Cambridge
Academy
In London

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 Outlines

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: 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