MBA in Data Science syllabus and subjects is unique as it blends business management with analytical skills. These programs focus on giving a holistic understanding of data science and building cross-functional management skills among students. Important MBA Data Science subjects are business analytics, artificial intelligence, machine learning, etc. MBA in Data Science includes both core and elective subjects. In the first year of an MBA in Data Science, students learn the core subjects, while in the second year, they move towards specialized and elective subjects.

Semester Wise MBA in Data Science Syllabus 

The course is divided into four semesters over two years. Students study core subjects in the first year and then move to elective subjects according to their interest in the second year. The course also focuses on practical learning through projects and internships. Listed below is the semester wise distribution of the syllabus:

MBA Data Science 1st Year Syllabus
Semester I Semester II
Business Communication Corporate Finance
Managerial Economics Human Resource Management
Principles of Management Operations Management
Marketing Management Statistical Modelling
Organizational Behaviour Data Visualisation
Statistics for Business: Decision Science I Spreadsheet Modelling II
Spreadsheet Modelling I Introduction to Machine Learning
Analytics toolkit: Decision Science II Entrepreneurial Development
MBA Data Science 2nd Year Syllabus
Semester Ⅲ Semester Ⅳ
Advanced Machine Learning Applications of Deep Learning
Natural Language Processing Applications of Natural Language
Web & Social Media Analytics Supply Chain Analytics
Finance and Risk Analytics HR Analytics
Healthcare Analytics Marketing Analytics
Deep Learning I Project Management

MBA in Data Science Subjects

MBA in Data Science includes two types of subjects-one Core and the other elective subjects. Along with this internship and project submission is there. Also, the learning in MBA Data Science is done through group discussions and presentations prepared by the students. Listed below are the core and the elective subjects:

Core Subjects:

  • Business Economics & Market Sensing
  • Marketing Science and Digital Practice
  • Communication Competencies for Leadership
  • Problem Solving & Decision Making
  • Business Finance & Accounting
  • Managing Human Capital & Organizational Behaviour
  • Industry Applications in Data Science

Elective Subjects:

  • Web & Social Media Analytics
  • Finance and Risk Analytics
  • Healthcare Analytics
  • Deep Learning I
  • HR Analytics
  • Project Management
  • Supply Chain Analytics

MBA Data Science Course Structure

MBA Data Science syllabus focuses on building holistic learning of data science. In the first year, subjects are generic subjects studied in all MBA courses. While in the second year subjects become specialized to the particular field and also some subjects become elective. In this way, students can choose the subjects which are of interest to them. The course structure is a mix of theoretical knowledge along with practical use of this knowledge through projects, research papers, group discussions, and also internships. The course structure includes:

  • Core and Elective Subjects
  • Ⅳ Semesters
  • Projects
  • Research Papers
  • Surveys
  • Seminars
  • Practicals
  • Thesis Writing

MBA Data Science Teaching Methodology and Techniques

The teaching methodology can be very different for MBA Data Science. It involves a mix of classroom teaching along with the real-world application of this knowledge through case study learning. This teaching methodology help in building a comprehensive understanding of Data Science. Through this methodology, students can understand the world of artificial intelligence, machine learning, etc. Some methodology techniques used by colleges are:

  • Discussions 
  • Field trips
  • Practical learnings
  • Problem-based 
  • Projects
  • E-learning
  • Co-curricular activities

MBA Data Science Projects

Projects in data science can be about various topics related to artificial intelligence, machine learning, etc. These projects help in boosting the confidence of students by applying the practical use of their learnings and help them in making better leaders and decision-makers. These projects can be of many types as the application of data science is in many fields. Some popular projects are:

  • Fake News Detection
  • Sentiment Analysis
  • Building Chatbots
  • Credit Card Fraud Detection
  • Speech Emotion Recognition
  • Forest Fire Prediction
  • Classifying Breast Cancer
  • Driver Drowsiness Detection

MBA Data Science Reference Books

Listed below are some popular reference books used in MBA Data Science courses:

MBA Data Science Books
Books Author
The Signal and the Noise Nate Silver
Business Research Methods Donald R. Cooper
Introduction to Machine Learning with Python: A Guide for Data Scientists Andreas C. Müller
R for Data Science Hadley Wickham
Big Data MBA: Driving Business Strategies with Data Science Bill Schmarzo
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Tom Fawcett

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