Data Analyst Course

Lesson 1 • The Data Analysis Workflow

Introduces the end-to-end analysis lifecycle from question framing to insight delivery. Provides a repeatable framework students apply in every subsequent chapter.

Lesson 2 • Data Ethics and Governance Basics

Introduces privacy principles, data ownership, and responsible use standards. Sets ethical boundaries students must apply when handling real datasets.

Lesson 3 • The Data Analyst Role

Defines analyst responsibilities, differentiates from data science and engineering roles. Grounds the chapter by establishing professional context before technical content.

Lesson 4 • Types and Sources of Data

Classifies structured, semi-structured, and unstructured data and maps common source types. Enables informed decisions about data collection strategies throughout the course.

Lesson 1 • Automation with Macros and Scripts

Introduces recorded macros and basic scripting to automate repetitive analyst tasks. Bridges manual spreadsheet work toward programmatic data handling introduced later.

Lesson 2 • Spreadsheet Navigation and Setup

Covers workbook structure, cell referencing, and formatting best practices. Establishes efficient habits that prevent errors in more complex spreadsheet work ahead.

Lesson 3 • Essential Formulas and Functions

Teaches lookup, logical, text, and date functions used daily by analysts. Directly enables data transformation tasks covered in later cleaning and analysis sections.

Lesson 4 • Spreadsheet Charts and Dashboards

Covers chart selection, formatting, and linking charts to dynamic data ranges. Introduces visual communication principles applied more deeply in the visualization chapter.

Lesson 1 • Core SQL Query Syntax

Teaches SELECT, WHERE, ORDER BY, and LIMIT clauses for precise data retrieval. Forms the syntactic foundation every subsequent SQL section builds upon.

Lesson 2 • Subqueries and Window Functions

Introduces subqueries, CTEs, and window functions for advanced analytical queries. Unlocks ranking, running totals, and period-over-period comparisons in SQL.

Lesson 3 • Aggregation and Grouping

Covers GROUP BY, HAVING, and aggregate functions for summarizing datasets. Connects statistical aggregation concepts from Chapter 1 to database-scale application.

Lesson 4 • SQL Query Optimization

Covers indexing, execution plans, and query rewriting for performance. Prepares analysts to work responsibly with large production databases.

Lesson 5 • Relational Database Fundamentals

Explains tables, keys, relationships, and schema design concepts. Provides the structural understanding needed to write accurate and efficient queries.

Lesson 1 • Data Cleaning in Python

Addresses missing values, duplicates, type errors, and outlier detection programmatically. Builds on Chapter 1 data quality concepts with scalable, repeatable code solutions.

Lesson 2 • Data Manipulation with pandas

Teaches DataFrame creation, indexing, filtering, and transformation using pandas. Directly mirrors SQL and spreadsheet operations in a programmatic environment.

Lesson 3 • Exploratory Data Analysis in Python

Applies pandas and basic plotting to profile datasets and surface patterns. Connects statistical foundations from Chapter 1 to hands-on investigative coding practice.

Lesson 4 • Numerical Analysis with NumPy

Introduces array operations, mathematical functions, and vectorized computation. Provides the numerical backbone for statistical analysis and future machine learning prep.

Lesson 1 • Reshaping and Restructuring Data

Covers pivoting, melting, stacking, and aggregating to reshape data for analysis needs. Directly applies pandas and SQL skills in a data engineering context.

Lesson 2 • Standardizing and Transforming Data

Teaches normalization, encoding, and format harmonization across datasets. Prepares data for consistent analysis and downstream visualization or modeling.

Lesson 3 • Building a Repeatable Data Pipeline

Introduces pipeline design principles, documentation, and version control for data prep. Ensures cleaning work is auditable, reproducible, and transferable across projects.

Lesson 4 • Handling Missing Data

Covers deletion, imputation, and flagging strategies with decision criteria for each. Builds on Python and SQL skills to implement solutions at scale.

Lesson 1 • Principles of Effective Visualization

Covers perceptual principles, chart selection logic, and common visualization mistakes. Establishes the design thinking framework applied in every subsequent section.

Lesson 2 • Visualization with Python Libraries

Teaches Matplotlib and Seaborn for creating publication-quality static charts in Python. Extends Python skills from Chapter 4 into visual output for analysis reports.

Lesson 3 • Data Storytelling and Narrative Design

Teaches structuring insights into a logical narrative arc for executive audiences. Integrates visualization skills with communication strategy for maximum business impact.

Lesson 4 • Interactive Dashboards with BI Tools

Introduces drag-and-drop BI platforms for building interactive, shareable dashboards. Connects spreadsheet and SQL data sources to visual business reporting layers.

Lesson 1 • Probability and Sampling Distributions

Deepens Chapter 1 probability concepts with sampling distributions and the central limit theorem. Provides the theoretical basis for all inferential techniques that follow.

Lesson 2 • Correlation and Regression Analysis

Teaches Pearson and Spearman correlation and simple linear regression modeling. Connects statistical theory to predictive and explanatory business questions.

Lesson 3 • Comparing Groups with Statistical Tests

Covers t-tests, ANOVA, and chi-square tests for comparing means and distributions. Enables analysts to validate observed differences with statistical rigor.

Lesson 4 • A/B Testing and Experimentation

Applies hypothesis testing to controlled experiments and A/B test design. Prepares analysts to evaluate product, marketing, and operational experiments rigorously.

Lesson 1 • Cohort and Funnel Analysis

Teaches retention cohort analysis and conversion funnel metrics for user behavior. Applies SQL and Python skills to product and marketing analytics use cases.

Lesson 2 • Time Series Analysis

Covers trend, seasonality, and forecasting methods for temporal business data. Applies Python and statistical skills to revenue, demand, and operational time series.

Lesson 3 • KPI Design and Metric Frameworks

Covers designing meaningful KPIs, metric trees, and north star frameworks. Connects analytical output to strategic business objectives and decision-making.

Lesson 4 • Capstone: End-to-End Analysis Project

Integrates all course skills in a full project from raw data to executive presentation. Demonstrates analyst readiness through a portfolio-quality deliverable.