Introduction to Data Analysis Course

Lesson 1 • Types and Structures of Data

Covers quantitative, qualitative, structured, and unstructured data with real examples. Enables correct data-type identification before any collection or processing begins.

Lesson 2 • Analytical Thinking and Problem Framing

Develops structured reasoning skills for breaking complex problems into answerable questions. Directly supports hypothesis formation and metric selection in later chapters.

Lesson 3 • The Data Analysis Workflow

Introduces the end-to-end process from question formulation to insight communication. Provides a repeatable mental model students apply throughout the course.

Lesson 1 • Evaluating Data Quality

Introduces accuracy, completeness, consistency, and timeliness as quality dimensions. Students apply a quality checklist before committing data to analysis.

Lesson 2 • Sampling Theory and Bias

Covers probability and non-probability sampling strategies and their effect on representativeness. Equips students to design samples that minimise systematic error.

Lesson 3 • Secondary and Open Data Sources

Explores publicly available datasets, organisational records, and third-party databases as secondary sources. Builds source-evaluation skills critical for real-world analysis projects.

Lesson 1 • Building a Reproducible Cleaning Pipeline

Structures cleaning steps into a documented, repeatable workflow using scripts or tools. Ensures consistency when datasets are updated or shared with colleagues.

Lesson 2 • Data Transformation and Normalisation

Applies scaling, encoding, and binning to make variables comparable and model-ready. Builds on data-type knowledge from Chapter 1 to select appropriate transformations.

Lesson 3 • Detecting and Treating Outliers

Uses statistical and visual methods to identify outliers and decides whether to remove, cap, or retain them. Prevents extreme values from distorting downstream analysis.

Lesson 4 • Reshaping and Merging Datasets

Covers pivoting, melting, and joining multiple tables to create unified analytical datasets. Prepares students for multi-source analysis projects in later chapters.

Lesson 1 • Univariate Distribution Analysis

Examines single-variable distributions through histograms, density plots, and frequency tables. Reveals data shape and anomalies before multivariate relationships are explored.

Lesson 2 • Grouping and Segmentation Analysis

Uses groupby operations and pivot tables to compare distributions across categories. Enables segment-level insights that inform targeted recommendations.

Lesson 3 • Documenting EDA Findings

Structures exploratory findings into a concise summary that guides the analytical plan. Bridges the gap between raw exploration and formal analysis in subsequent chapters.

Lesson 4 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape to summarise variable distributions. Provides the numerical foundation for all visual and inferential work that follows.

Lesson 1 • Confidence Intervals and Estimation

Constructs confidence intervals for means and proportions and interprets their practical meaning. Connects sample statistics from EDA to population-level claims.

Lesson 2 • Interpreting and Reporting Results

Translates statistical output into plain-language conclusions with appropriate caveats. Prepares students to communicate inferential findings to non-technical audiences.

Lesson 3 • Hypothesis Testing Framework

Establishes null and alternative hypotheses, significance levels, and decision rules for statistical tests. Gives students a universal framework applicable to all test types covered next.

Lesson 4 • Probability Concepts for Analysts

Covers probability rules, conditional probability, and common distributions relevant to data analysis. Provides the theoretical grounding required for all inferential techniques in this chapter.

Lesson 1 • Dashboard Design Fundamentals

Introduces layout principles, KPI selection, and interactivity concepts for operational dashboards. Prepares students to build monitoring tools covered in advanced chapters.

Lesson 2 • Principles of Effective Visualisation

Covers visual encoding, pre-attentive attributes, and the data-ink ratio as design foundations. Establishes standards that govern every chart created in this chapter.

Lesson 3 • Chart Types and Their Applications

Maps analytical goals—comparison, distribution, relationship, composition—to appropriate chart types. Prevents common mismatches between data and visual form.

Lesson 4 • Building a Data Narrative

Structures insights into a beginning-middle-end story arc that drives audience action. Connects visualisation skills to the communication goals established in Chapter 1.

Lesson 1 • Introduction to Predictive Modelling

Distinguishes descriptive, predictive, and prescriptive analytics and frames the modelling workflow. Positions predictive analysis as the next step beyond the inferential work in Chapter 5.

Lesson 2 • Model Evaluation and Validation

Uses accuracy, precision, recall, RMSE, and cross-validation to assess model performance objectively. Ensures students can compare models and select the best performer.

Lesson 3 • Communicating Model Results

Translates model outputs into business-relevant insights using plain language and supporting visuals. Applies storytelling skills from Chapter 6 to predictive findings.

Lesson 4 • Linear Regression for Continuous Outcomes

Builds simple and multiple linear regression models and interprets coefficients and fit metrics. Leverages correlation knowledge from EDA to select meaningful predictors.

Lesson 1 • Analytical Report Writing

Structures findings into a professional report with executive summary, methodology, results, and recommendations. Develops written communication skills essential for analyst roles.

Lesson 2 • Scoping and Planning an Analysis Project

Defines project objectives, deliverables, timelines, and stakeholder requirements before analysis begins. Applies the analytical thinking framework from Chapter 1 at project scale.

Lesson 3 • Presenting Findings to Stakeholders

Prepares and delivers a stakeholder presentation using slides, visuals, and a clear narrative arc. Combines visualisation and storytelling skills from Chapter 6 in a live context.

Lesson 4 • End-to-End Workflow Execution

Guides students through a full pipeline from raw data to cleaned, analysed, and visualised output. Reinforces the sequential skills built across Chapters 2 through 6.