Data Analytics Syllabus

Module 1: Introduction to data analytics

• What is Data Analytics?

• Types of Data Analytics

• The Data Analytics Process

• Importance of Data Analytics in Business

• Tools and Technologies in Data Analytics

• Real-World Examples

• Career scope and job roles in Data Analytics

Module 2: Excel for Data Analytics

• Introduction to Excel

• Understanding Cell Referencing

• Basic Arithmetic and Logical Operations

• Data Cleaning and Formatting

• Data Validation & Drop-downs

• Flash Fill and AutoFill

• Functions and Formulas

• Array Formulas & Dynamic Arrays (FILTER, SORT, UNIQUE)

• Pivot Tables and Charts

• Slicers and Timelines for Interactive Filtering

• Conditional Formatting

• Dashboard Creation

• Linking Pivot Tables with Slicers

• Introduction to Macros and VBA

• Workbook Protection and Data Security

Module 3: SQL for Data Analytics

• Introduction to SSMS

• Introduction to RDBMS (MySQL, SQL Server)

• Understanding Tables, Rows, Columns

• SQL Syntax, Case Sensitivity, Comments

• SQL Commands

• Using Aliases

• Filtering and Logic

• Aggregations & Grouping

• Difference between WHERE vs HAVING

• Joins – Inner, Left, Right, Full Outer, Self, Cross Join

• Subqueries & Nested Queries

• Window (Analytic) Functions

• DML – Data Manipulation Language

• DDL – Data Definition Language

• Time Series Analysis

Module 4: Power BI for Data Analytics

• Introduction to Power BI

• Installing Power BI Desktop

• Power BI Interface

• Data Loading & Importing from Various Sources

• Understanding Power BI File Types

• Power Query – Data Transformation

• Cleaning and Shaping Data

• Filtering and Sorting Data

• Unpivoting and Pivoting Data

• Data Modeling

• Creating Date Table & Time Intelligence

• DAX Basics: SUM, COUNT, DISTINCT, AVERAGE

• Logical, Aggregation and Filter Functions

• Data Visualization: Bar, Column, Line, Pie, Donut

• Advanced Charts: TreeMap, Funnel, Waterfall, Gauge, KPI

• Slicers and Filters

• Dashboard and Report Design

• Power BI Service

• Row-Level Security

Hands-on Practical Exercises

• E-commerce Product Performance

• Sales Dashboard

Module 5 : Data Analytics with Python

• Introduction to Python

• Why Python for Data Analytics?

• Python Syntax and Code Structure

• Arithmetic & Logical Operators

• Conditional Statements

• Loops & Functions

• Error Handling

• Data Structures in Python

• NumPy – Numerical Python

• NumPy Arrays vs Lists

• Statistical Functions: mean(), std(), sum()

• Pandas – Data Handling Library

• Visual EDA with Pandas

• Indexing, Filtering, and Slicing

• GroupBy and Aggregations

• Sorting, Renaming Columns

• Data Cleaning and Preparation

• Data Visualization with Python

• Statistical Analysis with Python

Module 6: Data Cleaning & Exploratory Data Analysis(EDA)

• Handling Missing Data: Imputation, Deletion

• Data Normalization and Standardization

• Detecting and Handling Outliers

• Feature Engineering

• EDA: Distribution Analysis, Correlation, Feature Selection

Frequently Asked Questions

1. What topics are covered in a complete data analytics program?

A complete data analytics program generally includes Excel, SQL, Power BI, Python, data visualization, dashboard creation, data cleaning, exploratory data analysis, and reporting techniques used for business decision-making.

2. Is this data analytics course suitable for beginners?

Yes. The course starts with fundamental concepts before introducing advanced analytical tools and practical applications, making it suitable for beginners as well as learners looking to strengthen their existing knowledge.

3. Why are Excel, SQL, Power BI, and Python important for data analytics?

These tools support different stages of the analytics process. Excel helps organize and analyze data, SQL retrieves information from databases, Power BI creates interactive dashboards, and Python enables advanced data analysis, automation, and visualization.