Analysing Business Data with AI

Learning Outcome
Gain 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.

Learning Outcome
Learn 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.

Learning Outcome
Understand 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.

Learning Outcome
Learn 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.