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Advanced Machine Learning (AML) - Data Management and Business Analytics

Learning Outcomes:

This lesson plan covers the key topics in Data Management and Business Analytics. By the end of this course, students will have a solid understanding of the subject matter and will have gained practical experience working with popular Machine Learning frameworks.

Course Objectives:

Understand the concepts of Data Marts, Data Warehouses, and Management Information Systems.
Gain knowledge about Business Analytics (descriptive, prescriptive, and proscriptive).
Learn the role of AI and Machine Learning models in Business Analytics.
Apply TensorFlow, PyTorch, and Scikit-Learn in practical applications.

Lesson 1: Introduction to Data Marts and Data Warehouses

Objectives:
Define Data Marts and Data Warehouses.
Understand their differences and use cases.
Lecture Topics:
Overview of Data Marts and Data Warehouses.
Differences between Data Marts and Data Warehouses.
Examples and use cases.
Activities:
Group discussion on real-world applications of Data Marts and Data Warehouses.
Resources:
Case studies and articles on Data Marts and Data Warehouses.

Lesson 2: Management Information Systems

Objectives:
Understand the role and importance of Management Information Systems.
Learn about the components and types of Management Information Systems.
Lecture Topics:
Overview of Management Information Systems.
Components and types of Management Information Systems.
Examples and use cases.
Activities:
Case study analysis on the implementation of Management Information Systems.
Resources:
Articles and case studies on Management Information Systems.

Lesson 3: Business Analytics: Descriptive, Prescriptive, and Proscriptive

Objectives:
Understand the different types of Business Analytics.
Learn the applications and benefits of each type.
Lecture Topics:
Overview of Business Analytics.
Descriptive, Prescriptive, and Proscriptive Analytics.
Examples and use cases.
Activities:
Group activity: Analyze a dataset and propose descriptive, prescriptive, and proscriptive solutions.
Resources:
Dataset for group activity and articles on Business Analytics.

Lesson 4: AI and Machine Learning in Business Analytics

Objectives:
Understand the role of AI and Machine Learning in Business Analytics.
Learn about popular Machine Learning frameworks.
Lecture Topics:
Overview of AI and Machine Learning in Business Analytics.
Introduction to TensorFlow, PyTorch, and Scikit-Learn.
Applications and examples.
Activities:
Hands-on tutorial: Introduction to TensorFlow, PyTorch, or Scikit-Learn.
Resources:
Tutorials, articles, and documentation on TensorFlow, PyTorch, and Scikit-Learn.

Lesson 5: Practical Applications and Project

Objectives:
Apply the knowledge gained throughout the course.
Develop a project that utilizes AML concepts and tools.
Lecture Topics:
Recap of course concepts.
Guidelines for the final project.
Activities:
Group project: Develop a solution using Data Marts, Data Warehouses, Management Information Systems, and Business Analytics.
Implement Machine Learning models using TensorFlow, PyTorch, or Scikit-Learn.
Resources:
Support materials and references for the final project.

Learning Outcomes:
This lesson plan covers the key topics in Data Management and Business Analytics. By the end of this course, students will have a solid understanding of the subject matter and will have gained practical experience working with popular Machine Learning frameworks.
Course Objectives:
Lesson 1: Introduction to Data Marts and Data Warehouses
Lesson 2: Management Information Systems
Lesson 3: Business Analytics: Descriptive, Prescriptive, and Proscriptive
Lesson 4: AI and Machine Learning in Business Analytics
Lesson 5: Practical Applications and Project
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