SSIM is the only institute offering the unique program PGDM – Business Analytics. Business Analytics has come to play a pivotal role in all the sectors. It uses the data to offer meaningful insights and uses this information to make companies competitive. Analytics is more than just Analytical Methodologies or techniques used in logical Analysis. Also, PGDM Business Analytics tools can help companies scale up the efficiency of their operations and improve Business decisions. It makes the decision-making process more accurate as it helps in understanding the sentiments of the customers towards the company, its brand, and products. With efficiency in decision making, the organization can stay on top of their competitors. This is one of the most useful advantages of PGDM Business Analytics. If we refer to various industry studies, PGDM Business Analytics has emerged as one of the most lucrative career options in terms of salary and growth. In fact, 'Data Scientist' has consistently featured in Glassdoor's “25 Best Jobs” for the past couple of years. As a part of this, students will be undergoing few fundamental courses and then pursue detailed and accurate learning in the areas of PGDM Business Analytics. This course focus on each function that how this can be applied in different industries and sectors in each domain. Students will be focusing on Marketing Analytics, Financial Analytics, HR Analytics, Operational Analytics etc.
1. To gain an understanding of how managers use PGDM business analytics to formulate and solve business problems and to support managerial decision making.
2. To become familiar with the processes needed to develop, report, and analyze business data.
3. To learn how to use and apply Excel and Excel add-ins to solve business problems.
4. Interpret data using latest data analytics tools to address organisational problems.
5. Assess decision problems and build models for creating solutions using business analytical tools
1. Business Statistics (Descriptive)
2. Introduction to Python & R Programming
3. Machine Learning ( Pattern Classification, SVM, Bayesian Decision Theory, Clustering)
4. Artificial Intelligence ( Neural Networks, Deep Learning & Speech Recognition)
5. SQL & Data Mining
6. Data Visualization (Tableau)
7. Predictive Analytics (Using R & Python)
8. Big Data (Hadoop/ Spark)
9. Business Forecasting & Time Series
10. Natural Language Processing, Text, Web & Social Media Analytics
11. Marketing Analytics (Sales, Retails & Pricing)
12. Database Management
13. Operations Analytics
14. Performance and Compensation Analytics
15. HR Metrics and Analytics
16. Cognitive Technologies
17. Multivariate Data Analysis
18. Reinforcement Learning
19. Venture analytics
20. Product analytics
21. Financial Analytics (Algorithmic Trading)
22. Stochastic Processes and Applications
23. Advanced Spreadsheet Modelling
24. Sports Analytics
25. Fraud and Risk Analytics
26. Business Accounting and Banking Analytics