Data Analytics Tutorial
Learn data analytics from the ground up, covering analytical thinking, spreadsheets, SQL, Python, statistics, data visualisation, exploratory data analysis, data cleaning, business and marketing analytics, forecasting, machine learning basics, and data workflows. See how each technique works, compare tools and methods, apply them to real business questions, and build job-ready skills through practical examples, exercises, and an end-to-end capstone project.
11 Modules
61 Lessons
English
1 Hr
Reading Plan
MODULE 1
Foundations of Data Analytics
MODULE 2
Spreadsheets and Practical Data Handling
MODULE 3
SQL for Data Analytics
MODULE 4
Python for Data Analytics
MODULE 5
Statistics for Data Analytics
MODULE 6
Data Visualization Reporting and Dashboards
MODULE 7
Exploratory Data Analysis and Data Cleaning
MODULE 8
Business Product and Marketing Analytics
MODULE 9
Advanced Analytics - Forecasting and Machine Learning Basics
MODULE 10
Data Workflows - Warehousing and Professional Practice
MODULE 11
Final Lesson: Bringing It All Together
Data Analytics Tutorial
This beginner-friendly data analytics tutorial covers the fundamentals, core tools, and real business applications of data analytics. Learn spreadsheets, SQL, Python, statistics, data visualisation, data cleaning, business analytics, forecasting, and machine learning basics through simple explanations, practical examples, code, and hands-on exercises. Turn raw data into clear insights and build a strong foundation for a data analytics career.
Data Analytics Tutorial for Beginners: Skills , Tools & Examples
Learn data analytics fundamentals, spreadsheets, SQL, Python, statistics, data visualisation, data cleaning, business analytics, and how to turn data into insights.
Data Analytics Tutorial for Beginners: Tools, Examples & Applications
Ideal for students, freshers, career switchers, and working professionals who want to learn data analytics, make data-driven decisions, and build job-ready skills.
Prerequisites
This course is suitable for:
- Basic computer knowledge.
- Basic understanding of mathematics, such as percentages and averages, is helpful.
- Familiarity with Excel or Google Sheets is beneficial but not mandatory.
- No prior knowledge of SQL, Python, statistics, or data analytics is required.









