AI+ Data PractitionerTM
Covers data science foundations, statistics, Python, R, data wrangling, generative AI and machine learning, ending with a capstone on employee attrition prediction.
Turn complex data into insight with AI
- Duration:
40 hours self-paced - Format:
Self-paced course, official exam, digital badge - Delivery:
Online labs, projects, case studies - Languages:
English, Spanish, Brazilian Portuguese
Prerequisites
- Basic knowledge of computer science and statistics (beneficial, not mandatory)
- Interest in data analysis
- Willingness to learn Python and R
The AI+ AI+ Data Practitioner™ self-paced course includes:
1. 40 hours of on-demand videos, an e-book, and podcasts.
2. Modular quizzes to help learners monitor their progress.
The course allows learners to study at their own pace. After completing the course, learners can take the certification exam online through a secure remote-proctored system.
Why This Certification Matters?
- Demand for certified experts Organizations need people who can turn complex data into insights while protecting data integrity and privacy.
- Mitigating data and AI risks Poor handling of data and AI can cause inaccurate analysis and business risk.
- Designing AI-driven data strategies Certified professionals help build strategies that improve performance and meet regulatory standards.
- Career advancement AI-powered data solutions are becoming essential, giving certified professionals a competitive edge.
What You'll Learn?
Introduction to Data Science
Data Science Life Cycle
Applications of Data Science
Basic Concepts of Statistics
Probability Theory
Statistical Inference
Types of Data
Data Sources
Data Storage Technologies
Introduction to Python for Data Science
Introduction to R for Data Science
Data Imputation Techniques
Handling Outliers and Data Transformation
Introduction to EDA
Data Visualization
Introduction to Generative AI Tools
Applications of Generative AI
Supervised Learning Algorithms
Unsupervised Learning
Algorithms for Clustering
Association Rule Learning with Implementation
Ensemble Learning Techniques
Dimensionality Reduction
Advanced Optimization Techniques
Introduction to Data-Driven Decision Making
Open Source Tools for Data-Driven Decision Making
Deriving Insights from a Sales Dataset
The Power of Data Storytelling
Identifying Use Cases and Business Relevance
Crafting Compelling Narratives
Visualizing Data for Impact
Project Introduction and Problem Statement
Data Collection and Preparation
Data Analysis and Modeling
Data Storytelling and Presentation
Understanding AI Agents
Case Studies
Hands-On Practice with AI Agents
Job Roles & Industry Outlook
AI Data Scientist
Analyzes complex data, builds predictive models, uses statistical methods, and communicates findings to guide decisions.
AI Machine Learning Engineer
Designs ML systems, implements algorithms, optimizes data pipelines, and integrates models into production.
AI Engineer
Develops AI solutions, programs neural networks, optimizes algorithms, and supports ethical deployment.
AI Data Analyst
Interprets data, generates reports, identifies trends, and presents data with visualization tools.
Skills You’ll Gain
Tools You’ll Master
Exam Details
Duration
Passing Score
Format
Delivery Method
Exam Blueprint
What’s Included
(One-Year Subscription + All Updates)
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
