MBA in Data Analytics Program Syllabus & Subjects

Dive into the detailed syllabus of the MBA program in Data Analytics. From data mining to predictive modeling, explore the subjects that will prepare you for a career in data-driven decision-making and analytics.

MBA Data Analytics Syllabus:

The MBA in Data Analytics syllabus focuses on harnessing the power of data for informed decision-making. Core courses cover statistical analysis, data visualization, and machine learning. Specialized subjects include big data analytics, predictive modeling, and data-driven business strategy. Students often work on data projects and collaborate with industry partners. Graduates are well-equipped for roles in data analysis, business intelligence, and data science.

Semester 1	Semester 2
Data Analytics Foundations	Machine Learning for Data Analytics
Statistical Methods for Business Analytics	Big Data Analytics and Management
Managerial Economics	Operations Research for Data Analytics
Financial Accounting for Analytics	Marketing Analytics
Organizational Behavior and Leadership	Data Visualization and Communication
Business Communication Skills	Database Management and SQL
Business Analytics Capstone Project (Part 1)	Business Analytics Capstone Project (Part 2)
Semester 3	Semester 4
Predictive Analytics and Modeling	Text Analytics and Natural Language Processing
Data Mining Techniques	Data Governance and Ethics
Supply Chain Analytics	Customer Analytics and Relationship Management
Financial Analytics	Social Media Analytics
Strategic Management for Analytics	Strategic Analytics and Decision Making
Business Analytics Capstone Project (Part 3)	Business Analytics Capstone Project (Part 4)

Projects

Throughout the MBA in Data Analytics program, students are required to work on various projects to apply their skills and knowledge in real-world scenarios. These projects aim to enhance their analytical abilities, problem-solving skills, and decision-making capabilities.

The projects can involve tasks such as data analysis, data visualization, predictive modeling, and business strategy formulation based on data insights. Students typically work individually or in teams to complete these projects, which may culminate in a final capstone project where they showcase their skills and present their findings to faculty and industry experts.

Reference Books

(1). "Data Science for Business" by Foster Provost and Tom Fawcett

(2). "Python for Data Analysis" by Wes McKinney

(3). "R for Data Science" by Hadley Wickham and Garrett Grolemund

(4). "Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier

(5). "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" by Eric Siegel