Data Science (MSc)
Overview
Educational
Objectives
Graduates will achieve deep subject knowledge in the course of study.Produce professionals who will be employed in industry, government and entrepreneurial endeavors to have a successful professional career.Develop eloquent, assiduous leaders and problem solvers who are committed to contribute to their field, society and human well-being by applying ethical principles.Producing agile and skilled professionals to understand, collect, extract, analyze and predict the given set of data to solve the major problems through real-time data analysis across all sectors.Programme graduates can pursue their career in research
Eligibility Criteria
| For Indian and International Students | ||
|---|---|---|
| A candidate shall have passed the qualifying examination of BCA / Bachelor Degree in Computer Science / Information Technology, or any Bachelor Degree with Mathematics / Statistics / Business Mathematics as a subject at UG Level or at 10+2 level with 50% marks in UG (45% in case of SC/ ST category). An equivalent qualification is necessary for International Students |
Admission Process
Through Marwadi University (MU)
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Fee structure
| For Indian Student (INR) | ||
|---|---|---|
| Master of Science (Data Science) | 2 Years | 37500/- (Per Sem) |
| For International Student (USD) | ||
|---|---|---|
| Master of Science (Data Science) | 2 Years | 1500/- (Annual) |
Career Opportunities
Data Science is considered as one of the most trending jobs in the world. To pursue a career in this specialized course which focuses on teaching various aspects of data collection, data extractions, data analyses, data mining, data visualization, etc across diverse sectors of business, healthcare, logistics, banking and others.
According to LinkedIn, Data Science is the future of business and e-commerce world. Around 40% increase is expected in jobs in the field of data science.
Student Outcomes
Students get their Data Analytics & Data Interpretation skills sharpened. Students will be able to apply statistical concepts of data analysis, data collection, data modelling. Students will be able to apply computing technologies, such as machine learning, AI, distributed computing, python and others, to solve real world problems Students will be able to think critically and creatively, conceptualizing problems from different perspectives. Students will be able to work productively in diverse teams and solve problems collaboratively. Ability to use proper techniques to analyze and formulate data Ability to come up with critique ideas for improving implemented solutions. Acquire problem solving ability of complex business decisions, quantitative literacy and critical thinking in seeking solutions to complex business problems. Ability to serve their employer in a productive manner. Curriculum
| Semester I (W.E.F. June 2022) | ||||||||||||
| Subject Code | Subject Name | Elaboration | Teaching Scheme (Hours) | Credits | Evaluation Scheme | |||||||
| Theory Marks | Tutorial/ Practical Marks | Total Marks | ||||||||||
| Theory | Practical | Tutorial | ESE (E) | IA | CSE | Viva (V) | Term Work (TW) | |||||
| Statistical methods | Primary | 4 | 2 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Python Programming | Primary | 3 | 4 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Machine Learning | Primary | 3 | 4 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Relational and Non-relational Databases | Primary | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 25 | 25 | 50 | |
| Design and Analysis of Algorithms | Primary | 4 | 2 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Library & Information Resources | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Sports & Yoga | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Total : | 14 | 16 | 0 | 24 | 200 | 120 | 80 | 125 | 125 | 650 | ||
| Semester II (W.E.F. June 2022) | ||||||||||||
| Subject Code | Subject Name | Elaboration | Teaching Scheme (Hours) | Credits | Evaluation Scheme | |||||||
| Theory Marks | Tutorial/ Practical Marks | Total Marks | ||||||||||
| Theory | Practical | Tutorial | ESE (E) | IA | CSE | Viva (V) | Term Work (TW) | |||||
| Advanced Statistical methods | Primary | 4 | 2 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Big Data Tools | Primary | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 25 | 25 | 50 | |
| Deep Learning | Primary | 4 | 2 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Data Mining Techniques | Primary | 4 | 0 | 1 | 5 | 50 | 30 | 20 | 0 | 0 | 100 | |
| Mini Project-1 | Secondary | 0 | 6 | 0 | 3 | 0 | 0 | 0 | 25 | 25 | 50 | |
| Library & Information Resources | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Sports & Yoga | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Total : | 12 | 18 | 1 | 22 | 150 | 90 | 60 | 100 | 100 | 500 | ||
| Semester III (W.E.F. June 2022) | ||||||||||||
| Subject Code | Subject Name | Elaboration | Teaching Scheme (Hours) | Credits | Evaluation Scheme | |||||||
| Theory Marks | Tutorial/ Practical Marks | Total Marks | ||||||||||
| Theory | Practical | Tutorial | ESE (E) | IA | CSE | Viva (V) | Term Work (TW) | |||||
| Data visualization | Primary | 3 | 4 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Modeling in Operations Management | Primary | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 25 | 25 | 50 | |
