsequent chapter.

Lesson 1 • Handling Missing and Erroneous Data

Covers detection and treatment strategies for nulls, outliers, and corrupt records. Ensures students can make defensible decisions about data imputation and removal.

Lesson 2 • Understanding Data Quality

Identifies the six dimensions of data quality and their impact on analytical outcomes. Sets the standard for what constitutes analysis-ready data.

Lesson 3 • Merging and Reshaping Datasets

Covers joins, unions, pivots, and melts to combine and restructure multiple tables. Enables students to build unified datasets from disparate sources.

Lesson 4 • Building Repeatable Data Pipelines

Introduces pipeline design principles for automating and documenting data preparation steps. Connects data engineering basics to scalable analytical workflows.

Lesson 1 • EDA Workflow and Documentation

Structures the EDA process into a reproducible, shareable workflow with clear findings. Prepares students to communicate exploratory results to stakeholders effectively.

Lesson 2 • Segmentation and Group Comparisons

Uses grouping and aggregation to compare subpopulations and detect meaningful differences. Directly supports business segmentation and A/B testing frameworks.

Lesson 3 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape for summarizing distributions. Provides the statistical language used in all subsequent analytical communication.

Lesson 4 • Correlation and Covariance

Quantifies linear and rank-based relationships between variables and interprets their strength. Establishes the foundation for regression and feature selection in later chapters.

Lesson 1 • Avoiding Misleading Visualizations

Identifies common visual distortions, truncated axes, and cherry-picked data presentations. Builds ethical and analytical credibility in all student outputs.

Lesson 2 • Narrative Structure for Data Stories

Applies storytelling frameworks to structure analytical findings into compelling narratives. Bridges technical analysis and executive communication throughout the course.

Lesson 3 • Chart Types and When to Use Them

Maps analytical questions to appropriate chart types across comparison, distribution, and flow. Prevents common mismatches between data and visual form.

Lesson 4 • Principles of Effective Data Visualization

Covers visual encoding, preattentive attributes, and the grammar of graphics. Establishes design standards applied to every chart and dashboard in the chapter.

Lesson 1 • Probability and Sampling Fundamentals

Covers probability rules, distributions, and sampling methods that underpin inference. Provides the theoretical grounding required for all hypothesis testing techniques.

Lesson 2 • Hypothesis Testing Framework

Establishes the null/alternative hypothesis structure, significance levels, and decision rules. Provides the universal testing framework applied in A/B testing and beyond.

Lesson 3 • Confidence Intervals and Estimation

Teaches point and interval estimation for population parameters from sample statistics. Connects estimation precision to sample size and business decision thresholds.

Lesson 4 • A/B Testing in Business Contexts

Designs controlled experiments for product, marketing, and operational decisions using inference. Integrates statistical rigor with practical experiment management.

Lesson 1 • Model Evaluation and Metrics

Teaches accuracy, precision, recall, F1, AUC-ROC, and RMSE for rigorous model assessment. Ensures students select metrics aligned with business costs of errors.

Lesson 2 • Classification Models

Applies logistic regression, decision trees, and ensemble methods to binary and multiclass problems. Prepares students to build classifiers for churn, fraud, and risk scenarios.

Lesson 3 • Feature Engineering and Selection

Covers feature creation, importance ranking, and dimensionality reduction to improve model performance. Bridges data preparation skills with predictive modeling outcomes.

Lesson 4 • Regression Models

Covers linear and polynomial regression for predicting continuous outcomes with interpretable coefficients. Connects regression outputs directly to business forecasting use cases.

Lesson 1 • Optimization and Simulation

Introduces linear programming, sensitivity analysis, and Monte Carlo simulation for decision support. Extends analytics from prediction to prescriptive action.

Lesson 2 • Model Deployment and Monitoring

Covers packaging models as APIs, batch scoring pipelines, and drift detection in production. Prepares students to sustain model value beyond initial development.

Lesson 3 • Time Series Analysis and Forecasting

Covers decomposition, stationarity, ARIMA, and exponential smoothing for temporal data. Directly supports demand forecasting, financial planning, and operational scheduling.

Lesson 4 • Recommendation and Association Systems

Builds collaborative filtering and market basket analysis models for personalization use cases. Connects unsupervised patterns to revenue-generating product recommendations.

Lesson 1 • Building an Analytics Team and Culture

Defines roles, skills, and organizational structures for high-performing analytics functions. Connects talent strategy to sustained analytical capability.

Lesson 2 • Aligning Analytics with Business Strategy

Maps analytical capabilities to strategic priorities and defines measurable value creation. Enables students to position analytics as a core organizational competency.

Lesson 3 • Measuring Analytics Program Effectiveness

Establishes KPIs, maturity assessments, and feedback loops for evaluating analytics investments. Closes the strategic loop by linking outputs to organizational outcomes.

Lesson 4 • Privacy, Ethics, and Responsible Analytics

Addresses consent, anonymization, fairness, and algorithmic bias in analytical systems. Builds the ethical judgment required for trustworthy data-driven organizations.