Rajeev Gandhi Memorial
College Of Engineering & Technology
Autonomous Institution
Approved by AICTE - New Delhi
Affiliated to JNTUA Anantapuramu
Counselling Code:

RGIT

COURSE DETAILS

Course Name : Computer Science & Engineering (Data Science)

Data science has become important due to recent technology disruptions. Most fundamental is Moore's Law which has driven an exponential growth in computing, storage, and communications per rupee over the past 50 years. This rate of growth shows no signs of abating. Consequently, today we have the Internet of Things: a plethora of sensors costing 10s of rupees or less, a global Internet with almost limitless bandwidth, and enormous storage in global clouds. The present era is full of technological advances in almost all spectrum of life and we are flooded with enormous amount of data. There is an increasing demand of capturing, analysing, and synthesizing this large amount of data sets in a number of application domains to better understand various phenomena and to convert the information available in the data into actionable strategies such as new scientific discoveries, business applications, policy making, and healthcare etc.


Data science is the area where applications of various tools and techniques from the disciplines of applied statistics, mathematics and computer science are used to get greater insight and to make better and informed decisions for various purposes by analysing a large amount of data. Jim Gray, database pioneer, has called Data Science the 4th paradigm of science. The first 3 are the empirical, the theoretical and the computational paradigms. In industry there is an escalating demand for trained professionals who can collect, process, and study the large data sets and reveal underlying trend and other insights. Consequently, the study of data science as a discipline has become essential to cater the growing need for professionals and researchers to deal with the future challenges.


Given the mounting importance of the data science paradigm, RGMCET has decided to start a new 4 years bachelor program on Computer Science and Engineering with a specialization Data Science. The curriculum of the this program focuses on exposing to the students with the essentials of applied statistics, applied mathematics, and computer science required in the context of data science and its applications with strong emphasis on having hands-on experience with the help of practicum, labs and experience of dealing with real-world problems.

Intake

60

Course Duration

4yrs

Course Credits

160

Total Semesters

8

Objectevies of the Program : After the completion of the degree, students would -

  • Be prepared with a varied range of expertise in different aspects of data science such as data collection, visualization, processing and modeling of large data sets
  • Acquire good understanding of both the theory and application of applied statistics mathematics and computer science based existing data science models to analyse huge data sets originating from diversified application areas.
  • Be able to create models using the knowledge acquired from the program to solve future challenges and real-world problems requiring large scale data analysis.
  • Be better trained professionals to cater the growing demand for data scientists and engineers in industry.

Program Outcomes (PO's) - Engineering Graduates will be able to:

  1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
  2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
  3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
  4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
  5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modelling to complex engineering activities with an understanding of the limitations.
  6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
  7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
  8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
  9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
  11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
  12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.