B.Sc. Applied Statistics and Data Analytics
Applied Statistics and Data Analytics (ASDA) is an exciting and interdisciplinary B.Sc. field that combines Computer Science, Data Analytics, Machine Learning, and Statistics. It prepares professionals with the means to collect, analyze, and use Data and Statistics towards meaningful interpretation and informed decisions.
This course provides access to vast opportunities in different sectors that require data-driven expertise. Industries are now witnessing unprecedented growth, with growth being necessitated by the development of technology. With this growth has come a greater need for professionals who can navigate and leverage the wealth of data and statistics. As long as data will be the lifeline of the industry, the course will also remain very much in demand, setting out to shape the futures of industries.
It combines course work in statistics and data analytics with practical experience analyzing real datasets. Students learn statistical concepts on topics like Deep Learning, Real Analysis, Demography, Data Analytics for HRM, Integral Transform, Statistical Computing Using R, Business Analytics, Functions of Complex Variables, Survey Sampling Techniques, Structured Query Language (SQL), Lattice Theory, Bioinformatics, Power BI, Tableau Probability, Statistical Inference, and Regression Analysis, along with techniques for data visualization, Machine Learning, Data Mining, and experience in using statistical software and programming languages such as R, Python.
Last Date to Apply : 30 June 2025
B.Sc. Applied Statistics and Data Analytics
| Scholarship Name | 10th Aggregate Score | 12th Aggregate Score |
| Dr. Vishwanath Karad Scholarship (100%) | 90 & Above | 85 & Above |
| MIT-WPU Scholarship I (50%) | 88 & Above | 83 & Above |
| MIT-WPU Scholarship II (25%) | 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 and there should be no disciplinary action against the student.
For more detailed information visit our website: https://mitwpu.edu.in/scholarships
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
The selection process is based on the MIT-WPU CET 2024 Personal Interaction score.
- Highly qualified faculty, with a strong track record of research and publication
- Workshops for hands-on experience in software such as R, SAS, Python, HADOOP, SQL, and SPSS
- Guest lectures, seminars, and workshops by eminent corporate leaders
- MOOCs, skill enhancement and interdisciplinary courses for holistic development
- Industry and teaching internship
- Dedicated Centre for Industry-Academia Partnerships to support students for internships and job placements
- MIT-WPU Technology Business Incubator (TBI) to support early-stage entrepreneurs, and students through funding, mentoring, and network connection.
- Rural, national, and international immersion programmes
Programme Structure
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
UC 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 |
|
Total Credits |
20 | ||
| Semester | Course Type | Course Name/Course Title | 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 |
|
Total Credits |
22 | ||
| Semester | Course Type | Course Name/Course Title | 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 |
|
Total Credits |
21 | ||
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Rural Immersion | UC | 1 |
|
2 |
Life Transformation Skills | UC | 1 |
|
3 |
University Electives - II | UE | 3 |
|
4 |
Introduction to Design of Experiments | PF | 4 |
|
5 |
IKS (Program Specific): Mathematics in India: From Vedic Period to Modern Times | PF | 2 |
|
6 |
Numerical Methods | PM | 3 |
|
7 |
Cryptography | PM | 2 |
|
8 |
Multivariate Analysis | PM | 3 |
|
9 |
Testing of Hypothesis | PM | 2 |
|
Total Credits |
21 | ||
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Managing Conflicts Peacefully: Tools and Techniques | 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 Based on Database Management System | Capstone | 1 |
|
Total Credits |
21 | ||
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
National Academic Immersion Program | UC | 2 |
|
2 |
Program Elective - II | PE | 4 |
|
3 |
Data Mining Lab | PF | 1 |
|
4 |
Introduction to Time Series | PF | 4 |
|
5 |
Time Series Lab | PF | 1 |
|
6 |
Introduction to Data Mining | PM | 4 |
|
7 |
Project based on Sampling Methods | Capstone | 2 |
|
Total Credits |
20 | ||
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Program Elective - III | PF | 4 |
|
2 |
Basics of Economics | PM | 3 |
|
3 |
Biostatistics | PM | 4 |
|
4 |
Project based on Machine Learning | Capstone | 4 |
|
5 |
Research Methodology | Capstone | 5 |
|
Total Credits |
20 | ||
| Semester | Course Type | Course Name/Course Title | Credits |
|---|---|---|---|
|
1 |
Program Elective - IV | PE | 4 |
|
2 |
Project based on Statistical Inference | Capstone | 4 |
|
3 |
Internship | Capstone | 12 |
|
Total Credits |
20 | ||
- Disciplinary knowledge : Capable of demonstrating comprehensive knowledge of statistics.
- Critical thinking : Capability to apply analytic thought to a body of knowledge.
- Problem solving : Capacity to extrapolate from what one has learned and apply their competencies to solve different kinds of non-familiar problems of statistics and data analytics.
- Analytical reasoning : Ability to evaluate the reliability and relevance of evidence.
- Research-related skills : A sense of inquiry and capability for asking relevant/ appropriate questions, problematizing, synthesising and articulating.
- Cooperation/Teamwork : Ability to work effectively and respectfully with diverse teams.
- Scientific reasoning : Ability to analyse, interpret and draw conclusions from quantitative/qualitative data.
- Reflective thinking : Critical sensibility to lived experiences, with self-awareness and reflexivity of both self and society.
- Information/digital literacy : Capability to use ICT in a variety of learning situations
- Self-directed learning : Ability to work independently.
- Multicultural competence : Possess knowledge of the values and beliefs of multiple cultures.
- Moral and ethical awareness/reasoning : Ability to use ethical practices in all work.
- Leadership readiness/qualities : Capability for mapping out the tasks of a team.
- Lifelong learning : Ability to acquire knowledge of statistics and data analytics skills.
The program focuses on equipping students with statistical knowledge and analytical skills necessary for data-driven decision-making across various sectors.
Yes, graduates can pursue higher studies such as a Master's degree in Statistics, Data Science, or related fields.
Students will work on projects that involve real-world data analysis problems, utilizing statistical tools and techniques learned throughout the course.
Many institutions collaborate with industry partners for internships, guest lectures, and project opportunities to enhance practical learning experiences.
admissions@mitwpu.edu.in 