| Artificial Intelligence | Primary | 4 | 2 | 0 | 5 | 50 | 30 | 20 | 25 | 25 | 150 | |
| Elective – 1 | Primary | 4 | 0 | 1 | 5 | 50 | 30 | 20 | 0 | 0 | 100 | |
| Mini Project-2 | Secondary | 0 | 6 | 0 | 3 | 0 | 0 | 0 | 25 | 25 | 50 | |
| Library & Information Resources | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Sports & Yoga | Tertiary | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Total : | 11 | 20 | 1 | 22 | 150 | 90 | 60 | 100 | 100 | 500 | ||
| List of Elective-1 |
| Computer vision |
| Information security |
| Parallel and Distributed Computing |
| Natural Language Processing |
| Semester IV (W.E.F. June 2022) | ||||||||||||
| Subject Code | Subject Name | Elaboration | Teaching Scheme (Hours) | Credits | Evaluation Scheme | |||||||
| Theory Marks | Tutorial/ Practical Marks | Total Marks | ||||||||||
| Theory | Practical | Tutorial | ESE (E) | IA | CSE | Viva (V) | Term Work (TW) | |||||
| Industry Defined Project | Primary | 0 | 44 | 0 | 22 | 0 | 0 | 0 | 25 | 100 | 350 | |
| Total : | 0 | 44 | 0 | 22 | 0 | 0 | 0 | 250 | 100 | 350 | ||
Core Courses
| Serial No. | Course name |
| 1 | Statistical methods |
| 2 | Python Programming |
| 3 | Machine Learning |
| 4 | Relational and Non-relational Databases |
| 5 | Design and Analysis of Algorithms |
| 6 | Advanced Statistical methods |
| 7 | Big data tools |
| 8 | Deep Learning |
| 9 | Data mining techniques |
| 10 | Data Visualization |
| 11 | Modelling in Operations Management |
| 12 | Artificial Intelligence |
| 13 | Industry Defined Project |
Elective Courses
| Serial No. | Course name |
| 1 | Computer vision |
| 2 | Information security |
| 3 | Parallel and Distributed Computing |
| 4 | Natural Language Processing |
Program Learning Outcomes
To apply the essentials of Data Science. To be able to solve complex problems by comparing various approaches. To be able to handle, manage, analyze, and interpret data effectively. To be able to interpret data findings effectively through ways of communication, presentation and documentation. To perform effectively as an individual or as a member or leader in a team. To acquire an in-depth understanding of concepts in statistics, data analysis, data mining, machine learning, and other data science techniques To be able to implement the industry practices through experiential learning. To obtain, clean, process, and transform data Program Learning Outcomes
To apply the essentials of Data Science.To be able to solve complex problems by comparing various approaches.To be able to handle, manage, analyze, and interpret data effectively.To be able to interpret data findings effectively through ways of communication, presentation and documentation.To perform effectively as an individual or as a member or leader in a team. To acquire an in-depth understanding of concepts in statistics, data analysis, data mining, machine learning, and other data science techniques To be able to implement the industry practices through experiential learning. To obtain, clean, process, and transform data Career Options
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Computer Programmer
Key Highlights
Growing Demand High-paying jobs Evolving Field Interesting Job role & Extensive Job experience Facilities
Lab
Total 4 labs equipped with high end processors like Core i7 and 8 GB of RAM with SSD.Specialized IoS lab for iPhone Application Development with 2.4 GHZ dual core Intel Core i5 processor with 8 GB RAM and 1 TB (5400 RPM) Hard Disks.Specialized lab for Big Data Analytics 
Library
There are two main libraries which make up the MU Learning Resource Centre. Specialist collections, use of up-to-date technology, and a team of enthusiastic and dedicated staff all combined to form a library which serves the Users of the Marwadi Education as well as contributes towards the research needs of the Institution, and is one of the best ICT-equipped academic libraries in the region.
Fully equipped with RFID (Radio Frequency Identification Device) TechnologySpecially devised and designed Self KIOSK for Self Check in & Check outE-Resource Lab having 60+ computer systems with latest configuration to assist for online research and resourcesSpecially devised and designed Mobile Application having features of; Intimation, Alerts, History, Account Status and Books search facilitiesConnected with other libraries and resource centers to retrieve information resources worldwide Separate Study rooms and discussion roomsAdditional Transportation facility for special Late Evening & Sunday for Library usersMore than 50000 books in the library
Scholarship
Campus Facilities
Impressive Infrastructure IT Enabled Infrastructure Sports Infrastructure State-of-the-art Classrooms Hostels with Gymnasium Library with 50000+ Books Academic & Cultural Events
Industry Association
Dean's Message
Dean, Faculty of Computer Applications
“Education is the passport to the future, for tomorrow belongs to those who prepare for it today” – Malcolm X
Interested students will also be given opportunities to take part in R & D projects initiated by the department.
Dear MSC Data Science aspirant, I welcome you to the Faculty of Computer Applications, Marwadi University to enrich yourself in just 2 years and to become a versatile computer professional.
Hoping to have a life long association with you all.
Awards & Recognition
Student Credit Systems
Sports Day
MU Fest
Career Guidance Seminars
Marwadi Got Talent
Our Recruiters
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