Course Structure

The course covers 8 modules to be run over 8 weeks and each module is broken down into 4 sections:

• Theoretical Overview
• Technical Applications
• Project Based Labs
• Interview Questions (Technical plus Behavioral)

Excel for Data Analysis with AI

• Excel Formulas: SUMIF, VLOOKUP, XLOOKUP, INDEX-MATCH and more.
• Data Organization: Sorting, filtering, and conditional formatting.
• Pivot Tables: Analyzing data using pivot tables.
• Power Query: Introduction to data transformation.
• Excel Copilot: Leverage Excel’s AI features for enhanced productivity.

Business Statistics in Excel

• Statistical Concepts: Mean, Median, Mode, Standard Deviation, Correlation, Regression, and Normalization.
• Data Analysis Techniques: Using Excel to perform basic statistical analysis.
• Hands-on assignments to calculate and interpret key statistical metrics.
• Projects to apply business statistics in real-world scenarios.

Power BI for Data Visualization

• Power BI Overview: Introduction to Power BI and its capabilities.
• Data Connections: Connect to various data sources.
• Data Transformation: Clean, model, and transform data using Power BI.
• DAX (Data Analysis Expressions): Learn basic to intermediate DAX functions for calculations.
• Data Modeling and Dashboarding: Create meaningful visualizations and dashboards.
• Publishing to Power BI Service: Share reports and collaborate in the cloud.

Business Fundamentals with AI Charts in Power BI

• Chart Selection: Understand when and how to use different types of charts (bar, line, pie, etc.).
• KPI Tracking: Learn to track key performance indicators in Power BI.
• AI-Driven Visualizations: Use AI-powered insights for data analysis in Power BI.
• Data Refresh Techniques: Ensure your reports are updated automatically with data refresh strategies.

SQL for Data Analysis

• SQL Basics: Introduction to SQL, database concepts, and SQL syntax.
• Key SQL Queries: SELECT, JOIN, WHERE, and GROUP BY clauses.
• Advanced SQL: Window functions, stored procedures, Common Table Expressions (CTEs), temporary tables, and views.
• SQL for Data Analysis: Extracting insights and performing analytics using SQL queries.

ETL and Data Warehousing

• ETL Process: Understand Extract, Transform, and Load (ETL) concepts.
• Data Warehousing: Learn the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing).
• Modern Trends: Explore cloud data warehouses, automation in ETL processes, and data lake concepts.

Python, Pandas, and AI Tools

• Python Basics: Introduction to Python programming, variables, control structures, and functions.
• File Handling: Learn to read, write, and manipulate files (CSV, JSON, Excel).
• Pandas for Data Analysis: Use Pandas Data Frames for data manipulation, handling missing data, merging, and grouping.
• Leveraging AI: Integrating AI tools like ChatGPT into your workflow for technical tasks and problem-solving.

Program Conclusion and Certification

• Final Project: Create a comprehensive data analysis project that incorporates Excel, Power BI, SQL, Python, and AI tools.

Career Counselling

• Resume, Cover Letter Building & Review
• LinkedIn Profile Review
• Job Hunt Strategy Assistance
• Mock Interview Session: Behavioral & Technical
• Interview Preparation
• Assistance with Business and Professional Email Writing