AI-Powered Data™

Your first step toward a promising career in AI-powered data™

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Certified by the Artificial Intelligence Institute

The importance of this certificate:

Demand for certified experts: Organizations are seeking certified professionals who can transform complex data into actionable insights while ensuring data integrity and privacy.


Risk reduction: The certificate helps prevent data analysis errors and the misuse of AI that could pose business risks.


AI-driven strategies: Certificate holders play a key role in designing intelligent data strategies that enhance performance while complying with regulatory standards.


Career advancement: The certificate provides a competitive edge to keep up with the growing demand for AI-powered data solutions and advance your career.

 
 

 

 

 

Quick Overview: Course and Exam

Program Name:

Artificial Intelligence and Data Science

Content:

Instructor-Led or Self-Paced Course + Official Exam + Digital Badge


Duration:

Instructor-Led: 5 Days (In-Person or Virtual)

Self-Paced: 40 Hours of Content


Prerequisites:

Basic knowledge of Computer Science and Statistics, Data Analysis, AI and Machine Learning concepts, Python, and R


Exam Format:

50 Questions, 70% Passing Score, 90 Minutes, Online with Proctoring


Mode of Learning:

Online Labs, Projects, and Case Studies


Outcome:

Industry-recognized certificate + hands-on experience

Who Should Enroll?

  • Data Analysts and Data Scientists: Enhance data analysis capabilities using AI for predictive modeling and decision-making.

  • Business Intelligence Specialists: Leverage AI to uncover insights, trends, and opportunities in complex datasets.

  • IT Experts and System Developers: Implement AI-powered solutions to enhance data management and infrastructure.

  • Data Engineers: Design and develop AI-driven data pipelines and systems for scalable solutions.

  • Students and Recent Graduates: Gain valuable skills in AI and Data Science to succeed in a data-driven world.

Job Roles and Industry Outlook

Data Analyst


Analyzes data, prepares reports, identifies trends, and supports business decisions by providing actionable insights, while using visualization tools to present data clearly.

Data Scientist


It analyzes complex data to extract insights, build predictive models, apply statistical methods, and present results to support decision-making.

Machine Learning Engineer


Designs and develops machine learning systems, implements algorithms, optimizes data pipelines, and integrates models into scalable, production-ready applications.

AI Engineer


Develops AI solutions, programs neural networks, optimizes AI algorithms, ensures ethical AI deployment, and troubleshoots issues in AI systems.

Skills You Will Acquire

Data Visualization Techniques 

Data Quality and Bias Mitigation 

Deep Learning for Data Processing   

Statistical Modeling  

Big Data Techniques  

Prerequisites

  • Basic knowledge of computer science and statistics (helpful but not mandatory).

  • Strong interest in data analysis.

  • Willingness to learn programming languages such as Python and R.

Exam Details

  • Duration: 90 minutes

  • Passing Score: 70% (35 out of 50)

  • Question Format: 50 Multiple-Choice / Multiple-Answer Questions

  • How to Apply: Online via a proctored exam platform (flexible scheduling).

Exam Blueprint

  • Data Science Fundamentals – 5%

  • Statistics Fundamentals – 5%

  • Data Sources and Types – 6%

  • Data Science Programming Skills – 10%

  • Data Cleaning and Preparation – 10%

  • Exploratory Data Analysis – 12%

  • Generative AI Tools for Insight Extraction – 6%

  • Machine Learning – 10%

  • Advanced Machine Learning – 10%

  • Data-Driven Decision Making – 10%

  • Data Storytelling – 6%

  • Capstone Project: Employee Attrition Prediction – 10%

Choose the Format That Fits Your Schedule
What the subscription includes (1 year + all updates)

  • High-Quality Videos, eBook (PDF & Audio), and Podcasts

  • AI mentor for personalized guidance

  • Quizzes, Assessments, and Course Resources

  • Online exam with one free retry allowed

  • Comprehensive exam study guide

  • Access via tablet and mobile devices

 

 

 
 
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Self-paced Online Learning

40 hours of on-demand video lessons, an e-book, podcasts, and interactive labs. Learn anytime, anywhere, with quizzes to track your progress.

Purchase the self-paced online course

Instructor-led training (live virtual or in-class)

5 days of intensive training with live demonstrations Real-time Q&A sessions, peer collaboration, and hands-on labs Training is delivered by AI-certified instructors through authorized training partners

Purchase the self-paced online course

Tools You Will Explore

Frequently Asked Questions (FAQ)

The AI+ Data™ certificate includes the fundamentals of data science, statistics, programming, and data preparation, along with advanced topics such as generative AI and machine learning.

 

This certificate equips participants with the tools and skills necessary to handle complex data challenges, such as data cleaning, transformation, and analysis.

Graduates of the AI+ Data™ certificate program can pursue roles such as Data Scientist, Machine Learning Engineer, Data Analyst, AI Consultant, and other data-driven positions.

 
Participants will gain skills in data analysis, machine learning, data visualization, data preparation, and predictive analytics, in addition to mastering the programming languages Python and R.
 
 
 
 
 

Yes, the AI+ Data™ certificate is designed to be flexible, and the course can be taken while working full-time. All course materials are available online.

What Will You Learn?

Course Introduction

 
 

  • 1.1 Introduction to Data Science

  • 1.2 Data Science Life Cycle

  • 1.3 Applications of Data Science

 

  • 2.1 Basic Concepts in Statistics

  • 2.2 Probability Theory

  • 2.3 Statistical Inference

 

 
 
 
 
 

  • 3.1 Types of Data

  • 3.2 Data Sources

  • 3.3 Data Storage Technologies

 

 
 
 
 

  • 4.1 Introduction to Python for Data Science

  • 4.2 Introduction to R for Data Science

 
 
 

  • 5.1 Data Imputation Techniques

  • 5.2 Handling Outliers and Data Transformation

 

 
 
 

  • 6.1 Introduction to Exploratory Data Analysis (EDA)

  • 6.2 Data Visualization

 
 
 

  • 7.1 Introduction to Generative AI Tools

  • 7.2 Applications of Generative AI

  • 8.1 Introduction to Supervised Learning Algorithms

  • 8.2 Introduction to Unsupervised Learning

  • 8.3 Different Clustering Algorithms

  • 8.4 Association Rule Learning with Practical Implementation9.1 Ensemble Learning Techniques
    9.2 Dimensionality Reduction
    9.3 Advanced Optimization Techniques

 
 
 
 
 
 

9.1 Ensemble Learning Techniques
9.2 Dimensionality Reduction
9.3 Advanced Optimization Techniques

10.1 Introduction to Data-Driven Decision Making
10.2 Open-Source Tools for Data-Driven Decision Making
10.3 Data-Driven Insights Extraction from a Sales Dataset

 

11.1 Understanding the Power of Data Storytelling
11.2 Identifying Use Cases and Business Significance
11.3 Crafting Engaging and Effective Stories
11.4 Data Visualization for Impact

12.1 Project Introduction and Problem Statement
12.2 Data Collection and Preparation
12.3 Data Analysis and Model Building
12.4 Data Storytelling and Results Presentation

 

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

 

Start your future now

Embark on the World of Data with AI™ — Take the Step That Will Transform Your Career.

Enroll Now