AI in Data Science & Machine Learning for Analystsmanagement Analysis & Operational Auditing

In any city around the world 00447455203759 Course Code: AI/2025/1004

Course Description

Introduction

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

Objectives

- 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.

Audience

- Aspiring data scientists

- Data analysts

- Business analysts

- Non-technical managers interested in AI analytics

5-Day 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