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M.Sc. in Master of Science in Data Science(M.Sc.)

Course Duration

4 Semesters
(2 Years)

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.

Overview

The School of Computer Science and Applications at REVA UNIVERSITY is offering 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 ICT industry, academics, research establishments or take up entrepreneurial route. The programme is designed to develop human resources to meet the challenges of ever-growing technologically advanced IT industry and digital revolution.

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, Business Intelligence and Analytics Consultant.

Course Curriculum

01Python for Data Science

02Statistical Methods for Decision Making

03Databases - SQL & NoSQL

04Mathematical Foundation for Data sciences

05 Machine Learning-I (end to end ML Workflow & Deployment)

06Data Visualization using Tableau

07Structuring & Visualizing Analytics Problems (SVAP)

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