Course Outline
Module 1: Python for Data Science
Introduction to Python for Data Science
- Python Basics: Syntax, Variables, and Data Types
- Control Structures: Conditionals and Loops
- Functions and Modules
Data Analysis with Python
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Data Cleaning and Preprocessing
Data Visualization
- Matplotlib and Seaborn for Visualization
- Creating Plots and Graphs
- Advanced Visualization Techniques
Machine Learning with Python
- Introduction to Machine Learning
- Supervised and Unsupervised Learning
- Scikit-Learn for Building Models
Module 2: Power BI for Data Visualization
Introduction to Power BI
- Overview of Power BI and Its Components
- Connecting to Data Sources
- Data Transformation and Modeling
Data Visualization with Power BI
- Creating and Customizing Visualizations
- Using Filters, Slicers, and Drill-Downs
- Interactive Dashboards and Reports
Advanced Power BI Features
- Power Query for Data Shaping
- DAX (Data Analysis Expressions) for Calculations
- Power BI Service and Sharing Reports
Module 3: Excel for Data Analysis
Excel Basics and Functions
- Introduction to Excel Interface and Tools
- Basic and Advanced Formulas
- Data Sorting, Filtering, and PivotTables
Data Analysis Techniques
- Using Excel for Data Cleaning and Preparation
- Analyzing Data with Charts and Graphs
- Advanced PivotTables and PivotCharts
Excel for Data Visualization
- Creating Effective Data Visualizations
- Using Conditional Formatting
- Dashboards and Data Presentation
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