AI+ Data™ eLearning

Mastering AI, Maximizing Data: Your Path to Innovation

• Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling

• Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytics

• Capstone Application: Solve real-world problems like employee attrition with AI

• Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship

1. Course Introduction Preview Module 1: Foundations of Data Science
   • 1.1 Introduction to Data Science
   • 1.2 Data Science Life Cycle
   • 1.3 Applications of Data Science
   Module 2: Foundations of Statistics
   • 2.1 Basic Concepts of Statistics
   • 2.2 Probability Theory
   • 2.3 Statistical Inference
   Module 3: Data Sources and Types
   • 3.1 Types of Data
   • 3.2 Data Sources
   • 3.3 Data Storage Technologies
   Module 4: Programming Skills for Data Science
   • 4.1 Introduction to Python for Data Science
   • 4.2 Introduction to R for Data Science
   Module 5: Data Wrangling and Preprocessing
   • 5.1 Data Imputation Techniques
   • 5.2 Handling Outliers and Data Transformation
   Module 6: Exploratory Data Analysis (EDA)
   • 6.1 Introduction to EDA
   • 6.2 Data Visualization
   Module 7: Generative AI Tools for Deriving Insights
   • 7.1 Introduction to Generative AI Tools
   • 7.2 Applications of Generative AI
   Module 8: Machine Learning
   • 8.1 Introduction to Supervised Learning Algorithms
   • 8.2 Introduction to Unsupervised Learning
   • 8.3 Different Algorithms for Clustering
   • 8.4 Association Rule Learning with Implementation
   Module 9: Advance Machine Learning
   • 9.1 Ensemble Learning Techniques
   • 9.2 Dimensionality Reduction
   • 9.3 Advanced Optimization Techniques
   Module 10: Data-Driven Decision-Making
   • 10.1 Introduction to Data-Driven Decision Making
   • 10.2 Open Source Tools for Data-Driven Decision Making
   • 10.3 Deriving Data-Driven Insights from Sales Dataset
   Module 11: Data Storytelling
   • 11.1 Understanding the Power of Data Storytelling
   • 11.2 Identifying Use Cases and Business Relevance
   • 11.3 Crafting Compelling Narratives
   • 11.4 Visualizing Data for Impact
   Module 12: Capstone Project - Employee Attrition Prediction
   • 12.1 Project Introduction and Problem Statement
   • 12.2 Data Collection and Preparation
   • 12.3 Data Analysis and Modeling
   • 12.4 Data Storytelling and Presentation
   Optional Module: AI Agents for Data Analysis
   • 1. Understanding AI Agents
   • 2. Case Studies
   • 3. Hands-On Practice with AI Agents
   Tools you will explore
   • Google Colab
   • MLflow
   • Alteryx
   • KNIME

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.