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Postgraduate Diploma in Business Analytics

Program Introduction

The Postgraduate Diploma in Business Analytics is a comprehensive program designed to immerse individuals in the world of data-driven decision-making within the realm of business. This diploma offers a structured curriculum that blends theoretical knowledge with practical applications, equipping students with the necessary skills to harness the power of data and transform it into actionable insights. Throughout the program, students explore a wide array of topics, including statistical analysis, predictive modeling, data mining, machine learning, and data visualization. Hands-on experience with industry-relevant tools and methodologies provides a practical understanding of how to extract, analyze, and interpret data to make informed business decisions. Ethical considerations and responsible data use are woven into the program, emphasizing the importance of privacy, security, and ethical implications in data analytics. Students learn to navigate these aspects while gaining a holistic understanding of the impact and responsibilities associated with utilizing data in business contexts. The program often includes industry collaborations, real-world projects, and case studies, allowing students to apply their skills to solve complex business problems. Graduates emerge with a deep understanding of the role of analytics in driving innovation, optimizing operations, and gaining a competitive edge across various industries. Overall, the Postgraduate Diploma in Business Analytics serves as a gateway for individuals seeking to become proficient in leveraging data to make strategic business decisions and drive organizational success in today's data-centric business landscape.

Program Mission

To equip individuals with practical analytics skills and ethical understanding, enabling them to make informed decisions and drive business growth.

Program Duration

This is a one (1) year program comprised of two (2) semesters.

Program Educational Objectives (PEOs)

Postgraduate Diploma in Business Analytics program aims to create a higher learning culture that enables students to:

  1. Proficient Analytical Skills Development: Acquire a solid foundation in analytical methodologies, statistical techniques, and data mining tools, enabling them to proficiently handle and analyze complex datasets to derive valuable insights for informed decision-making in diverse business contexts.
  2. Advanced Application of Analytical Techniques: Demonstrate advanced proficiency in applying statistical analysis, machine learning algorithms, and Python programming in the context of business analytics, empowering them to utilize sophisticated techniques for data-driven decision-making.
  3. Comprehensive Database Management Competence: Attain a comprehensive understanding of database management systems, enabling them to efficiently organize, manipulate, and extract insights from large datasets, contributing to effective data governance and utilization.
  4. Holistic Business Decision-Making Skills: Integrate knowledge from accounting, finance, decision science, and operations analytics, equipping them with the interdisciplinary skills needed to formulate strategic decisions and optimize business processes.
  5. Applied Problem-Solving Proficiency: Develop problem-solving skills grounded in decision science and operations analytics, enabling them to identify, analyze, and solve complex business problems by applying quantitative and analytical techniques.

Program Learning Objectives (PLOs)

No.

ATTRIBUTES

OUTCOMES

1

Data Analysis Proficiency

Demonstrate advanced skills in utilizing statistical methods and machine learning for insightful data analysis.

2

Business Decision-Making

Apply analytical models to solve real-world business problems and improve organizational performance.

3

Effective Data Communication

Present complex findings through visualization and reports for informed decision-making.

4

Technical Competence

Master programming languages/tools to manipulate, analyze, and visualize data effectively.

5

Ethical Data Practices

Understand and adhere to ethical and legal considerations in data collection, usage, and privacy.

6

Strategic Analytics Application

Build predictive models, prescribe actions, and optimize strategies using analytics solutions.

Post Graduate Diploma in Business Analytics Job Placements

Graduates of this program will be prepared for roles such as:

  • Data Analyst
  • Analytics Consultant
  • Policy Analyst (Government or NGOs)
  • Data Scientist
  • Business Intelligence Analyst

 

First Semester

Course Code

Course Title

Th

Lab

Cr. Hrs.

1

Foundations of Business Analytics

3

0

3

2

Ethics and Data Privacy in Business Analytics

3

0

3

3

Statistics for Business Analytics

3

0

3

4

Python Programming for Business Analytics

3

0

3

 

Total Credit Hours

12

 

Second Semester

Course Code

Course Title

Th

Lab

Cr. Hrs.

1

Data Analytics for Finance

3

0

3

2

Predictive Analytics and Modeling

3

0

3

3

Big Data Analytic

3

0

3

4

Business Intelligence Tools and Techniques

3

0

3

 

Total Credit Hours

12

 

Course Specifications

Course Title

Course Description

Recommended Readings

Foundations of Business Analytics

Introduction to fundamental concepts of business analytics, focusing on data analysis in decision-making.

**Data Science for Business** by Foster Provost, Tom Fawcett. 2013, O'Reilly Media.

Ethics and Data Privacy in Business Analytics

Covers ethical considerations and data privacy issues in business analytics, including data protection laws and ethical data use.

**Ethics of Big Data: Balancing Risk and Innovation** by Kord Davis. 2012, O'Reilly Media.

Statistics for Business Analytics

Focuses on statistical methods for business analytics, including descriptive statistics and regression analysis.

**Naked Statistics: Stripping the Dread from the Data** by Charles Wheelan. 2013, W. W. Norton & Company.

Python Programming for Business Analytics

Introduction to Python for data manipulation and analysis in business, using libraries like Pandas and NumPy.

**Python for Data Analysis** by Wes McKinney. 2017 (2nd Edition), O'Reilly Media.

Data Analytics for Finance

Explores data analytics applications in finance, including risk management and financial forecasting.

**Python for Finance: Mastering Data-Driven Finance** by Yves Hilpisch. 2018 (2nd Edition), O'Reilly Media.

Predictive Analytics and Modeling

Covers predictive analytics and modeling techniques for forecasting, including linear regression and decision trees.

**Data Mining for Business Analytics: Concepts, Techniques, and Applications in R** by Shmueli, Patel, Bruce. 2016 (3rd Edition), Wiley.

Big Data Analytics

Examines big data technologies and their business applications, addressing challenges and tools for big data analysis.

**Big Data: A Revolution That Will Transform How We Live, Work, and Think** by Viktor Mayer-Schönberger, Kenneth Cukier. 2013, Eamon Dolan/Houghton Mifflin Harcourt.

Business Intelligence Tools and Techniques

Introduces BI tools and techniques for data analysis and insight generation, including data warehousing and dashboard creation.

**Successful Business Intelligence: Unlock the Value of BI & Big Data** by Cindi Howson. 2013 (2nd Edition), McGraw-Hill Education.

16 Years of Education / Equivalent.

Open Merit Fee - 1st Semester

Fee HeadsFee (Rs)
Admission Fee (Once at admission) 5,000
Tuition Fee (Per Semester) 50,000
TOTAL 55,000

2nd Semester

Fee HeadsFee (Rs)
Tuition Fee (Per Semester) 50,000
TOTAL 50,000

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