B.Sc. Applied Statistics and Data Analytics
Programme Overview
B.Sc. Applied Statistics and Data Analytics (ASDA) 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
3 Years
Applications Open for 2025
Fee Per Year
₹ 1,00,000
Scholarship
| Scholarship for AY 2025-26 | 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: Student will have to qualify both the criteria i.e. 10th Aggregate Score and 12th Aggregate Score for availing the scholarship.
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 score in 10+2/Class 12th or in equivalent examination with Mathematics as a subject (at least 45% marks, in case of Reserved Class category candidate belonging to Maharashtra State only)
or
Minimum 55% aggregate score in any Engineering Diploma from any UGC-recognised university
Selection Process
The selection process is based on the MIT-WPU CET 2025 Personal Interaction score.
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 |
|
I |
University Core |
Effective Communication |
1 |
|
I |
University Core |
Critical Thinking |
1 |
|
I |
University Core |
Environment and Sustainability |
1 |
|
I |
University Core |
Foundations of Peace |
2 |
|
I |
University Core |
Yoga - I |
1 |
|
I |
University Core |
SLDP |
1 |
|
I |
Programme Foundation |
Descriptive Statistics-I |
3 |
|
I |
Programme Foundation |
Introduction to Probability Theory |
3 |
|
I |
Programme Foundation |
R Programming |
1 |
|
I |
Programme Major |
Calculus |
3 |
|
I |
Programme Major |
Discrete Mathematics |
3 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
II |
University Core |
Advanced Excel |
1 |
|
II |
University Core |
Financial Literacy |
1 |
|
II |
University Core |
Yoga - II |
1 |
|
II |
University Core |
Co-creation |
1 |
|
II |
University Core |
Indian Constitution |
1 |
|
II |
University Core |
IKS(General) |
2 |
|
II |
University Core |
Sports |
1 |
|
II |
Programme Foundation |
Introduction to Number Theory |
3 |
|
II |
Programme Foundation |
Introduction to Python |
1 |
|
II |
Programme Foundation |
Operations Research-I |
3 |
|
II |
Programme Major |
Continuous Distribution |
3 |
|
II |
Programme Major |
Descriptive Statistics-II |
3 |
|
II |
Programme Major |
Linear Algebra |
3 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
III |
University Core |
Research Innovation Design Entrepreneurship (RIDE) |
1 |
|
III |
University Core |
Spiritual & Cultural Heritage; Indian Experience |
2 |
|
III |
University Electives |
UE - I |
3 |
|
III |
University Electives |
UE-II |
3 |
|
III |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project Based Learning - I (Statistical Inference) |
1 |
|
III |
Foundation |
Introduction to Machine Learning |
2 |
|
III |
Programme Major |
Introduction to Sampling |
3 |
|
III |
Programme Major |
Sampling Distribution |
3 |
|
III |
Programme Major |
Statistical Inference |
3 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
IV |
University Electives |
UE-III |
3 |
|
IV |
University Core |
Rural Immersion |
1 |
|
IV |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project Based Learning - II (Numerical Method) |
1 |
|
IV |
University Core |
Life Transformation Skills |
1 |
|
IV |
Programme Foundation |
Introduction to DoE |
3 |
|
IV |
Programme Foundation |
IKS (Programme Specific): Mathematics in India: From vedic period to modern times |
2 |
|
IV |
Programme Major |
Numerical Method |
3 |
|
IV |
Programme Major |
Operations Research - II |
3 |
|
IV |
Programme Major |
Multivariate Analysis |
3 |
|
IV |
Programme Major |
Testing of Hypothesis |
3 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
V |
University Core |
Managing Conflicts Peacefully: Tools and Techniques |
2 |
|
V |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project Based Learning - III (Regression Analysis) |
1 |
|
V |
Programme Electives |
Programme Elective |
4 |
|
V |
Programme Foundation |
Introduction to DBMS |
4 |
|
V |
Programme Foundation |
Introduction to Stochastic Process |
4 |
|
V |
Programme Major |
Regression Analysis |
4 |
|
V |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project Based on DBMS |
2 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
VI |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project Based Learning - IV (Time Series) |
1 |
|
VI |
University Core |
National Academic Immersion |
2 |
|
VI |
Programme Electives |
Programme Elective |
4 |
|
VI |
Programme Foundation |
Data Mining Lab |
1 |
|
VI |
Programme Foundation |
Introduction to Time Series |
4 |
|
VI |
Programme Foundation |
Time Series Lab |
1 |
|
VI |
Programme Major |
Introduction to Data Mining |
4 |
|
VI |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project based on Sampling methods |
1 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
VII |
Programme Electives |
Programme Elective |
4 |
|
VII |
Programme Major |
Basics of Economics |
3 |
|
VII |
Programme Major |
Biostatistics |
4 |
|
VII |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project based on Machine Learning |
3 |
|
VII |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Project based on Statistical Inference |
4 |
|
VII |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Research Methodology |
6 |
| Semester | Course Type | Course Name/Course Title | Total Credits |
|
VIII |
Programme Electives |
Programme Elective |
4 |
|
VIII |
Programme Capstone Project/Problem Based Learning/Seminar and Internships |
Internship |
12 |
| Semester | Program Electives | Course Name/Course Title | Total Credits |
|
V |
Programme Elective - I |
Deep Learning |
4 |
|
V |
Programme Elective - I |
Real Analysis |
4 |
|
V |
Programme Elective - I |
Demography |
4 |
|
VI |
Programme Elective - II |
Data Analytics for HRM |
4 |
|
VI |
Programme Elective - II |
Integral Transform |
4 |
|
VI |
Programme Elective - II |
Statistical Computing Using R |
4 |
|
VII |
Programme Elective - III |
Business Analytics |
4 |
|
VII |
Programme Elective - III |
Functions of Complex Variables |
4 |
|
VII |
Programme Elective - III |
Survey Sampling Techniques |
4 |
|
VIII |
Programme Elective - IV |
Structural Query Language (SQL) |
4 |
|
VIII |
Programme Elective - IV |
Lattice Theory |
4 |
|
VIII |
Programme Elective - IV |
Econometrics |
4 |
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 
