AI+ Data™
AT-120
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
Why This Certification Matters
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Job Roles & Industry Outlook
Data Science & AI Specialist
Turn complex datasets into actionable insights by applying AI and machine learning models to solve business challenges.
Data Scientist
Analyze complex data and develop AI-driven models to uncover insights, optimize solutions, and drive innovation.
AI Systems Engineer
Design and optimize data pipelines, warehouses, and systems to enable AI-driven analytics and applications.
Statistician
Apply AI-driven statistical models to analyze data, uncover trends, and support evidence-based decision-making.
Business Intelligence Analyst
Transform data into actionable insights by applying AI-driven analytics to guide smarter business decisions.
Healthcare Innovator
Leverage AI for predictive analytics and patient data insights to enhance clinical decisions and improve healthcare outcomes.
Industry Growth: AI+ Data™
- Demand for Certified Experts: Organizations seek certified experts who can transform complex data into actionable insights while ensuring data integrity and privacy.
- Mitigating Data and AI Risks: Poor handling of data and AI technologies can lead to inaccurate analysis and business risks. This certification helps professionals mitigate such challenges.
- Designing AI-Driven Data Strategies: Certified professionals play a crucial role in designing AI-driven data strategies that optimize performance and align with regulatory standards.
- Career Advancement: As AI-powered data solutions become essential for businesses, this certification provides professionals with a competitive edge in advancing their careers.
Who Should Enroll?
- Data Analysts & Scientists: Enhance data analysis capabilities using AI for predictive modeling and decision-making.
- Business Intelligence Professionals: Leverage AI to uncover insights, trends, and opportunities in complex data sets.
- IT Specialists & System Integrators: Implement AI-powered solutions to optimize data management and infrastructure.
- Data Engineers: Design and develop AI-driven data pipelines and architectures for scalable solutions.
- Students & New Graduates: Build valuable AI and data science skills to thrive in an increasingly data-driven world.
What You'll Learn
- Course Introduction Preview
- 1.1 Introduction to Data Science
- 1.2 Data Science Life Cycle
- 1.3 Applications of Data Science
- 2.1 Basic Concepts of 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 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 Algorithms for Clustering
- 8.4 Association Rule Learning with Implementation
- 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 Deriving Data-Driven Insights from Sales Dataset
- 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
- 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
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
Tools You'll Explore
Google Colab
MLflow
Alteryx
KNIME
Prerequisites
- Basic knowledge of computer science and statistics, data analysis, fundamental AI/ML concepts, Python and R.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Foundations of Data Science - 7%
- Module 2: Foundations of Statistics - 7%
- Module 3: Data Sources and Types - 7%
- Module 4: Programming Skills for Data Science - 7%
- Module 5: Data Wrangling and Preprocessing - 8%
- Module 6: Exploratory Data Analysis (EDA) - 8%
- Module 7: Generative AI Tools for Deriving Insights - 8%
- Module 8: Machine Learning - 8%
- Module 9: Advance Machine Learning - 8%
- Module 10: Data-Driven Decision-Making - 8%
- Module 11: Data Storytelling - 8%
- Module 12: Capstone Project - Employee Attrition Prediction - 8%
- Optional Module: AI Agents for Data Analysis - 8%
Choose the Format That Fits Your Schedule
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
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.