- This course introduces business students to AI-driven analytics techniques that can be applied to business decision-making processes. It covers key AI concepts such as predictive modeling, machine learning algorithms, and data visualization to enable students to make better decisions, predict future trends, and optimize business strategies.
- Level: Basic
- Prerequisites: Basic understanding of business analytics and data interpretation. No coding knowledge required.
- Assessments: Weekly Micro Assessments, Final Project-based Assessment.
- Job Roles Applicability: Business Analyst, Data Analyst, AI Consultant, Strategy Analyst.
Week 1 - Introduction to AI and Business Analytics
Learning OutcomeGain an overview of AI concepts and understand their relevance in business analytics.
1.1 What is AI and how it applies to business analytics.1.2 AI techniques and tools for business decision-making.
1.3 Understanding the difference between descriptive, predictive, and prescriptive analytics.
Practical Component
Showing the various forms of Data, and technologies out there for analytics.
Week 2 - Data Collection, Preparation, and Feature Engineering
Learning OutcomeLearn the importance of quality data in building AI models and the process of preparing data for analysis.
2.1 Data collection techniques and data quality.2.2 Data cleaning, preprocessing, and feature engineering.
2.3 Dealing with missing values and outliers in business data.
Practical Component
Depicting various data sets, their problems, and how to use them effectively with reasoning exercises for correctness understanding.
Week 3 - Understanding Predictive Analytics Models
Learning OutcomeUnderstand predictive modeling and how to use AI algorithms to forecast business outcomes.
3.1 Overview of machine learning algorithms (linear regression, decision trees, etc.).3.2 Building predictive models for sales forecasting, demand prediction, etc.
3.3Model evaluation and selection based on business needs.
Practical Componenet
Build a simple predictive model to forecast future sales using historical data.
Week 4 - Visualization of Business Data Insights Using AI
Learning OutcomeLearn how to visualize AI-driven insights to present clear business decisions.
4.1 Importance of data visualization in business analysis.4.2 Tools and techniques for visualizing AI insights.
4.3 Creating dashboards and reports for decision-makers.
Practical Componenet
Use a business analytics tool to create a dashboard for visualising predictive insights.
Week 5 - Implementing Prescriptive Analytics for Optimization
Learning OutcomeExplore how AI can optimize decision-making and business strategies using prescriptive analytics.
5.1 Introduction to optimization techniques in AI.5.2 Using AI to recommend actions that drive business improvement.
5.3 Case studies: AI for supply chain optimization, pricing strategies, and marketing.
Practical Component
Create an AI-based optimization model for inventory management or pricing strategies.
Week 6 - Live Presentation to Investors/Stake Holders
Learning OutcomeApply all AI and analytics techniques to solve a real-world business problem.
6.1 Group project to design a predictive analytics solution for a business case.6.2 Final presentations and peer reviews.
6.3 Instructor feedback and final evaluation.
Practical Component
Present a business decision-making scenario, including data collection, model creation, and decision outcomes.