Focuses on data analytics, statistics, machine learning, and business intelligence to equip students with skills for data-driven decision-making.
The syllabus for the Integrated Program in Business Analytics (IPBA) is designed to equip students with advanced skills in data analysis and business intelligence. Students typically cover core topics in data science, statistics, and data visualization. The curriculum includes courses on machine learning, predictive analytics, and big data analytics. Students also delve into topics related to business strategy, data-driven decision-making, and data ethics. Practical experiences such as data analytics projects, real-world data analysis, and data visualization projects may be included to help students apply their skills in real-world scenarios. Graduates are well-prepared for careers in data analytics, business intelligence, and data-driven decision support roles.
Integrated Program In Business Analytics (IPBA)Course Syllabus & Subjects
Sno. | Subjects |
1 | Introduction to Business Analytics |
2 | Statistics and Probability for Analytics |
3 | Data Analysis and Visualization |
4 | Programming for Data Science (Python/R) |
5 | Business Communication |
6 | Managerial Economics |
7 | Machine Learning for Business Analytics |
8 | Big Data Technologies and Tools |
9 | Database Management Systems |
10 | Marketing Analytics |
11 | Financial Analytics |
12 | Predictive Analytics |
13 | Operations Analytics |
14 | Supply Chain Analytics |
15 | Decision Models and Optimization |
Project Work |
Let's look at some of the most creative Integrated Program In Business Analytics (IPBA) project ideas that are suitable for both experts and newcomers:
(i). Sales Prediction Risk Evaluation.
(ii). Detecting fraud.
(iii). Product Suggestions.
(iv). Inventory Control.
(v). Supply Chain Management.
(vi). Optimizing Marketing Campaigns.
(vii). Analysis of employee performance.
(viii). Monetary Modeling.
Integrated Program In Business Analytics (IPBA) books provide students with both a thorough general review of the subject matter and a close examination of their specific area of expertise. The following are some of the books for reference:
Name of Author | Name of Book |
Abhijit Dasgupta | Data Science for Business with Python |
Sudipto Banerjee | Business Analytics with R |
Prof. G.S. Raghavan | Introduction to Business Analytics |
Prof. V.S.R. Murthy | Business Analytics: Text and Cases |
Prof. S.P. Singh | Data Analytics: Concepts, Techniques, and Applications |
Alok R. Gupta | Business Analytics: Concepts and Applications |
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