Course Syllabus
Download SyllabusModule 1: Core Python
- Introduction to Python programming: syntax, variables, and data types
- Control structures: loops, conditionals, and error handling
- Functions, modules, and packages for code reusability
- Object-Oriented Programming (OOP) concepts: classes, objects, inheritance, and polymorphism
- Working with Python libraries for data handling and basic analytics
Module 2: Pandas Functions
- Data manipulation: filtering, sorting, and indexing datasets
- Handling missing data, duplicates, and inconsistent records
- Grouping, merging, concatenating, and pivoting datasets
- Exploratory Data Analysis (EDA) and statistical operations
- Time series analysis and working with large datasets
Module 3: SQL & Advanced SQL Functions
- Writing basic and complex SQL queries
- Using joins, subqueries, aggregations, and nested queries
- Window functions, indexing, and query optimization
- Managing relational databases and integrating SQL with Python for analytics
- Real-time dataset querying and performance analysis
Module 4: Excel Processing
- Data cleaning and transformation using formulas and functions
- Pivot tables, charts, and conditional formatting
- Creating dynamic dashboards and reports
- Data validation and automation using macros
- Integration of Excel with Python and SQL for advanced analytics
Module 5: Power BI
- Introduction to Power BI and its interface
- Building interactive dashboards, reports, and KPIs
- Data modeling, relationships, and DAX formulas
- Data visualization best practices and storytelling
- Connecting Power BI to various data sources (SQL, Excel, APIs)
Module 6: Visualizations
- Creating charts, graphs, and plots using Python libraries and Power BI
- Understanding different types of visualizations for different data
- Data storytelling and presenting actionable insights
- Customizing visualizations for dashboards and reports
- Best practices for effective communication of data insights
By completing this course, learners gain a comprehensive understanding of data analytics tools and techniques, from Python and SQL to Excel and Power BI. Graduates are prepared to handle end-to-end data workflows, create impactful visualizations, and contribute effectively to data-driven decision-making in any organization.
