Day 1

Session 1: Introduction to Python for AI and Data Science

• What is Python? Overview and applications in AI and data science
• Setting up the Python environment (Anaconda, Jupyter Notebooks)
• Writing your first Python program: print statements, variables, and basic syntax

Session 2: Python Fundamentals

• Data types: integers, floats, strings, and booleans
• Control structures: if-else statements, loops (for, while)
• Functions: defining, calling, and using functions effectively

Session 3: Hands-on Lab: Python Basics

• Writing simple Python scripts
• Using loops and conditional statements to automate tasks
• Creating functions for basic data processing

Session 4: Introduction to Data Structures in Python

• Lists, tuples, sets, and dictionaries
• Working with data structures: adding, removing, and accessing elements
• Practical applications of data structures in data analysis

Session 5: Hands-on Lab: Working with Python Data Structures

• Implementing lists and dictionaries for simple data storage
• Nested loops and conditional logic for data handling
• Real-world exercises: managing datasets with Python basics

Day 2

Session 1: Introduction to NumPy for Numerical Computing

• What is NumPy? Importance in AI and data science
• Working with NumPy arrays: creation, indexing, and slicing
• Mathematical operations with NumPy: basic arithmetic, statistical functions

Session 2: Hands-on Lab: NumPy for Data Analysis

• Creating and manipulating arrays
• Performing mathematical operations on large datasets
• Using NumPy for basic statistical analysis

Session 3: Introduction to Pandas for Data Manipulation

• Overview of Pandas: Series and DataFrames
• Loading data from CSV, Excel, and JSON files
• Data manipulation techniques: filtering, sorting, grouping, and merging

Session 4: Hands-on Lab: Data Manipulation with Pandas

• Importing real-world datasets into Pandas
• Cleaning and transforming data for analysis
• Aggregating and summarizing data to identify key insights

Session 5: Data Cleaning and Preprocessing

• Handling missing values, duplicates, and inconsistent data
• Data transformation: normalization, encoding, and date-time processing
• Preparing datasets for analysis and visualization

Day 3

Session 1: Introduction to Data Visualization with Matplotlib

• Importance of data visualization in data science
• Creating basic plots: line charts, bar graphs, histograms, and scatter plots
• Customizing plots: titles, labels, legends, and colors

Session 2: Hands-on Lab: Data Visualization with Matplotlib

• Visualizing real-world datasets to identify patterns and trends
• Combining multiple plots for comparative analysis
• Exporting visualizations for reports and presentations

Session 3: Advanced Data Analysis with Pandas and NumPy

• Time series analysis basics with Pandas
• Pivot tables and advanced grouping techniques
• Applying statistical functions for data summarization

Session 4: Hands-on Lab: End-to-End Data Analysis Project

• Importing, cleaning, analyzing, and visualizing a real-world dataset
• Applying all concepts learned to extract insights from data
• Presenting data-driven findings in a structured format

Session 5: Real-World Applications and Course Wrap-Up

• Case study 1: Analyzing sales data to identify business trends
• Case study 2: Data-driven decision-making in marketing campaigns
• Key takeaways and next steps for advanced AI and data science learning
• Q&A session to address participants’ specific questions