Week 1: Introduction to Data Analysis & Tools

• Objective: Understand fundamental concepts of data analysis and become familiar with tools like Python, Excel, Power BI, and MySQL.
• Topics:
  • Overview of data analysis and its importance.
  • Introduction to data tools and their applications.
  • Basic Excel functions for data analysis (SUM, AVERAGE, COUNT).
  • Data cleaning basics: handling duplicates, missing values.
• Activities:
  • Install and configure tools.
  • Practice basic data cleaning in Excel.

Week 2: Advanced Excel for Data Analysis

• Objective: Develop advanced skills in Excel for analytical tasks.
• Topics:
  • Advanced functions (VLOOKUP, INDEX-MATCH).
  • Pivot tables and charts for summarizing data.
  • Basic data visualization techniques in Excel.
• Activity: Analyze a sample dataset using pivot tables and charts.

Week 3-4: Data Visualization with Power BI

• Objective: Learn to create interactive dashboards and reports.
• Topics:
  • Importing and cleaning data in Power BI.
  • Creating visualizations: charts, maps, and tables.
  • Introduction to DAX for calculated columns and measures.
• Activities:
  • Build an interactive dashboard to display business trends.

Week 5-6: SQL for Data Analysis

• Objective: Gain foundational and advanced SQL skills for data querying.
• Topics:
  • Writing basic SQL queries: SELECT, WHERE, GROUP BY, ORDER BY.
  • Advanced SQL techniques: joins, subqueries, and aggregations.
  • Creating and managing relational databases.
• Activity: Query a sample database and build summaries for analysis.

Week 7-8: Python for Data Analysis

• Objective: Use Python and pandas for data manipulation and analysis.
• Topics:
  • Python basics: variables, data types, and control structures.
  • Data manipulation with pandas: loading, cleaning, and analyzing data.
  • Data visualization with matplotlib and seaborn.
• Activity: Analyze a dataset and create visualizations using Python.

Week 9: Machine Learning Basics

• Objective: Introduction to machine learning concepts and techniques.
• Topics:
  • Supervised learning: regression, classification.
  • Unsupervised learning: clustering.
  • Evaluation metrics: accuracy, precision, recall, F1 score.
• Activity: Build and evaluate a simple machine learning model.

Week 10: Final Project

• Objective: Apply learned skills to a comprehensive project.
• Project:
  • Define a business problem.
  • Extract and clean data using MySQL.
  • Analyze data using Python/pandas.
  • Create dashboards with Power BI and Excel.
• Presentation: Present findings and dashboards.

Week 11-12: Advanced Machine Learning

• Topics:
  • Deep learning basics with TensorFlow/PyTorch.
  • Neural networks for image recognition or NLP.
  • Hyperparameter tuning and model optimization.

Week 13: AI Ethics and Privacy

• Topics:
  • Responsible AI development.
  • Ethical considerations in data privacy and transparency.

Week 14-15: Big Data and Cloud Integration

• Topics:
  • Introduction to Hadoop and Spark for big data processing.
  • Working with cloud platforms (AWS, Google Cloud) for scalable solutions.

Week 16-20: Capstone AI/ML Project

• Objective: Build an end-to-end AI or machine learning solution for a real-world problem.
• Deliverables:
  • Data preprocessing pipeline.
  • Machine learning model deployment using Flask or Docker.
  • Final presentation with insights and recommendations.