B.Sc. Applied Statistics and Data Analytics (Hons)
Programme Overview
B.Sc. Applied Statistics and Data Analytics (Hons) is an interdisciplinary undergraduate programme combining the elements of Computer Science, Data Analytics, Machine Learning, with Statistics. It prepares professionals with the skills to collect, analyse, and interpret Data for informed decisions.
The programme opens doors to a wide array of career opportunities in industries increasingly reliant on data expertise. As technology continues to advance, the demand for professionals adept at navigating and interpreting complex data sets is rapidly rising. The B.Sc. Applied Statistics and Data Analytics programme remains highly relevant, preparing graduates to meet the evolving needs of data-centric industries.
Major Tracks
- Descriptive Statistics
- Machine Learning
- Numerical Methods
- Regression Analysis
- Data Mining
Duration & Fees
Duration
4 Years
Last Date to Apply : 06 April 2026
Fee Per Year
₹ 110,000
Scholarship
| Scholarship for AY 2026-27 | 10th Aggregate Score | 12th Aggregate Score |
|---|---|---|
|
Dr. Vishwanath Karad Scholarship |
90& Above |
85& Above |
|
MIT-WPU Scholarship I |
88 & Above |
83 & Above |
|
MIT-WPU Scholarship II |
85 & Above |
80 & Above |
Note: Scholarships will be awarded based on the Class 10/SSC and Class 12/HSC score.
Terms & Conditions Apply:
- All Scholarships are awarded on a First Come First Serve basis. All Scholarships are awarded as fee adjustments.
- To continue the scholarship for the entire duration of the programme, a minimum level of the academic score has to be maintained at an 8 CGPA across all semesters, attendance is to be maintained at a minimum of 80 percent, with no live backlogs in any subject/programme and no semester break, and there should be no disciplinary action against the student.
For more detailed information visit our website: https://mitwpu.edu.in/scholarships
Eligibility
Minimum 50% aggregate marks in Class 10+2/ HSC or its equivalent examination with Mathematics or Mathematics & Statistics subject (Minimum 45% aggregate marks for candidates belonging to the Reserved Category from Maharashtra State).
OR
Minimum 60% aggregate marks in any 3 years Engineering Diploma from State Government approved Institution/ board.
Selection Process
Admission will be based on the Personal Interaction (PI) score, conducted by the University according to the prescribed schedule
Programme Highlights
- Hands-on Software Training: Participate in workshops for practical experience with leading analytical tools and programming languages, including R, SAS, Python, HADOOP, SQL, and SPSS.
- Holistic Skill Development: Access MOOCs, skill enhancement modules, and interdisciplinary programmes to foster comprehensive personal and professional growth.
- Internship Opportunities: Gain valuable experience through industry and teaching internships, bridging academic learning with practical application.
- Dedicated Placement Support: Leverage the Centre for Industry-Academia Partnerships, which assists students with internships and job placements, ensuring strong industry connections.
- Entrepreneurial Ecosystem: Take advantage of the MIT-WPU Technology Business Incubator (TBI), offering funding, mentoring, and networking for early-stage entrepreneurs and student innovators.
- Immersion Programmes: Broaden your perspective with rural, national, and international immersion experiences, enhancing cultural understanding and industry exposure.
- Future-Ready Curriculum: Stay ahead with an interdisciplinary syllabus that integrates the latest in statistics, data analytics, machine learning, and business intelligence tools.
- Project-Based Learning: Engage in real-world projects and case studies, developing critical problem-solving and analytical skills essential for a data-driven career.
Programme Structure
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
1 |
Indian Constitution |
UC |
1 |
|
2 |
Environment and Sustainability |
UC |
1 |
|
3 |
Yoga - I |
UC |
1 |
|
4 |
Social Leadership Development Program |
UC |
1 |
|
5 |
Financial Literacy |
UC |
1 |
|
6 |
Descriptive Statistics-I |
PF |
4 |
|
7 |
Introduction to Probability Theory |
PF |
4 |
|
8 |
R Programming |
PF |
1 |
|
9 |
Calculus |
PM |
3 |
|
10 |
Discrete Mathematics |
PM |
3 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
1 |
Yoga - II |
UC |
1 |
|
2 |
Co-creation |
UC |
1 |
|
3 |
AI for Everyone |
UC |
2 |
|
4 |
Foundation of Peace |
UC |
2 |
|
5 |
Indian Knowledge System (General) |
UC |
2 |
|
6 |
Sports |
UC |
1 |
|
7 |
Introduction to Number Theory |
PF |
3 |
|
8 |
Introduction to Python |
PF |
1 |
|
9 |
Operations Research |
PF |
2 |
|
10 |
Probability Distributions |
PM |
3 |
|
11 |
Descriptive Statistics-II |
PM |
2 |
|
12 |
Linear Algebra |
PM |
2 |
| Semester | Course Type | Course Name/ Course Title | Total Credits |
|---|---|---|---|
|
1 |
Spiritual and Cultural Heritage: Indian Experience |
UC |
2 |
|
2 |
Research Innovation Design Entrepreneurship (RIDE) |
