Data Analytics Course for Beginners

Lesson 1 • The Four Analytics Categories

Introduces descriptive, diagnostic, predictive, and prescriptive analytics with examples. Students map each category to specific business questions.

Lesson 2 • Types of Data and Their Sources

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

Lesson 3 • The Data Analytics Workflow

Outlines the end-to-end process from problem definition to insight delivery. Provides a repeatable framework that students apply throughout the course.

Lesson 4 • Roles and Career Paths in Analytics

Maps analyst, engineer, and scientist roles to required skills and typical responsibilities. Helps students align course goals with career objectives.

Lesson 1 • Sorting, Filtering, and Tables

Demonstrates sorting, multi-level filtering, and converting ranges to structured tables. Structured tables make data management scalable and formula-aware.

Lesson 2 • Spreadsheet Interface and Navigation

Orients students to rows, columns, cells, and workbook structure. Efficient navigation reduces errors and speeds up all subsequent spreadsheet work.

Lesson 3 • Entering and Formatting Data

Covers data types, cell formatting, and consistent entry conventions. Proper formatting prevents calculation errors and improves readability.

Lesson 4 • PivotTables for Summary Analysis

Builds PivotTables to aggregate, group, and cross-tabulate data without formulas. Students summarise large datasets in minutes using drag-and-drop logic.

Lesson 1 • Detecting and Removing Duplicates

Teaches methods to find exact and fuzzy duplicate records across datasets. Duplicate removal ensures counts and aggregations reflect true data volume.

Lesson 2 • Handling Missing and Null Values

Covers detection, imputation, and deletion strategies for missing data. Choosing the right strategy prevents bias and preserves analytical integrity.

Lesson 3 • Understanding Data Quality Dimensions

Defines accuracy, completeness, consistency, timeliness, and validity as quality pillars. Students audit datasets against these dimensions to prioritise cleaning tasks.

Lesson 4 • Standardising and Transforming Data

Applies text normalisation, date standardisation, and unit conversion to raw fields. Consistent formats enable accurate joins, filters, and comparisons.

Lesson 1 • Subqueries and Common Table Expressions

Introduces subqueries and CTEs to break complex logic into readable, reusable steps. These structures are essential for advanced reporting and data pipeline queries.

Lesson 2 • Joining Multiple Tables

Demonstrates INNER, LEFT, RIGHT, and FULL joins to combine data across tables. Multi-table queries unlock richer analysis unavailable in single-table views.

Lesson 3 • Relational Database Fundamentals

Explains tables, primary keys, foreign keys, and relationships in relational databases. This context makes SQL syntax and join logic intuitive from the start.

Lesson 4 • Writing Basic SELECT Queries

Covers SELECT, FROM, WHERE, ORDER BY, and LIMIT clauses for data retrieval. Students extract targeted subsets of data from any relational table.

Lesson 1 • Categorical Data Exploration

Uses frequency counts, bar charts, and cross-tabulations to explore categorical variables. Categorical EDA reveals segment differences and drives grouping decisions.

Lesson 2 • Univariate Distribution Analysis

Analyses single-variable distributions using histograms, box plots, and frequency tables. Understanding individual variables is the prerequisite for multivariate analysis.

Lesson 3 • Descriptive Statistics Essentials

Covers mean, median, mode, variance, standard deviation, and percentiles for numeric data. These measures summarise distributions and reveal central tendency and spread.

Lesson 4 • EDA Workflow and Documentation

Structures EDA into a repeatable, documented process with findings summaries. Documented EDA ensures reproducibility and communicates assumptions to stakeholders.

Lesson 1 • Dashboard Design and Layout

Applies layout hierarchy, white space, and interactivity to build cohesive dashboards. Well-designed dashboards enable self-service exploration by non-technical users.

Lesson 2 • Building Visualizations with BI Tools

Demonstrates creating charts, filters, and calculated fields in a business intelligence tool. Hands-on practice bridges chart theory to production-ready visual outputs.

Lesson 3 • Data Storytelling for Stakeholders

Structures analytical findings into a narrative arc with context, conflict, and resolution. Storytelling transforms data outputs into decisions by connecting insights to actions.

Lesson 4 • Principles of Effective Data Visualization

Covers visual encoding, pre-attentive attributes, and the data-ink ratio for chart design. These principles prevent misleading visuals and maximise audience comprehension.

Lesson 1 • Python Basics for Analysts

Covers variables, data types, control flow, and functions in Python. These fundamentals enable analysts to write reusable, readable analysis scripts.

Lesson 2 • Visualization with Matplotlib and Seaborn

Creates line, bar, scatter, and distribution plots using Matplotlib and Seaborn. Python-based visuals integrate directly into analysis notebooks for reproducible reporting.

Lesson 3 • Data Cleaning with pandas

Applies pandas methods to handle nulls, duplicates, and type conversions programmatically. Scripted cleaning is reproducible and scalable compared to manual spreadsheet work.

Lesson 4 • Aggregation and GroupBy in pandas

Replicates SQL-style aggregation using groupby, agg, and pivot_table in pandas. Students produce summary tables and cross-tabulations entirely in Python.

Lesson 1 • Building a Portfolio-Ready Dashboard

Designs and publishes an interactive dashboard that showcases analytical findings. A polished dashboard serves as a tangible portfolio artifact for job applications.

Lesson 2 • End-to-End Data Pipeline Practice

Executes a full pipeline from raw data ingestion through cleaning, analysis, and output. Students experience the cumulative complexity of real-world data projects.

Lesson 3 • Defining an Analytics Problem Statement

Translates a vague business challenge into a structured, measurable analytics question. A precise problem statement prevents scope creep and guides all subsequent analysis decisions.

Lesson 4 • Reflecting and Iterating on Analysis

Reviews project decisions, identifies improvement areas, and documents lessons learned. Structured reflection accelerates skill growth and builds analytical maturity.