Course Details

Become a job-ready Data Analyst with a practical, project-based learning experience designed to build strong data analysis and problem-solving skills. Learn how to collect, clean, analyze, visualize, and interpret data using industry-relevant tools such as Excel, SQL, Python, and Power BI. You will also learn statistics, data visualization, dashboards, and business analytics through hands-on projects. By the end of the course, you will be able to work with real-world datasets, create interactive dashboards, generate meaningful insights, and prepare a professional portfolio for Data Analyst internships and entry-level jobs.

Module 1: Introduction to Data Analytics
• Introduction to Data Analytics
• Role of a Data Analyst
• Data Analytics Lifecycle
• Types of Data
• Structured and Unstructured Data
• Data Collection and Sources
• Understanding Business Problems
• Data-Driven Decision Making

Module 2: Excel for Data Analysis
• Excel Fundamentals
• Data Entry and Formatting
• Sorting and Filtering
• Excel Tables
• Basic and Advanced Formulas
• IF, SUMIF, COUNTIF and related functions
• VLOOKUP and XLOOKUP
• INDEX and MATCH
• Conditional Formatting
• Data Cleaning in Excel
• Pivot Tables
• Pivot Charts
• Excel Dashboards

Module 3: Statistics for Data Analytics
• Introduction to Statistics
• Mean, Median and Mode
• Range and Variance
• Standard Deviation
• Percentiles and Quartiles
• Probability Basics
• Correlation
• Regression Fundamentals
• Sampling
• Hypothesis Testing Basics
• Interpreting Statistical Results

Module 4: SQL Fundamentals
• Introduction to Databases
• Relational Database Concepts
• SQL Syntax
• SELECT Statements
• WHERE Clause
• ORDER BY and GROUP BY
• Aggregate Functions
• INSERT, UPDATE and DELETE
• DISTINCT
• CASE Statements
• Subqueries
• Joins
• Primary and Foreign Keys

Module 5: Advanced SQL
• INNER, LEFT, RIGHT and FULL Joins
• Common Table Expressions
• Window Functions
• Ranking Functions
• Date and Time Functions
• String Functions
• Data Aggregation
• Advanced Filtering
• Query Optimization Basics
• Solving Real-World Business Problems with SQL

Module 6: Python for Data Analytics
• Introduction to Python
• Variables and Data Types
• Conditional Statements
• Loops
• Functions
• Lists, Tuples and Dictionaries
• Exception Handling
• Working with Files
• Introduction to Jupyter Notebook

Module 7: Python Data Analysis Libraries
• NumPy Fundamentals
• Pandas Fundamentals
• Series and DataFrames
• Importing Data
• Data Selection and Filtering
• Handling Missing Data
• Data Cleaning with Pandas
• Data Transformation
• GroupBy and Aggregation
• Merging and Joining Data

Module 8: Data Visualization with Python
• Introduction to Data Visualization
• Matplotlib
• Seaborn
• Creating Charts and Graphs
• Bar Charts
• Line Charts
• Pie Charts
• Histograms
• Scatter Plots
• Correlation Visualization
• Choosing the Right Visualization

Module 9: Power BI
• Introduction to Power BI
• Power BI Desktop
• Importing Data
• Data Transformation with Power Query
• Data Cleaning
• Data Modeling
• Relationships Between Tables
• Creating Visualizations
• Filters and Slicers
• DAX Fundamentals
• Calculated Columns and Measures
• Interactive Dashboards
• Publishing and Sharing Reports

Module 10: Data Cleaning & Preparation
• Understanding Data Quality
• Handling Missing Values
• Removing Duplicates
• Detecting Outliers
• Data Formatting
• Data Transformation
• Data Validation
• Preparing Data for Analysis
• Exploratory Data Analysis

Module 11: Business & Exploratory Data Analysis
• Understanding Business Requirements
• Asking the Right Data Questions
• Exploratory Data Analysis
• Identifying Trends and Patterns
• Customer Analysis
• Sales Analysis
• Financial Analysis
• KPI Analysis
• Generating Business Insights
• Presenting Data-Driven Recommendations

Module 12: Real-World Projects
• Sales Performance Analysis
• Customer Segmentation Analysis
• E-Commerce Data Analysis
• HR Analytics Dashboard
• Financial Data Analysis
• Marketing Campaign Analysis
• Superstore Sales Dashboard
• Final End-to-End Data Analytics Project

Module 13: Career Preparation
• Data Analyst Interview Questions
• Excel Interview Preparation
• SQL Interview Preparation
• Python Interview Preparation
• Power BI Interview Preparation
• Data Analytics Case Studies
• Portfolio Project Preparation
• GitHub Portfolio Setup
• Resume Preparation
• Data Analyst Internship and Job Preparation