Introduction

Data science is the foundation of modern analytics. This course introduces AI-powered machine learning techniques tailored for analysts without heavy coding requirements.

Course Objectives

By the end of this course, participants will be able to:

• Understand AI’s role in data science.
• Apply machine learning algorithms for analysis.
• Use AI tools without deep programming.
• Build predictive models for real-world problems.

Target Audience

This course is designed for:

• Aspiring data scientists
• Data analysts
• Business analysts
• Non-technical managers interested in AI analytics

Course Outline

Day 1: AI & Data Science Fundamentals

• AI in the data science workflow
• Supervised vs. unsupervised learning
• Tools: RapidMiner, DataRobot, Google AutoML
• Case study: AI data science success stories
• Workshop: Data science with AutoML

Day 2: Machine Learning for Analysts

• Regression and classification models
• Clustering and segmentation
• Case study: AI in customer segmentation
• Workshop: Build ML model with AI tools
• Peer review

Day 3: Feature Engineering with AI

• AI-driven feature selection & extraction
• Reducing dimensionality with AI
• Demo: Automated feature engineering tool
• Exercise: AI feature selection activity
• Feedback session

Day 4: Predictive Modeling with AI

• AI in predictive model building
• Validation and accuracy measures
• Group simulation: Predictive model building
• Peer collaboration
• Expert feedback

Day 5: Deploying AI Models for Analysts

• AI deployment strategies
• Integrating AI models into BI & reporting systems
• Group project: Analyst-focused AI model
• Presentations & review
• Wrap-up & certification