Data Analytics Tutorial

Last Updated : 9 Jun, 2026

Data Analytics is a process of examining, cleaning, transforming and interpreting data to discover useful information, draw conclusions and support decision-making. It helps businesses and organizations understand their data better, identify patterns, solve problems and improve overall performance.

Foundations

This section explains the basic concepts of data, types of analytics and the difference between Data Science and Data Analytics.

Excel

This section covers how Excel is used for data cleaning, analysis, formulas, pivot tables, charts and dashboards.

Introduction
Basic Excel Formulas
Sorting
Filtering
Conditional formatting
Data Validation
Removing duplicates
Lookup functions: VLOOKUP, HLOOKUP, INDEX & MATCH
Text functions: LEFT, RIGHT, MID, CONCATENATE
IF Function
Date Functions
Creating pivot tables
Charts
Dashboards

Python

This section introduces Python basics and explains how it is used for data analysis and visualization.

Data Analysis Libraries

This section focuses on important Python libraries used for data manipulation, numerical analysis, visualization and basic modeling.

Pandas: Data manipulation and analysis
NumPy: Numerical operations and matrix handling
Matplotlib/Seaborn: Data visualization

Reading and Loading Datasets

This section explains how to import data from different file formats and prepare it for analysis.

Data Preprocessing

This section covers techniques used to clean, transform and prepare data before analysis.

Data Visualization

This section explains how charts and graphs are used to present data clearly and highlight key insights.

SQL

SQL is essential for working with structured data stored in databases. This section focuses on querying, filtering, aggregating and optimizing data for analysis.

Introduction
Installing MySQL/PostgreSQL
CREATE DATABASE
Queries
Operators
Aggregate functions
Joins
Subqueries
Window Functions
Date and Time Functions
Data Cleaning: Duplicates, Missing values & Type casting
Performance Basics: Indexes & Query optimization

Mathematics & Statistics

Mathematics and statistics provide the core logic behind data analysis. This section helps in understanding data patterns, measuring uncertainty and making data-driven decisions

Probability

Basic probability: Sample space, Types of events, Probability Rules
Conditional Probability
Bayes' Theorem
Probability distributions

Statistics

Descriptive Statistics: Mean, Median, Mode, Variance, Standard deviation
Inferential Statistics: Confidence Interval, Hypothesis Testing, Central Limit Theorem
Skewness and Kurtosis
Tests: T-test, F-Test, Z-test, Chi-square Test
Correlation: Pearson, Spearman

Linear Algebra & Calculus

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) helps understand data through summaries and visualizations. It is used to identify patterns, trends and potential issues in data.

Power BI

Power BI helps transform raw data into interactive dashboards and reports. This section focuses on data modeling, DAX calculations and visual storytelling.

Tableau

Tableau is a popular data visualization tool used to explore data and build interactive dashboards. It enables analysts to communicate insights effectively through visuals.