B.Sc. Applied Statistics and Data Analytics

Programme Overview

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.

Duration
3 Years

Last Date to Apply : 30 June 2025
Programme Name

B.Sc. Applied Statistics and Data Analytics

Fee Per Year

Rs.1,00,000

Scholarship
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.

Scholarship DetailsScholarship Details

*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

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 2024 Personal Interaction score.

Programme Highlights
  • 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
Programme Outcomes
  • 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.
FAQ's

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.

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