Data Analytics Course Syllabus
Course Syllabus
Download SyllabusModule 1: Core Python for Data Analytics
- Python fundamentals: syntax, variables, and data types
- Control structures: loops, conditionals, and error handling
- Functions, modules, and reusable code practices
- Object-Oriented Programming (OOP) concepts
- Working with Python libraries for data handling and analysis
Module 2: Data Analysis Using Pandas
- Data cleaning, filtering, sorting, and indexing
- Handling missing values, duplicates, and inconsistent data
- Grouping, merging, concatenation, and pivot operations
- Exploratory Data Analysis (EDA) and statistical computations
- Time-series analysis and working with large datasets
Module 3: SQL & Advanced SQL for Analytics
- Writing basic to advanced SQL queries
- Joins, subqueries, aggregations, and nested queries
- Window functions, indexing, and query optimization
- Working with relational databases
- Integrating SQL with Python for analytics use cases
Module 4: Excel for Data Processing
- Data cleaning and transformation using formulas
- Pivot tables, charts, and conditional formatting
- Creating dynamic dashboards and reports
- Data validation and automation techniques
- Integrating Excel with SQL and Python for analytics
Module 5: Power BI for Business Intelligence
- Power BI fundamentals and interface
- Building interactive dashboards, reports, and KPIs
- Data modeling, relationships, and DAX functions
- Visualization best practices and storytelling
- Connecting Power BI with multiple data sources
Module 6: Data Visualization & Storytelling
- Creating charts and visualizations using Python and Power BI
- Selecting the right visualization for different data types
- Data storytelling and insight presentation
- Customizing dashboards and reports
- Communicating insights effectively to stakeholders
By completing this course, learners gain end-to-end exposure to data analytics tools and workflows, from data preparation and analysis to visualization and reporting. The program helps build practical analytics skills required to support data-driven decision-making across various business environments.
