Data Analyst Course for Beginners

Lesson 1 • Core Statistical Concepts for Analysts

Introduces mean, median, variance, distributions, and probability fundamentals. These concepts underpin every analytical technique taught later.

Lesson 2 • Types and Sources of Data

Distinguishes structured, semi-structured, and unstructured data and common source systems. Provides vocabulary used throughout the course.

Lesson 3 • Setting Up Your Work Environment

Guides installation and configuration of core tools used in the course. Ensures every student has a functional, consistent workspace before coding begins.

Lesson 4 • The Data Analysis Lifecycle

Maps the end-to-end process from business question to insight delivery. Students use this framework to organize every project in the course.

Lesson 1 • Excel Interface and Data Entry

Covers the ribbon, cell referencing, and efficient data entry techniques. Establishes spreadsheet literacy needed for all Excel-based analysis.

Lesson 2 • Basic Data Visualization in Excel

Covers chart selection, formatting, and dashboard layout principles in Excel. Prepares students to present findings before learning dedicated BI tools.

Lesson 3 • Essential Formulas and Functions

Teaches SUM, AVERAGE, IF, VLOOKUP, INDEX-MATCH, and text functions. These formulas automate calculations central to data summarization tasks.

Lesson 4 • PivotTables and PivotCharts

Demonstrates how to summarize large datasets interactively with PivotTables. Students connect summaries to PivotCharts for quick visual exploration.

Lesson 1 • Writing Basic SELECT Queries

Teaches SELECT, FROM, WHERE, ORDER BY, and LIMIT clauses with hands-on practice. Students retrieve targeted subsets of data from a sample database.

Lesson 2 • Aggregating and Grouping Data

Covers GROUP BY, HAVING, COUNT, SUM, AVG, MIN, and MAX for summary analysis. Aggregation is the bridge between raw records and business metrics.

Lesson 3 • Relational Database Fundamentals

Explains tables, primary keys, foreign keys, and relational schema design. This context makes SQL syntax meaningful rather than mechanical.

Lesson 4 • Joining Multiple Tables

Explains INNER, LEFT, RIGHT, and FULL joins with practical multi-table scenarios. Students combine data from separate tables to answer complex questions.

Lesson 5 • Window Functions for Advanced Analysis

Teaches ROW_NUMBER, RANK, LAG, LEAD, and running totals using OVER and PARTITION BY. Window functions enable time-series and ranking analyses without collapsing rows.

Lesson 1 • Introduction to NumPy

Explains arrays, vectorized operations, and basic linear algebra with NumPy. NumPy underpins pandas and most scientific Python libraries.

Lesson 2 • Data Manipulation with pandas

Teaches DataFrame creation, indexing, filtering, merging, and groupby operations. pandas is the primary tool for tabular data work in Python.

Lesson 3 • Cleaning Data with Python

Addresses missing values, outliers, type conversion, and string normalization in pandas. Reinforces cleaning concepts from Excel with scalable, repeatable code.

Lesson 4 • Exploratory Data Analysis in Python

Uses descriptive statistics and matplotlib/seaborn plots to understand dataset structure. EDA is the analytical thinking phase before formal modeling or reporting.

Lesson 1 • Handling Missing Data

Compares deletion, imputation, and flagging strategies with guidance on when to use each. Incorrect missing-data handling is a leading cause of biased analysis.

Lesson 2 • Understanding Data Quality Issues

Catalogs common quality problems: missing values, duplicates, inconsistencies, and errors. Recognizing issue types is the first step toward systematic remediation.

Lesson 3 • Building a Repeatable Cleaning Pipeline

Guides students in writing modular, documented cleaning scripts that can be rerun. Reproducibility is a professional standard that distinguishes reliable analysts.

Lesson 4 • Reshaping and Restructuring Data

Teaches pivoting, melting, stacking, and splitting columns to match analysis needs. Data shape often determines which analytical operations are possible.

Lesson 1 • Choosing the Right Chart Type

Maps analytical goals—comparison, distribution, relationship, composition—to chart types. Correct chart selection prevents misleading or confusing visualizations.

Lesson 2 • Interactive Dashboards with Power BI

Introduces Power BI Desktop for connecting data, building visuals, and publishing dashboards. Students create an interactive report from a real dataset.

Lesson 3 • Data Storytelling Techniques

Teaches narrative structure, insight sequencing, and annotation strategies for presentations. Storytelling transforms correct analysis into actionable communication.

Lesson 4 • Principles of Effective Visualization

Covers Gestalt principles, pre-attentive attributes, and chart junk reduction. These principles guide every design decision in subsequent sections.

Lesson 1 • Confidence Intervals and Estimation

Explains point estimates, margin of error, and confidence interval construction. Confidence intervals quantify uncertainty, which is essential for honest reporting.

Lesson 2 • Common Statistical Tests

Covers t-tests, chi-square tests, and ANOVA with Python implementation. Students select and run the correct test for a given analytical question.

Lesson 3 • Correlation and Simple Regression

Measures linear relationships with Pearson correlation and fits simple linear regression models. Regression is the gateway to predictive analytics covered next.

Lesson 4 • Hypothesis Testing Framework

Introduces null and alternative hypotheses, p-values, significance levels, and error types. This framework governs all formal statistical tests in the chapter.

Lesson 1 • Presenting and Defending Findings

Prepares students to present analysis, handle questions, and document limitations. Confident presentation is the final step in delivering analytical value.

Lesson 2 • Data Acquisition and Auditing

Covers extracting data via SQL and APIs, then auditing it for quality before analysis. Auditing early prevents downstream errors that invalidate conclusions.

Lesson 3 • Building the Final Deliverable

Assembles findings into a dashboard or slide deck tailored to the target audience. The deliverable format determines how insights reach decision-makers.

Lesson 4 • Scoping and Planning an Analysis

Teaches how to translate a business problem into a structured analytical plan. Clear scoping prevents wasted effort and misaligned deliverables.