UC |
1 |
|
3 |
University Electives - I |
UE |
3 |
|
4 |
Introduction to Machine Learning |
PF |
4 |
|
5 |
Survey Sampling Theory |
PM |
3 |
|
6 |
Sampling Distributions |
PM |
4 |
|
7 |
Statistical Inference |
PM |
4 |
| Semester | Course Type | Course Name/ Course Title | Total Credits |
|---|---|---|---|
|
1 |
Rural Immersion |
UC |
2 |
|
2 |
University Electives - II |
UE |
3 |
|
3 |
Introduction to Design of Experiments |
PF |
4 |
|
4 |
IKS (Program Specific): Mathematics in India |
PF |
2 |
|
5 |
Numerical Methods |
PM |
3 |
|
6 |
Cryptography |
PM |
2 |
|
7 |
Multivariate Analysis |
PM |
3 |
|
8 |
Testing of Hypothesis |
PM |
2 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
1 |
Managing Conflicts Peacefully |
UC |
2 |
|
2 |
University Electives - III |
UE |
3 |
|
3 |
Program Elective - I |
PE |
4 |
|
4 |
Introduction to Database Management System |
PF |
4 |
|
5 |
Stochastic Processes |
PF |
4 |
|
6 |
Regression Analysis |
PM |
3 |
|
7 |
Project on DBMS |
PR |
1 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
VI |
UC | National Academic Immersion Program | 2 |
|
VI |
PE | Program Elective - II | 4 |
|
VI |
PM | Environmental Engineering: Wastewter Engineering. | 3 |
|
VI |
Program Capstone Project/Seminar and Internships | Building Planning & CAD | 3 |
|
VI |
PM | Design of Reinforced Concrete Structure | 3 |
|
VI |
Program Capstone Project/Seminar and Internships | Hydrology and Irrigation Engineering | 3 |
|
VI |
Program Capstone Project/Seminar and | AIML in Infrastructure Engineering | 1 |
|
VI |
Program Capstone Project/Seminar and Internships | AIML in Infrastructure Engineering | 1 |
|
VI |
Program Capstone Project/Seminar and Internships | Mini Project | 1 |
| VI | Program Capstone Project/Seminar and Internships | BIM in Civil Engineering | 2 |
| Total Credits: | 22 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
1 |
Program Elective - III |
PF |
4 |
|
2 |
Basics of Economics |
PM |
3 |
|
3 |
Biostatistics |
PM |
4 |
|
4 |
Program Project based on Machine Learning |
PR |
4 |
|
5 |
Capstone Project / Seminar and Internships |
PR |
5 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|---|---|---|---|
|
1 |
Program Capstone Project / Seminar and Internships |
PR |
12 |
|
2 |
Research Project |
PR |
8 |
Career Prospects
Biostatistician
Content Analyst
Data Analyst
Data Engineer
Data Governance Analyst
Data Visualisation Engineer
Data Scientist
Decision Scientist
Business Intelligence Analyst
Business Analysts
Financial Analyst
Machine Learning
Marketing Analytics Manager
Programme Outcomes
- Disciplinary Knowledge : Demonstrate comprehensive understanding of statistical concepts, theories, and applications across diverse domains.
- Critical Thinking : Apply analytical thought processes to evaluate and synthesise knowledge in statistics and data analytics.
- Problem Solving : Extrapolate learned concepts to solve unfamiliar and complex problems in statistics and data analytics using innovative approaches.
- Analytical Reasoning : Assess the reliability, validity, and relevance of data and evidence to draw meaningful conclusions.
- Research-Related Skills : Exhibit a sense of inquiry by formulating relevant questions, problematising issues, synthesising information, and articulating research findings effectively.
- Scientific Reasoning : Analyse, interpret, and draw logical conclusions from quantitative and qualitative data using scientific methods.
- Reflective Thinking : Demonstrate critical sensibility, self-awareness, and reflexivity regarding personal experiences and societal contexts.
- Information and Digital Literacy : Utilise information and communication technologies (ICT) proficiently in various learning and professional situations.
- Self-Directed Learning : Exhibit the ability to work independently, manage learning goals, and pursue knowledge proactively.
- Leadership Readiness/Qualities : Demonstrate leadership skills by effectively organising, guiding, and motivating teams to achieve shared goals.
FAQs
The programme focuses on equipping students with statistical knowledge and analytical skills necessary for data-driven decision-making across various sectors.
Students gain hands-on experience with R, Python, SAS, HADOOP, SQL, SPSS, Power BI, and Tableau, among others.
Students will work on projects that involve real-world data analysis problems, utilising statistical tools and techniques learned throughout the programme.
A dedicated placement cell and the Centre for Industry-Academia Partnerships provide guidance, training, and connections to leading employers for internships and job placements.
If you have more questions, feel free to contact the programme office or visit the university’s official website for detailed information.
Graduates can pursue roles such as Data Analyst, Statistician, Business Analyst, Data Scientist, Research Analyst, and more in sectors like IT, finance, healthcare, government, and consulting.
admissions@mitwpu.edu.in
