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Week 1: Excel Basics, Pivot Tables, and Power Query

Class 1: Introduction to Data Analysis and Excel Basics (2 hours)

  • Overview of Data Analysis
  • Importance of Data in Decision Making
  • Introduction to Excel Interface
  • Basic Formulas and Functions (SUM, AVERAGE, COUNT)

Class 2: Pivot Tables in Excel (2 hours)

  • Understanding Pivot Tables
  • Creating Pivot Tables
  • Basic Pivot Table Analysis

Class 3: Power Query in Excel and Week 1 Final Project (2 hours)

  • Introduction to Power Query
  • Importing and Transforming Data using Power Query
  • Final Project: Apply Pivot Tables and Power Query to analyze a dataset

Week 2: Power BI Basics

Class 4: Introduction to Power BI (2 hours)

  • Installing and Configuring Power BI
  • Connecting to Data Sources
  • Building Basic Visualizations in Power BI

Class 5: Power BI Advanced Features (2 hours)

  • Data Modeling in Power BI
  • Introduction to DAX for Calculations

Class 6: Power BI Dashboards and Week 2 Final Project (2 hours)

  • Building Interactive Dashboards
  • Report Publishing and Sharing
  • Final Project: Create a comprehensive Power BI dashboard

Week 3: SQL Fundamentals

Class 7: Introduction to SQL (2 hours)

  • Understanding Relational Databases
  • Basic SQL Syntax (SELECT, FROM, WHERE)

Class 8: SQL Advanced Queries (2 hours)

  • JOINs and Subqueries
  • Aggregation Functions (GROUP BY, HAVING)

Class 9: SQL Practice and Week 3 Final Project (2 hours)

  • Hands-on SQL exercises
  • Final Project: Write SQL queries to analyze a given database

Week 4: Python Basics and Data Analysis with Pandas

Class 10: Python Basics (2 hours)

  • Introduction to Python
  • Variables, Data Types, and Operators
  • Control Flow and Loops

Class 11: Data Analysis with Pandas (2 hours)

  • Introduction to Pandas Library
  • Data Structures in Pandas (Series, DataFrame)
  • Data Manipulation and Cleaning with Pandas

Class 12: Capstone Project and Week 4 Final Project (2 hours)

  • Apply Python and Pandas skills
  • Capstone Project: Work on a comprehensive data analysis project
  • Final Project: Present findings and insights from the capstone project

Additional Tips:

  • Self-Study Assignments: Encourage learners to practice outside of class.
  • Online Resources: Provide additional resources for further exploration.
  • Feedback Sessions: Allocate time for feedback and discussion during project presentations.

This curriculum incorporates a final project at the end of each week, allowing learners to immediately apply the skills learned during that week. It promotes hands-on experience and a deeper understanding of the tools and concepts covered in the classes.