Digital Data Analyst Course

Lesson 1 • The Data Analyst Role

Defines analyst responsibilities, stakeholder relationships, and workflow stages. Anchors all subsequent technical skills in a professional context.

Lesson 2 • Types and Sources of Data

Covers structured, unstructured, and semi-structured data alongside primary and secondary sources. Enables students to identify appropriate data for any analytical task.

Lesson 3 • Analytical Thinking Frameworks

Introduces problem decomposition, hypothesis formation, and logical reasoning patterns. Provides a repeatable mental model for approaching any analytical challenge.

Lesson 1 • Core Formulas and Functions

Teaches lookup, logical, text, and date functions essential for data transformation. Directly enables the data cleaning and aggregation tasks covered later in the chapter.

Lesson 2 • Basic Statistical Functions

Applies descriptive statistics—mean, median, standard deviation, and percentiles—within spreadsheets. Prepares students for deeper statistical analysis in later chapters.

Lesson 3 • Pivot Tables and Aggregation

Demonstrates pivot table construction, grouping, and calculated fields for summarising data. Bridges raw data to the summary insights stakeholders need.

Lesson 4 • Spreadsheet Navigation and Setup

Covers workbook structure, cell referencing, and formatting conventions. Establishes efficient habits that reduce errors in all subsequent spreadsheet work.

Lesson 1 • Window Functions

Covers RANK, ROW_NUMBER, LAG, LEAD, and running totals using OVER clauses. Enables time-series and ranking analyses without collapsing row-level detail.

Lesson 2 • Aggregation and Grouping

Teaches GROUP BY, HAVING, and aggregate functions to summarise large datasets. Enables analysts to produce the summary metrics stakeholders request most often.

Lesson 3 • Relational Database Concepts

Explains tables, keys, relationships, and schemas as the structural foundation for SQL. Understanding this context prevents query errors and improves join logic.

Lesson 4 • Subqueries and CTEs

Introduces subqueries and Common Table Expressions for modular, readable query design. Prepares students for the complex analytical queries used in advanced reporting.

Lesson 5 • Core SELECT Queries

Covers SELECT, WHERE, ORDER BY, and LIMIT clauses for basic data retrieval. These are the building blocks for every more complex query in the chapter.

Lesson 1 • Hypothesis Testing

Teaches null and alternative hypotheses, p-values, significance levels, and Type I/II errors. Enables analysts to make statistically defensible claims from sample data.

Lesson 2 • Descriptive Statistics in Depth

Extends spreadsheet statistics to cover distributions, skewness, kurtosis, and robust measures. Provides the vocabulary needed to describe any dataset precisely.

Lesson 3 • Probability Fundamentals

Covers probability rules, conditional probability, and common distributions relevant to business data. Underpins all inferential methods introduced later in the chapter.

Lesson 4 • Correlation and Simple Regression

Covers Pearson correlation, simple linear regression, and residual interpretation. Introduces predictive modelling concepts that are expanded in the next chapter.

Lesson 1 • Python Fundamentals for Analysts

Covers variables, data types, control flow, and functions as the minimum Python needed for analysis. Keeps focus on analyst use cases rather than general software engineering.

Lesson 2 • Aggregation and GroupBy Operations

Applies groupby, pivot_table, and resample for summarising data across dimensions and time. Extends SQL aggregation concepts into a flexible programmatic workflow.

Lesson 3 • Data Manipulation with pandas

Teaches DataFrame creation, indexing, filtering, and merging using pandas. Mirrors the spreadsheet and SQL skills already learned in a programmatic environment.

Lesson 4 • Data Cleaning with Python

Addresses missing values, type conversion, string normalisation, and outlier detection in pandas. Produces reliable, analysis-ready data from messy real-world inputs.

Lesson 1 • Visualisation Design Principles

Covers pre-attentive attributes, chart selection logic, and common misleading chart patterns. Grounds all tool-specific work in universal design standards.

Lesson 2 • Charts and Graphs in Python

Builds static and interactive charts using matplotlib and seaborn. Connects Python data manipulation directly to visual output for end-to-end workflows.

Lesson 3 • Storytelling with Data

Teaches narrative structure, annotation, and sequencing visuals to guide audience interpretation. Transforms technically correct charts into persuasive analytical stories.

Lesson 4 • Dashboard Performance and Usability

Addresses filter design, load optimisation, and accessibility standards for production dashboards. Ensures dashboards remain usable as data volumes and user bases grow.

Lesson 1 • Multiple Linear Regression

Extends simple regression to multiple predictors, covering multicollinearity and model selection. Builds directly on the regression foundations from the statistics chapter.

Lesson 2 • Time-Series Analysis

Covers trend, seasonality, and decomposition for forecasting business metrics over time. Addresses the temporal data patterns common in sales, finance, and operations.

Lesson 3 • Segmentation and Clustering

Applies k-means and hierarchical clustering to group customers, products, or behaviours. Enables analysts to discover natural patterns without predefined labels.

Lesson 4 • Logistic Regression and Classification

Introduces logistic regression for binary outcomes and evaluates models with confusion matrices. Expands the analyst's toolkit to categorical prediction problems.

Lesson 1 • Measuring Analytical Impact

Introduces frameworks for tracking whether analytical recommendations were implemented and effective. Closes the feedback loop between analysis and business value.

Lesson 2 • Presenting to Stakeholders

Teaches slide design, verbal delivery, and handling questions during analytical presentations. Bridges the gap between rigorous analysis and confident communication.

Lesson 3 • Structuring Analytical Reports

Covers executive summary writing, finding hierarchy, and supporting evidence organisation. Ensures analytical work is accessible to non-technical decision-makers.