2 Years
Post Graduate
Management
Full Time
The MBA (Data Analytics) syllabus typically includes a blend of core business administration subjects and specialized data analytics courses. Core subjects often cover strategic management, financial accounting, marketing management, and organizational behavior to provide a comprehensive business foundation. The specialized data analytics component delves into data mining, predictive modeling, machine learning, statistical analysis, and big data technologies. Students also learn about data visualization, database management, and business intelligence tools. Emphasis is placed on practical applications, with courses on data-driven decision making, and often includes hands-on projects, case studies, and internships to develop practical skills in analyzing and interpreting complex data to inform business strategies.
The MBA in Data Analytics typically spans four semesters. In the first semester, core management subjects like Accounting, Marketing, and Organizational Behavior are covered. The second semester introduces Data Analytics fundamentals, including Statistical Analysis and Data Mining. The third semester delves deeper into Big Data Technologies, Predictive Analytics, and Machine Learning. The final semester focuses on advanced topics such as Data Visualization, Business Intelligence, and a capstone project or internship to apply practical skills.
| Course Title | Description |
|---|---|
| Principles of Management | Introduction to the fundamental concepts, functions, and techniques of management. |
| Business Statistics | Study of statistical methods and their applications in business decisions, including descriptive and inferential statistics. |
| Financial Accounting | Understanding financial statements, accounting principles, and financial reporting. |
| Data Analytics Foundations | Basics of data analysis, including data collection, cleaning, and initial analysis techniques. |
| Quantitative Techniques | Application of mathematical models and statistical methods to solve business problems. |
| Business Communication | Effective business communication skills including writing, presentations, and interpersonal communication. |
| Information Systems in Business | Overview of information systems in organizations, including the role of IT in business processes and decision-making. |
| Marketing Management | Fundamental concepts of marketing, including market analysis, strategy development, and consumer behavior. |
| Programming for Data Analytics | Introduction to programming languages (such as Python or R) used in data analytics. |
| Research Methodology | Techniques and methods for conducting research in business and data analytics. |
| Course Title | Description |
|---|---|
| Advanced Data Mining Techniques | Advanced techniques for extracting insights and patterns from large datasets. |
| Statistical Analysis | Application of statistical methods in analyzing and interpreting data. |
| Machine Learning Applications | Practical applications of machine learning algorithms in various business scenarios. |
| Big Data Technologies | Exploration of technologies used to handle and process big data efficiently. |
| Data Visualization | Techniques for visually representing data to facilitate understanding and decision-making. |
| Business Intelligence | Introduction to BI tools and their application in deriving actionable insights from data. |
| Research Methodology | Methods and techniques for conducting research in the field of data analytics. |
| Project Management | Principles and practices of project management with a focus on data analytics projects. |
| Course | Description |
|---|---|
| Project Management Software | Study and application of various project management tools and software like MS Project, Primavera, etc. |
| Big Data Analytics | Concepts and techniques related to big data processing, including Hadoop, MapReduce, Pig, Hive, and applications of big data in real-world scenarios. |
| Machine Learning | Introduction to machine learning algorithms, supervised and unsupervised learning, neural networks, and applications in business. |
| Business Intelligence | Frameworks and tools for business intelligence, data warehousing, business performance management, and data mining techniques. |
| Data Visualization Lab Sessions | Practical sessions focused on data visualization tools like Tableau, Power BI, and creating impactful visual data stories. |
| Elective I | Varies based on interest; options can include courses like Advanced Predictive Analytics, Natural Language Processing, etc. |
| Elective II | Continuation of elective studies, allowing further specialization in areas such as Deep Learning, Advanced Statistical Methods, etc. |
| Project Work I/Viva | Hands-on project work applying data analytics techniques to solve real-world business problems, culminating in a viva examination. |
| Course Title | Topics Covered |
|---|---|
| Advanced Data Mining Techniques | Advanced techniques in data preprocessing, classification, clustering, association rule mining, anomaly detection, pattern mining |
| Big Data Analytics | Hadoop ecosystem, MapReduce, Spark, NoSQL databases, data warehousing, real-time analytics, big data tools and technologies |
| Machine Learning Applications | Supervised learning, unsupervised learning, neural networks, deep learning, reinforcement learning, applications of machine learning in business |
| Data Visualization and Reporting | Principles of data visualization, data storytelling, visualization tools (Tableau, Power BI), dashboard design, best practices in reporting and presentation |
| Predictive Analytics | Predictive modeling, regression analysis, time series forecasting, decision trees, ensemble methods, model evaluation and validation |
