About This Course
Skills You’ll Gain
Course Information
Course Code
AI+ Data™
Duration
40 hours of content
Delivery
Self Paced
Exam Voucher
Included
Course Curriculum
- Course Introduction
- 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
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All You Need to Know
Who Should Attend
- 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.
Prerequisites
- Basic knowledge of computer science and statistics (beneficial but not mandatory).
- Keen interest in data analysis.
- Willingness to learn programming languages such as Python and R.
Examination
50 questions, 70% passing, 90 minutes, online proctored exam
Frequently Asked Questions
Data analysts, scientists, and business intelligence professionals looking to apply AI for predictive modelling and decision-making.
Data science foundations, Python, statistics, data wrangling, generative AI, machine learning, and predictive analytics.
Yes — the course includes a capstone application solving real-world problems such as employee attrition prediction.
Some familiarity with data analysis is helpful, though the course builds up core data science foundations as well.
AI-driven data science roles, business intelligence positions, and data-focused strategy roles.