Data Analysis Course for Beginners

Lesson 1 • Measurement Scales and Variables

Explains nominal, ordinal, interval, and ratio scales and their impact on method selection. Correct scale identification prevents analytical errors later.

Lesson 2 • The Data Analysis Workflow

Introduces the end-to-end process: define, collect, clean, analyse, visualise, and communicate. Provides a repeatable framework applied in every subsequent chapter.

Lesson 3 • Types and Sources of Data

Covers quantitative, qualitative, structured, and unstructured data with real examples. Students can identify data types before choosing analysis methods.

Lesson 1 • Pivot Tables and Summary Analysis

Builds pivot tables to aggregate, group, and cross-tabulate data without formulas. Pivot tables are the fastest path from raw data to summary insight.

Lesson 2 • Data Import and Export Workflows

Covers importing CSV, text, and web data and exporting clean outputs for downstream tools. Smooth data handoffs reduce errors between analysis stages.

Lesson 3 • Sorting, Filtering, and Conditional Logic

Demonstrates sorting, multi-level filtering, and conditional formatting to surface patterns quickly. These skills accelerate exploratory data review.

Lesson 4 • Essential Formulas and Functions

Teaches arithmetic, logical, lookup, and text functions used daily by analysts. Each function is applied to realistic business datasets.

Lesson 1 • Standardizing and Transforming Data

Teaches text normalisation, date parsing, unit conversion, and categorical encoding. Standardised data enables accurate merging and comparison across sources.

Lesson 2 • Merging and Reshaping Datasets

Covers joins, unions, pivoting, and melting to combine and restructure data from multiple sources. Correct merge logic prevents row duplication and data loss.

Lesson 3 • Documenting and Auditing Data Changes

Establishes a change log practice and reproducible cleaning scripts to ensure auditability. Documentation protects analytical credibility and enables peer review.

Lesson 4 • Diagnosing Data Quality Problems

Identifies missing values, duplicates, inconsistent formats, and outliers using profiling techniques. Diagnosis precedes any cleaning action to avoid introducing new errors.

Lesson 1 • Visualising Distributions

Selects and interprets histograms, density plots, and box plots for numeric data. Correct chart choice depends on data type and the question being answered.

Lesson 2 • Exploring Relationships Between Variables

Uses scatter plots, correlation matrices, and grouped summaries to detect associations. Relationship exploration guides feature selection and hypothesis formation.

Lesson 3 • Frequency Analysis and Cross-Tabulation

Builds frequency tables and contingency tables to examine categorical variable distributions. Cross-tabulation reveals group differences before formal testing.

Lesson 4 • Identifying and Handling Outliers

Applies Z-score, IQR, and visual methods to detect outliers and decides on treatment. Outlier decisions must be justified by domain knowledge, not just statistics.

Lesson 1 • Dashboards and Multi-Chart Layouts

Arranges multiple charts into a coherent dashboard that tells a single analytical story. Layout hierarchy and interactivity guide the viewer's attention effectively.

Lesson 2 • Common Visualisation Mistakes to Avoid

Diagnoses truncated axes, dual-axis misuse, 3D distortion, and misleading scales. Recognising errors in others' charts sharpens critical analytical judgement.

Lesson 3 • Visual Design Principles for Analysts

Applies Gestalt principles, colour theory, and data-ink ratio to improve chart clarity. Good design reduces cognitive load and speeds audience comprehension.

Lesson 4 • Building Charts in Spreadsheet Tools

Creates and formats publication-ready charts using spreadsheet charting features. Hands-on practice reinforces design principles with real datasets.

Lesson 1 • Subqueries and Common Table Expressions

Introduces subqueries and CTEs to break complex queries into readable, reusable steps. Modular query design reduces errors and improves maintainability.

Lesson 2 • Joining Multiple Tables

Applies INNER, LEFT, RIGHT, and FULL joins to combine data across related tables. Join type selection determines which records appear in the result set.

Lesson 3 • Relational Database Concepts

Explains tables, primary keys, foreign keys, and relationships in a relational model. Understanding schema structure is essential before writing any query.

Lesson 4 • Aggregation and Grouping

Uses GROUP BY with COUNT, SUM, AVG, MIN, and MAX to produce summary statistics from raw tables. HAVING filters aggregated results the way WHERE filters rows.

Lesson 1 • Probability Fundamentals for Analysts

Covers basic probability rules, conditional probability, and independence with business examples. Probability literacy prevents misinterpretation of risk and likelihood.

Lesson 2 • Common Statistical Tests

Applies t-tests, chi-square tests, and ANOVA to answer typical business questions. Test selection depends on data type, sample size, and the comparison being made.

Lesson 3 • Hypothesis Testing Framework

Builds the null/alternative hypothesis structure, significance levels, and decision rules. A consistent framework prevents ad hoc interpretation of test results.

Lesson 4 • Correlation and Simple Regression

Quantifies linear relationships with correlation and builds simple linear regression models. Regression extends correlation by enabling prediction and effect estimation.

Lesson 1 • Defining and Tracking Key Metrics

Guides selection of KPIs aligned to business goals and builds monitoring frameworks. Metrics without context mislead; this section teaches contextual interpretation.

Lesson 2 • Structuring an Analytical Narrative

Applies the Situation-Complication-Resolution framework to organise findings logically. A strong narrative ensures the audience understands the so-what before the how.

Lesson 3 • Writing Clear Analytical Reports

Covers executive summaries, methodology sections, findings, and recommendations in written reports. Each section serves a distinct audience need and must be written accordingly.

Lesson 4 • Presenting Data to Non-Technical Audiences

Adapts language, chart complexity, and level of detail to audience expertise and decision role. Effective presentation converts analysis into organisational action.