Day 1

Session 1: Introduction to AI and Data Analysis

• What is AI and how does it support data analysis?
• Key concepts in data analysis: data types, variables, and datasets
• The data analysis process: from raw data to actionable insights

Session 2: Setting Up for Data Analysis

• Introduction to Python for data analysis
• Installing Jupyter Notebooks and Python libraries (Pandas, Matplotlib, Seaborn)
• Overview of data structures in Python: lists, arrays, and dataframes

Session 3: Hands-on Lab: Getting Started with Python for Data Analysis

• Loading datasets using Pandas
• Exploring data: viewing, summarizing, and describing datasets
• Basic data manipulation: sorting, filtering, and grouping data

Session 4: Data Visualization for Beginners

• Importance of data visualization in understanding data
• Types of visualizations: bar charts, line graphs, scatter plots, histograms
• Introduction to Matplotlib and Seaborn for data visualization

Session 5: Hands-on Lab: Data Visualization with Python

• Creating basic plots to visualize data trends
• Customizing plots: titles, labels, legends, and color schemes
• Identifying insights from visual data representations

Day 2

Session 1: Basic Statistical Analysis with AI Tools

• Descriptive statistics: mean, median, mode, variance, and standard deviation
• Introduction to correlation analysis and covariance
• Identifying data distributions and outliers

Session 2: Hands-on Lab: Statistical Analysis Using Python

• Calculating basic statistics with Pandas
• Performing correlation analysis to identify relationships between variables
• Detecting outliers using statistical techniques

Session 3: Introduction to Pattern Recognition with AI

• What is pattern recognition and why is it important?
• Basic AI techniques for identifying patterns in data
• Introduction to anomaly detection for spotting unusual data points

Session 4: Hands-on Lab: Pattern Recognition and Anomaly Detection

• Implementing simple pattern recognition techniques using Python
• Detecting anomalies in real-world datasets
• Case study: Identifying fraud patterns in financial transactions

Session 5: Real-World Applications of Data Analysis with AI

• Case study 1: Sales trend analysis in retail
• Case study 2: Customer behavior analysis using AI-driven insights
• Case study 3: Predictive insights for business growth

Session 6: Group Activity and Course Wrap-Up

• Group activity: Analyze a sample dataset to uncover insights and present findings
• Key takeaways from the course
• Resources for continued learning in data analysis and AI
• Q&A session to address participants’ specific questions