AI+ Data™ – Course Outline
Program Overview
The AI+ Data™ certification is a foundational and practical training program designed to introduce learners to the essential concepts of data science and its integration with Artificial Intelligence (AI). The program focuses on how data is collected, processed, analyzed, and transformed into actionable insights that support decision-making in modern organizations.
Participants will explore core data science principles, including data lifecycle management, data preprocessing, statistical analysis, and machine learning fundamentals. The course emphasizes real-world applications of AI-powered data analysis in business, technology, finance, and operations.
Learners will gain hands-on insights into working with datasets, understanding data patterns, and using AI tools to extract meaningful insights. The program also highlights ethical considerations, data privacy, governance, and responsible data usage in AI-driven environments.
By the end of the program, participants will be able to understand data science fundamentals, apply basic analytical techniques, and leverage AI tools to support data-driven decision-making.
Course Objectives
• Understand foundational concepts of data science and Artificial Intelligence
• Explore the data science lifecycle and its key stages
• Learn how data is collected, cleaned, and processed
• Apply basic statistical and analytical techniques to datasets
• Understand machine learning applications in data analysis
• Develop skills in interpreting and visualizing data insights
• Explore AI tools used in data-driven decision-making
• Understand ethical considerations in data handling and privacy
• Improve problem-solving using data-driven approaches
• Build foundational skills for AI-powered data analysis workflows
Target Audience
• Beginners in data science and Artificial Intelligence
• Students and graduates from any discipline
• Business professionals working with data
• Analysts and aspiring data professionals
• IT professionals transitioning into data roles
• Finance, marketing, and operations professionals
• Entrepreneurs using data for decision-making
• Researchers and academics working with datasets
• Professionals seeking foundational AI and data knowledge
• Anyone interested in data science and analytics
Course Duration
• Instructor-Led: 5 days (live or virtual session)
• Self-Paced: 40 hours of structured learning content
Assessment
• Module-based quizzes on data science fundamentals
• Practical exercises on data handling and analysis
• Scenario-based data interpretation tasks
• Case study evaluations using real-world datasets
• Hands-on exercises in data visualization and insights
• Knowledge checks on ethics and data governance
• Final assessment or mini-project on data-driven analysis
Certification
Upon successful completion of all assessments and final evaluation, participants will be awarded the AI+ Data™ Certification.
This certification validates the learner’s foundational understanding of data science and demonstrates competency in applying AI-driven data analysis techniques for decision-making.
Training Methodology
• Instructor-led live or virtual training sessions
• Interactive lectures on data science and AI fundamentals
• Real-world examples of data-driven decision-making
• Hands-on practice with datasets and analytical tools
• Scenario-based learning for data interpretation
• Guided exercises on data analysis workflows
• Continuous assessments and quizzes
• Practical demonstrations of data visualization techniques
• Applied mini-projects using real-world datasets
Course Modules
Module 1: Foundations of Data Science
• Introduction to Data Science
• Data Science Life Cycle
• Applications of Data Science
Module 2: Foundations of Statistics
• Basic Concepts of Statistics
• Probability Theory
• Statistical Inference
Module 3: Data Sources and Types
• Types of Data
• Data Sources
• Data Storage Technologies
Module 4: Programming Skills for Data Science
• Introduction to Python for Data Science
• Introduction to R for Data Science
Module 5: Data Wrangling and Preprocessing
• Data Imputation Techniques
• Handling Outliers and Data Transformation
Module 6: Exploratory Data Analysis (EDA)
• Introduction to EDA
• Data Visualization
Module 7: Generative AI Tools for Deriving Insights
• Introduction to Generative AI Tools
• Applications of Generative AI
Module 8: Machine Learning
• Introduction to Supervised Learning Algorithms
• Introduction to Unsupervised Learning
• Different Algorithms for Clustering
• Association Rule Learning with Implementation
Module 9: Advance Machine Learning
• Ensemble Learning Techniques
• Dimensionality Reduction
• Advanced Optimization Techniques
Module 10: Data-Driven Decision-Making
• Introduction to Data-Driven Decision Making
• Open Source Tools for Data-Driven Decision Making
• Deriving Data-Driven Insights from Sales Dataset
Module 11: Data Storytelling
• Understanding the Power of Data Storytelling
• Identifying Use Cases and Business Relevance
• Crafting Compelling Narratives
• Visualizing Data for Impact
Module 12: Capstone Project – Employee Attrition Prediction
• Project Introduction and Problem Statement
• Data Collection and Preparation
• Data Analysis and Modeling
• Data Storytelling and Presentation
Optional Module: AI Agents for Data Analysis
• Understanding AI Agents
• Case Studies
• Hands-On Practice with AI Agents
