B.Sc. Applied Statistics and Data Analytics

Programme Overview

The B.Sc. in Applied Statistics and Data Analytics (ASDA) is a dynamic and interdisciplinary field that combines Computer Science, Data Analytics, Machine Learning, and Statistics. It empowers professionals to collect, analyse, and harness Data and Statistics to derive valuable insights and make informed decisions. This course opens the door to a wide range of opportunities in various sectors that rely on data-driven expertise. Nowadays industries are experiencing unprecedented growth, driven by the development of technology. With this growth comes an increased need for professionals who can navigate and leverage the wealth of data and statistics. As long as data remains the lifeblood of the industry, this course will remain in high demand, poised to shape the future of industries.

It combines coursework in statistics and data analytics with hands-on experience analysing real datasets. Students learn about statistical concepts such as Deep Learning, Real Analysis, Demography, Data Analytics for HRM , Integral Transform , Statistical Computing Using R, Business Analytics, Functions of Complex Variables, Survey Sampling Techniques, Structural Query Language (SQL), Lattice Theory, Bioinformatics, Power BI, Tableau Probability, Statistical Inference, and Regression Analysis, as well as methods for data visualization, Machine Learning, Data Mining, Statistical software and programming languages such as R, Python and gain experience using these tools to analyse data.

Graduates of this programme are well-prepared for careers in fields such as market research, data science, business analytics, or public policy analysis, where they apply their skills to solve real-world problems and make data-driven decisions. They are also well-prepared for post graduate study in statistics, data science, or related fields.

3 Years

Last Date to Apply : 10 May 2024
Programme Name

B.Sc. Applied Statistics and Data Analytics

Fee Per Year


Scholarship Name Xth Score XII 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. Xth Aggregate Score and XIIth 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 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)
Minimum 55% aggregate score in any Engineering Diploma from any UGC-recognised university

Selection Process

The selection process is based on 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 Outcomes
  • Disciplinary knowledge: Capable of demonstrating comprehensive knowledge of statistics.
  • Communication Skills: Ability to express thoughts and ideas effectively in writing and orally.
  • 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/Team work: 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.
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