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

Course Overview

* 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

Exam: 50 questions, 70% passing, 90 minutes, online proctored exam

Instructor-led OR Self-paced course + Official exam + Digital badge