Master of Science in Data Science

The school of computer science and applications at reva university is offering a master of science in data science (m. Sc. ) – a two-year postgraduate programme.

• Data Science is a multidisciplinary field that utilizes scientific inference and mathematical algorithms to extricate important insights from a lot of structured and unstructured data. The aim of this programme is to produce postgraduates with advanced knowledge and understanding of Data Science; higher order critical, analytical, problem-solving, and transferable skills; ability to think rigorously and independently to meet higher level expectations of the ICT industry, academics, and research establishments or take up the entrepreneurial route. The programme is designed to develop human resources to meet the challenges of the ever-growing technologically advanced IT industry and the digital revolution.
• The global big data and business analytics market stood at US$ 169 billion in 2018 and is projected to grow to US$ 274 billion by 2022. PwC report predicts that by 2022, there will be around 3.5 million job postings in Data Science and Analytics in the US alone. According to TeamLease, India is staring at a shortage of 2, 00,000 analytics professionals over the next 3 years. Some of the career opportunities that require data analytics professionals are Big Data Engineer, Big Data Analyst, Big Data Analytics Architect, Big Data Solution Architect, Analytics Associate, Metrics and Analytics Specialist, Big Data Analytics Business Consultant, and Business Intelligence and Analytics Consultant.

Eligibility Criteria

• B E / B Tech in ECE / IT / EEE / CSE / ISE / TE / BCA / M.Sc or B.Sc in Computer Science / Mathematics / Information Science / Information Technology with a minimum of 50% (45% in case of SC/ST) marks in aggregate from any recognized University / Institution or AMIE or any other qualification recognized as equivalent thereto. 

Course Curriculum
Sem 1

• Principles of Data Science (Innovation)
• Programming with Python (Innovation)
• Data Mining
• Data Preparation and information Retrieval
• Mathematical Foundations for Data science-I
• Advanced Computer Networks
• Advanced DBMS
• Advanced operating Systems
• Practical Courses
• Data Mining with R Lab
• Python Lab
• Mandatory - (Non Creditable Courses)

Sem 2

• Machine Learning using Python (Innovation)
• Mathematical Foundations for Data science-II
• Big Data with NoSQL
• Foundation of Data Visualization (Innovation)
• Big Data Security
• Image and Video Analytics (Innovation)
• Cloud Computing
• Parallel and Distributed Systems
• Natural Language Processing (Innovation)
• Internet of Things (Innovation)
• Practical Courses
• Mandatory - (Non Creditable Courses)

Sem 3

• Deep Learning (Innovation)
• Data Wrangling (Innovation)
• Intelligent Systems (Innovation)
• Graphs-Algorithms & Mining (Innovation)
• Cloud Analytics (Innovation)
• Time Series Analysis and Forecasting (Innovation)
• Multivariate Methods for data Analysis (Innovation)
• Recommender Systems (Innovation)
• Minor Project (Innovation and Intellectual Property)

Sem 4

• Research/Technical paper (Innovation and Intellectual Property)
• Internship/ Certification (Innovation and Intellectual Property)
• Major Project (Innovation and Intellectual Property)

Programme Educational Objectives (PEOs)

• PEO 1: Be skilled data scientists, use existing techniques to develop Computer Engineering, Data Science solutions, Provide computer based solutions for real life problems, design, develop and test real life data science applications for specific needs.
• PEO 2: Understand the concepts and theories behind data science and adapt to the upcoming trends and technologies to the level of developing of commercially viable, robust and reliable software by ensuring that projects are completed satisfactorily, on time, and within budget.
• PEO 3: Work as a member of a team and communicate effectively across team members, to be equipped to be competent in the field of computer science and be equipped to act as business administrators or as administrators in public, private and government organisations or become an entrepreneur.
• PEO 4: Understand environmental, legal, cultural, social, ethical, public safety issues work along with engineering, medical, ICT professionals and scientists to assist them in their research and development work after further training.

Programme Outcomes (POs)

• PO 1 Disciplinary knowledge: Capable of demonstrating comprehensive knowledge and understanding of data science that form a part of the graduate programme Master of Science in Data Science.
• PO 2 Scientific reasoning: Ability to analyse, and understand concepts in Data science, and explain the theories behind Data science. critically evaluate ideas, logical reasoning and experiences in programming, software development and application development.
• PO 3 Problem solving: Capacity to extrapolate and apply competencies to solve different kinds of non-familiar problems, such as solving of real life problems through computing, provide Solutions to computing problems, analyze existing algorithms of different applications, design and develop new algorithms, operate various commercial software tools to solve scientific and business problems.
• PO 4 Environment and Sustainability: Understand the issues of environmental contexts and sustainable development and provide solutions for the same using domain knowledge in data science.
• PO 5 Research-related skills: Ability to recognize cause-and-effect relationships, define problems, formulate hypotheses, test hypotheses, analyze, interpret and draw conclusions from data, establish hypotheses, predict cause-and-effect relationships; ability to plan, execute and report the results of an experiment or investigation in current technologies.
• PO 6 Ethics: Conduct as a responsible citizen by recognizing different value systems and understand and accept responsibility of the moral dimensions and take decisions which conform to cultural, environmental, sustainability and ethical issues for them.
• PO 7 Cooperation/Team work: Ability to work effectively and respectfully with diverse teams; facilitate cooperative or coordinated effort on the part of a group, and act together as a group or a team in the interests of a common cause and work efficiently as a member of a team.
• PO 8 Communication Skills: Ability to express thoughts and ideas effectively in writing and orally; Communicate with others using appropriate media; demonstrate the ability to listen carefully, read and write analytically, and present complex information in a clear and concise manner to different groups.
• PO 9 Self-directed and Life-long Learning: Acquire the ability to engage in independent and life-long learning in the broadest context socio-technological changes.

Programme Specific Outcomes

• PSO 1: Apply the latest trends in technology to design, develop and test data scientists and related applications for specific needs.
• PSO 2: Explore the concepts and theories behind data science to develop innovative software applications.
• PSO 3: Instill life-long learning skills through the development of a research environment and higher educational opportunities.

Career Opportunities

• Data Scientist 
• Data analyst
• Data Engineer 
• Machine learning engineer
• Machine Learning Scientist 
• Applications Architect 
• Data architect
• Statistician
• Chief technology officer (CTO) 
• Chief data officer (CDO) 
• Application architect