Overview of Data Analytics – Role of a Data Analyst – Applications – Data Types and Sources – Basics of Data Cleaning – Career Opportunities in Data Analytics. Lab Practice 1. Perform simple data exploration and descriptive statistics using Excel. 2. Explore data sets and perform basic analysis using Python.

Data Cleaning: Handling Missing Data, Removing Duplicates, Outlier Detection. Data Transformation: Normalization, Aggregation, Merging, and Joins. Introduction to Power Query. Lab Practice 1. Perform cleaning and transformation using Excel/Power Query. 2. Apply normalization and aggregation using Python (NumPy, Pandas).

Fundamentals of Data Visualization – Principles of Effective Dashboards – Slicers, Filters, Interactive Visualizations. Introduction to Power BI – Building Basic Reports – Data Visualization with Python (Matplotlib/Seaborn). Lab Practice 1. Create simple charts and plots using Python. 2. Build interactive visualizations in Power BI.

Introduction to Data Modelling – Relationships – Measures and DAX Functions. Advanced Visualization in Power BI – Tableau Basics – Creating Interactive Dashboards. Lab Practice 1. Create a Data Model in Power BI and apply DAX functions. 2. Design dashboards using Tableau.

Publishing Dashboards using Power BI Service – Collaboration and Cloud Sharing. Case Studies: E-commerce, FinTech, Healthcare Analytics. Lab Practice Lab Practice 1. Publish dashboards to Power BI Service. 2. Implement a mini project using real-world dataset. (End-to-End Analytics Solution (Cleaning, Visualization, Dashboard).