Data Analyst Training Course

Lesson 1 • Data Ethics and Governance Basics

Introduces privacy principles, data ownership, and responsible use standards. Grounds students in ethical obligations before they handle real datasets.

Lesson 2 • Data Types and Structures

Covers structured, semi-structured, and unstructured data formats and their storage models. Provides the vocabulary needed to work with any dataset encountered in practice.

Lesson 3 • The Analytics Project Lifecycle

Maps the end-to-end process from business question to insight delivery. Students apply the lifecycle framework to a sample business scenario.

Lesson 1 • PivotTables and Summary Analysis

Demonstrates how to aggregate and slice data dynamically using PivotTables. Students summarise a business dataset and draw initial conclusions from pivot outputs.

Lesson 2 • Data Cleaning in Spreadsheets

Addresses duplicate removal, blank handling, and format standardisation within spreadsheets. Directly prepares students for the data quality challenges in later chapters.

Lesson 3 • Core Formulas and Functions

Teaches arithmetic, logical, lookup, and text functions used daily by analysts. Students build a reusable formula library applicable to real business datasets.

Lesson 4 • Spreadsheet Interface and Navigation

Orients students to the spreadsheet environment, keyboard shortcuts, and workbook organisation. Efficiency habits established here accelerate all later spreadsheet work.

Lesson 1 • Relational Database Fundamentals

Explains tables, keys, relationships, and schema design principles. Provides the structural knowledge required to write accurate JOIN queries later.

Lesson 2 • Joining Multiple Tables

Explains INNER, LEFT, RIGHT, and FULL joins with practical multi-table scenarios. Students construct queries that combine data across three or more related tables.

Lesson 3 • Basic SELECT Queries

Teaches SELECT, WHERE, ORDER BY, and LIMIT clauses for targeted data retrieval. Students query a sample database to answer specific business questions.

Lesson 4 • Subqueries and Common Table Expressions

Introduces subqueries, CTEs, and window functions for advanced data shaping. Students refactor complex nested queries into readable, maintainable CTE structures.

Lesson 1 • Standardising and Transforming Data

Covers format normalisation, type casting, and feature engineering for analysis readiness. Students transform raw fields into consistent, correctly typed analysis variables.

Lesson 2 • Documenting the Cleaning Process

Establishes practices for logging transformations, versioning datasets, and writing data dictionaries. Reproducible documentation ensures cleaning steps can be audited and repeated.

Lesson 3 • Deduplication and Record Linkage

Teaches exact and fuzzy matching methods to identify and merge duplicate records. Students deduplicate a customer dataset using both rule-based and similarity approaches.

Lesson 4 • Assessing Data Quality

Defines completeness, consistency, accuracy, and timeliness as quality dimensions. Students profile a raw dataset and document all identified quality issues.

Lesson 1 • Univariate Distribution Analysis

Examines single-variable distributions using histograms, box plots, and density curves. Students identify distributional shape, outliers, and data concentration zones.

Lesson 2 • Categorical Data Exploration

Applies bar charts, count plots, and chi-square tests to categorical variable analysis. Students assess category distributions and test independence between categorical fields.

Lesson 3 • Bivariate and Multivariate Analysis

Explores relationships between two or more variables using scatter plots and correlation. Students quantify and visualise associations across multiple dataset dimensions.

Lesson 4 • Descriptive Statistics Essentials

Covers measures of central tendency, spread, and shape for summarising distributions. Students compute and interpret descriptive stats for continuous and categorical variables.

Lesson 1 • Presenting Findings Effectively

Prepares students to deliver live data presentations with confidence and clarity. Students present their dashboard story and receive structured peer feedback.

Lesson 2 • Visualisation Design Principles

Establishes rules for chart selection, colour use, and cognitive load reduction. Correct design choices prevent misinterpretation and increase stakeholder trust in findings.

Lesson 3 • Building Charts in Visualisation Tools

Demonstrates construction of bar, line, scatter, and map charts in a BI tool. Students replicate EDA visuals in a professional visualisation environment.

Lesson 4 • Dashboard Design and Layout

Covers layout hierarchy, interactivity, and filter design for multi-chart dashboards. Students assemble a functional dashboard that answers a defined business question.

Lesson 1 • Common Statistical Tests

Covers t-tests, ANOVA, and chi-square tests for comparing groups and testing associations. Students select and execute the correct test for each analytical scenario.

Lesson 2 • Regression Analysis Fundamentals

Introduces simple and multiple linear regression for modelling relationships and prediction. Students build, evaluate, and interpret regression models on business datasets.

Lesson 3 • Probability and Sampling Foundations

Covers probability rules, distributions, and sampling methods underpinning inference. Students distinguish between population parameters and sample statistics with precision.

Lesson 4 • Confidence Intervals

Explains interval estimation for means and proportions with varying confidence levels. Students construct and communicate confidence intervals for business metrics.

Lesson 1 • Forecasting and Trend Analysis

Covers moving averages, trend decomposition, and basic time-series forecasting methods. Students build a short-term forecast and quantify its uncertainty for stakeholders.

Lesson 2 • A/B Testing and Experimentation

Designs and analyses controlled experiments to evaluate product or process changes. Students plan an A/B test, calculate sample size, and interpret results correctly.

Lesson 3 • Segmentation and Cohort Analysis

Applies clustering logic and cohort tracking to understand customer and user behaviour. Students segment a dataset and compare cohort performance over time.

Lesson 4 • Defining and Scoping Analytical Problems

Translates ambiguous business questions into structured analytical problem statements. Students produce a project brief with defined scope, metrics, and success criteria.