| Strategic Management | Strategic analysis, strategic planning, competitive strategy, strategic implementation, organizational change management, case studies |
| Capstone Project | Real-world data analytics project, project management, problem-solving, critical thinking, presentation skills, professional communication |
| Elective Course 1 | Specialized topics in data analytics such as text mining, sentiment analysis, web analytics, etc. |
| Elective Course 2 | Another elective course chosen from a selection of topics like healthcare analytics, financial analytics, marketing analytics, etc. |
| Subject | Topics Covered |
|---|---|
| Quantitative Aptitude | Arithmetic (Percentage, Profit and Loss, Ratio and Proportion, Time and Work, Time-Speed-Distance), Algebra, Geometry, Trigonometry, Mensuration, Probability, Permutations and Combinations |
| Data Interpretation and Logical Reasoning | Data Tables, Pie Charts, Bar Graphs, Line Graphs, Caselets, Data Sufficiency, Logical Reasoning (Syllogisms, Blood Relations, Coding-Decoding, Seating Arrangements, Direction Sense) |
| Verbal Ability and Reading Comprehension | Vocabulary, Grammar, Sentence Correction, Reading Comprehension (Passages from various domains like Business, Economics, Science, Technology) |
| Data Analytics Concepts | Basics of Statistics, Descriptive and Inferential Statistics, Probability Distributions, Hypothesis Testing, Regression Analysis, Time Series Analysis, Data Mining Techniques, Machine Learning Concepts |
| Business Awareness | Current Affairs, Business News, Economic Indicators, Company Profiles, Industry Trends, Government Policies, International Trade, Financial Markets |
| Book Title | Author(s) | Publisher |
|---|---|---|
| "Data Science for Business" | Foster Provost, Tom Fawcett | O'Reilly Media |
| "Data Mining for Business Analytics" | Galit Shmueli, Peter C. Bruce, et al. | Wiley |
| "Big Data: Principles and Best Practices" | Jules J. Berman | Academic Press |
| "Business Intelligence Guidebook" | Rick Sherman | Morgan Kaufmann |
| "Python for Data Analysis" | Wes McKinney | O'Reilly Media |
| "Competing on Analytics: Updated, with a New Introduction" | Thomas H. Davenport, Jeanne G. Harris | Harvard Business Review Press |
| "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" | Eric Siegel | Wiley |
| "The Analytics Edge" | Dimitris Bertsimas, Allison O'Hair | Dynamic Ideas LLC |
| "Practical Statistics for Data Scientists" | Peter Bruce, Andrew Bruce, Peter Gedeck | O'Reilly Media |
| "Storytelling with Data" | Cole Nussbaumer Knaflic | Wiley |
Q. What is the structure of the MBA (Data Analytics) program?
Ans. The MBA in Data Analytics program typically spans over two years and is divided into multiple semesters. Each semester covers various core and elective courses related to data analytics, business management, and specialized topics in analytics.
Q. What are the core courses included in the syllabus?
Ans. Core courses often include subjects like Data Mining, Business Intelligence, Predictive Analytics, Data Visualization, Statistical Analysis, Machine Learning, Big Data Management, and Business Analytics Strategy.
Q. Are there any prerequisites for the program?
Ans. While specific prerequisites may vary between institutions, a strong foundation in mathematics, statistics, and programming is often recommended. Some programs may require applicants to have prior work experience in analytics or related fields.
Q. What elective courses can students choose from?
Ans. Elective courses allow students to tailor their MBA experience to their interests and career goals. Common elective topics include Marketing Analytics, Financial Analytics, Healthcare Analytics, Supply Chain Analytics, and Social Media Analytics.
Q. How is the program balanced between theory and practical application?
Ans. The program typically integrates theoretical knowledge with hands-on experience through case studies, projects, internships, and practical exercises. Students may work with real-world datasets and analytical tools to solve business problems and gain practical skills.
Q. Does the syllabus cover software and tools used in the industry?
Ans. Yes, the syllabus often includes training in popular analytics software and tools such as R, Python, SQL, Tableau, SAS, and Hadoop. Students learn how to use these tools for data analysis, visualization, and management.
Q. Are there opportunities for specialization within the program?
Ans. Some MBA programs offer specializations or concentrations in specific areas of data analytics, such as Marketing Analytics, Financial Analytics, or Healthcare Analytics. Students can choose elective courses aligned with their specialization interests.
Q. How are students assessed throughout the program?
Ans. Assessment methods may include exams, quizzes, projects, presentations, case analyses, and group assignments. The evaluation criteria often emphasize critical thinking, problem-solving abilities, and the application of analytics concepts to real-world scenarios.
Q. Is there a capstone project or thesis requirement?
Ans. Many MBA programs culminate in a capstone project or thesis, where students apply their analytical skills to solve a significant business problem or conduct research on a relevant topic. This project allows students to demonstrate their mastery of data analytics concepts and techniques.
Q. What career opportunities are available to graduates of the program?
Ans. Graduates of an MBA in Data Analytics program are well-equipped to pursue diverse career paths in industries such as consulting, finance, healthcare, technology, retail, and manufacturing. Potential roles include Data Analyst, Business Intelligence Analyst, Data Scientist, Analytics Manager, and Strategy Consultant.
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