Training on Data Science and Visualization for AI Projects

Module 1: Data Collection, Cleaning, and Preparation

• Data sources and collection methods
• Data cleaning: handling missing values, outliers, and inconsistencies
• Data transformation and normalization
• Feature engineering for AI projects
• Data quality and validation techniques
• Case Study: Cleaning and preparing a real-world dataset

Module 2: Exploratory Data Analysis (EDA)

• Importance of EDA in AI projects
• Descriptive statistics and summary measures
• Data visualization for EDA
• Identifying patterns, trends, and anomalies
• Formulating hypotheses from EDA
• Case Study: Conducting EDA on a real-world dataset

Module 3: Data Visualization Principles and Techniques

• Principles of effective data visualization
• Types of visualizations: charts, graphs, maps, and plots
• Choosing appropriate visualizations for different data types
• Creating interactive visualizations
• Visual communication and storytelling with data
• Case Study: Creating a visualization dashboard for AI insights

Module 4: Statistical Analysis for AI Projects

• Key statistical concepts for AI
• Hypothesis testing and confidence intervals
• Correlation and regression analysis
• Statistical techniques for model evaluation
• Avoiding statistical pitfalls in AI projects
• Case Study: Applying statistical analysis to an AI dataset

Module 5: Data Storytelling and Communication

• Structuring a data story
• Creating compelling data narratives
• Targeting different audiences with data insights
• Using visualizations in presentations and reports
• Best practices for data communication
• Case Study: Presenting AI insights to stakeholders

Module 6: Advanced Data Visualization Techniques

• Interactive dashboards with Plotly and Dash
• Geospatial data visualization with maps
• Time series data visualization
• Visualization for model evaluation and interpretation
• Customizing and styling visualizations
• Case Study: Building an advanced data visualization dashboard

Module 7: Data Wrangling and Transformation at Scale

• Working with large datasets efficiently
• Data aggregation and summarization
• Pivot tables and crosstabulation
• Advanced data manipulation with Pandas
• Scaling data wrangling with Dask and Spark
• Case Study: Performing data wrangling on a large dataset

Module 8: Data Visualization for Model Interpretation

• Visualizing model predictions and errors
• Feature importance visualization
• SHAP and LIME visualizations for model explanation
• Visualizing model performance metrics
• Communicating model insights with visualizations
• Case Study: Creating visualizations to explain a machine learning model

Module 9: Data Quality and Governance

• Understanding data quality dimensions
• Data quality assessment and monitoring
• Data governance frameworks and best practices
• Data lineage and provenance tracking
• Ensuring data integrity for AI projects
• Case Study: Implementing a data quality framework

Module 10: Data Science Project Management

• Managing data science projects
• Agile and iterative approaches for data science
• Collaborative workflows and tools
• Communicating project progress and results
• Ensuring reproducibility and documentation
• Case Study: Managing a data science project from start to finish