Data-Driven Decision Making with AI and ML

Introduction

This course empowers professionals to leverage AI and machine learning for data-driven decision making in modern business contexts.

Objectives

• Understand the fundamentals of data-driven decision making.
• Gain foundational knowledge of AI and machine learning concepts.
• Apply AI/ML tools to analyze and interpret data.
• Develop actionable insights from complex datasets.
• Make informed decisions using AI-powered analytics.

Course Outlines

Day 1: Foundations of Data-Driven Decision Making

• Introduction to data-driven decision making
• Key principles and benefits in business
• Types of data and their sources
• Challenges in data collection and management
• Overview of analytics maturity models

Day 2: Introduction to AI and Machine Learning

• Defining AI and machine learning
• Core concepts and terminology
• Types of machine learning: supervised vs unsupervised
• Common AI/ML applications for business
• Ethics and considerations in AI/ML adoption

Day 3: Data Analysis Techniques and Tools

• Preparing and cleaning data for analysis
• Exploratory data analysis with AI/ML tools
• Introduction to popular platforms (e.g., Python, Excel, cloud tools)
• Visualizing data for actionable insights
• Case studies in real-world data analysis

Day 4: Building and Interpreting AI/ML Models

• Selecting appropriate AI/ML models
• Training and validating models
• Evaluating performance with key metrics
• Interpreting AI/ML outputs for decision making
• Hands-on model building exercise

Day 5: Driving Business Decisions with AI and ML

• Integrating AI/ML insights into business processes
• Developing data-driven strategies
• Presenting findings to stakeholders
• Change management and fostering adoption
• Final project: Solving a business challenge using AI/ML