Data Analysis Course

Lesson 1 • Analytical Thinking and Problem Framing

Develops structured thinking skills: decomposing problems, forming hypotheses, and avoiding cognitive bias. These habits underpin every analytical decision made later.

Lesson 2 • The Data Analysis Workflow

Introduces the end-to-end process: define, collect, clean, analyze, visualize, and communicate. Provides a repeatable framework students apply in every subsequent chapter.

Lesson 3 • What Data Analysis Is

Defines data analysis, its business value, and how it differs from data science. Anchors the chapter by establishing shared vocabulary for all subsequent topics.

Lesson 4 • Setting Up the Analytical Environment

Guides students through installing and configuring tools used throughout the course. A functional workspace is required before hands-on exercises begin.

Lesson 1 • Primary vs. Secondary Data Sources

Distinguishes internally generated data from externally sourced data and explains trade-offs in cost, timeliness, and control. Sets the stage for source selection decisions.

Lesson 2 • Databases and Query Basics

Introduces relational database concepts and basic SQL for extracting data. Querying skills are essential for accessing structured organizational data.

Lesson 3 • Data Quality Assessment

Teaches dimensions of data quality: accuracy, completeness, consistency, and timeliness. Analysts must evaluate fitness for purpose before investing in analysis.

Lesson 4 • Ethical and Privacy Considerations

Addresses consent, anonymization, and responsible data handling aligned with global privacy principles. Ethical collection practices protect individuals and organizations.

Lesson 1 • Merging and Reshaping Datasets

Teaches joins, concatenation, pivoting, and melting to combine and restructure data. Analysts routinely integrate multiple sources into a single analytical table.

Lesson 2 • Identifying and Handling Missing Data

Explains missing-at-random vs. not-at-random patterns and applies deletion, imputation, and flagging strategies. Proper handling prevents biased downstream results.

Lesson 3 • Building a Reproducible Cleaning Pipeline

Structures cleaning steps into reusable scripts and documents each transformation decision. Reproducibility ensures that cleaning can be audited and repeated on new data.

Lesson 4 • Detecting and Treating Outliers

Uses statistical and visual methods to identify outliers and decides whether to remove, cap, or retain them. Outlier decisions directly affect summary statistics and models.

Lesson 1 • Documenting EDA Findings

Structures EDA outputs into a narrative with annotated charts and key observations. Documented findings serve as the foundation for formal analysis and reporting.

Lesson 2 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape for both numeric and categorical variables. These statistics form the baseline summary every analysis begins with.

Lesson 3 • Grouping and Segmentation Analysis

Uses groupby operations and pivot tables to compare statistics across segments. Segmentation reveals subgroup differences invisible in aggregate summaries.

Lesson 4 • Univariate Distribution Analysis

Examines single-variable distributions using histograms, density plots, and Q-Q plots. Understanding individual variable behavior precedes multivariate exploration.

Lesson 1 • Building Charts with Python Libraries

Implements visualizations using Matplotlib and Seaborn with full control over styling. Programmatic charts are reproducible and easily updated with new data.

Lesson 2 • Chart Types and When to Use Them

Maps analytical goals (comparison, distribution, relationship, composition) to appropriate chart types. Correct chart selection is the first step in clear communication.

Lesson 3 • Interactive and Dashboard Visualizations

Introduces Plotly and basic dashboard frameworks for interactive exploration. Interactivity allows stakeholders to self-serve insights without analyst intervention.

Lesson 4 • Principles of Effective Visualization

Covers Tufte's data-ink ratio, pre-attentive attributes, and chart selection frameworks. Principled design prevents misleading or cluttered visuals.

Lesson 1 • Hypothesis Testing Framework

Introduces null and alternative hypotheses, p-values, significance levels, and Type I/II errors. This framework is applied in every test covered in subsequent sections.

Lesson 2 • Sampling and Estimation

Explains sampling methods, sample size determination, and point vs. interval estimation. Proper sampling ensures results generalize to the target population.

Lesson 3 • Correlation and Simple Regression

Quantifies linear relationships and builds simple regression models for prediction and explanation. Regression extends correlation into actionable predictive statements.

Lesson 4 • Probability Fundamentals for Analysts

Covers probability rules, conditional probability, and common distributions relevant to business data. Probability underpins every inferential technique introduced later.

Lesson 1 • Time-Series Analysis

Decomposes time-series data into trend, seasonality, and residual components and applies forecasting models. Temporal patterns are critical in operational and financial analysis.

Lesson 2 • Segmentation and Clustering

Applies k-means and hierarchical clustering to segment customers, products, or behaviors. Segmentation enables targeted strategies based on data-driven groupings.

Lesson 3 • Multiple Regression Analysis

Builds and interprets multiple linear regression models with diagnostics and variable selection. Extends simple regression to handle multiple predictors simultaneously.

Lesson 4 • Feature Engineering and Selection

Creates and selects informative features to improve model performance. Well-engineered features often matter more than algorithm choice in practical applications.

Lesson 1 • Measuring Analytical Impact

Establishes metrics to track whether recommendations were implemented and generated expected outcomes. Closing the feedback loop builds analyst credibility and improves future work.

Lesson 2 • Building Executive-Ready Presentations

Designs slide decks with clear titles, minimal text, and supporting visuals aligned to the narrative. Executive audiences require concise, decision-focused communication.

Lesson 3 • Translating Analysis into Recommendations

Converts statistical findings into specific, prioritized business recommendations with expected impact. Recommendations bridge the gap between analysis and organizational action.

Lesson 4 • Handling Stakeholder Questions

Prepares analysts to defend methodology, address skepticism, and clarify assumptions under pressure. Credibility depends on confident, transparent responses to scrutiny.