AI+ Data™

AI+ Data™

Mastering AI, Maximizing Data: Your Path to Innovation

Certificate Code: AT-120

About This Course

  • 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

Certificate Overview

Included

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

Duration

  • Instructor-Led: 5 days (live or virtual) 
  • Self-Paced: 40 hours of content

Prerequisites

Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.

Exam Format

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

Course Modules

1

Course Overview

  1. Course Introduction Preview
2

Module 1: Foundations of Data Science

  1. 1.1 Introduction to Data Science
  2. 1.2 Data Science Life Cycle
  3. 1.3 Applications of Data Science
3

Module 2: Foundations of Statistics

  1. 2.1 Basic Concepts of Statistics
  2. 2.2 Probability Theory
  3. 2.3 Statistical Inference
4

Module 3: Data Sources and Types

  1. 3.1 Types of Data
  2. 3.2 Data Sources
  3. 3.3 Data Storage Technologies
5

Module 4: Programming Skills for Data Science

  1. 4.1 Introduction to Python for Data Science
  2. 4.2 Introduction to R for Data Science
6

Module 5: Data Wrangling and Preprocessing

  1. 5.1 Data Imputation Techniques
  2. 5.2 Handling Outliers and Data Transformation
7

Module 6: Exploratory Data Analysis (EDA)

  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization
8

Module 7: Generative AI Tools for Deriving Insights

  1. 7.1 Introduction to Generative AI Tools
  2. 7.2 Applications of Generative AI
9

Module 8: Machine Learning

  1. 8.1 Introduction to Supervised Learning Algorithms
  2. 8.2 Introduction to Unsupervised Learning
  3. 8.3 Different Algorithms for Clustering
  4. 8.4 Association Rule Learning with Implementation
10

Module 9: Advance Machine Learning

  1. 9.1 Ensemble Learning Techniques
  2. 9.2 Dimensionality Reduction
  3. 9.3 Advanced Optimization Techniques
11

Module 10: Data-Driven Decision-Making

  1. 10.1 Introduction to Data-Driven Decision Making
  2. 10.2 Open Source Tools for Data-Driven Decision Making
  3. 10.3 Deriving Data-Driven Insights from Sales Dataset
12

Module 11: Data Storytelling

  1. 11.1 Understanding the Power of Data Storytelling
  2. 11.2 Identifying Use Cases and Business Relevance
  3. 11.3 Crafting Compelling Narratives
  4. 11.4 Visualizing Data for Impact
13

Module 12: Capstone Project - Employee Attrition Prediction

  1. 12.1 Project Introduction and Problem Statement
  2. 12.2 Data Collection and Preparation
  3. 12.3 Data Analysis and Modeling
  4. 12.4 Data Storytelling and Presentation
14

Optional Module: AI Agents for Data Analysis

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

AI Tools You'll Learn

Google Colab

Google Colab

MLflow

MLflow

Alteryx

Alteryx

KNIME

KNIME