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AI+ Data Practitioner™

AI+ Data Practitioner™
  • 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
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Certificate Code

AT-120

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. Course Introduction Preview

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

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

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

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

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

  1. 6.1 Introduction to EDA
  2. 6.2 Data Visualization

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

  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

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

  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

  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

  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

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

AI Tools Covered

Google Colab
MLflow
Alteryx
KNIME
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