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

Duration: 2 years (4 Semesters), Eligibility: B.Tech/B.Sc. with Math or equivalent

Admission Details:

SessionIn the month of June-July, once a year
Seats30
Class Timing:1:00Pm - 8:00pm (Mon-Fri,Second Shift)
Recognition/AffiliationRecognised by MAKAUT.
Admission Process

B.Sc.(CS/IT)OR equivalent
Through CET Examination.

Fees Structure

ProspectusRs. 600/-
Admission Fees(One time)Rs. 10,000/-
Caution Money(One time)Rs. 5,000/-(Refundable without Interest at the end of the Programme,after adjustment if any))
Registration FeesRs. 20,000/-
Tution Fees per SemesterRs. 30,000/- X 4 =1,20,000/-
Total Fees in 2 YearsRs. 1,50,000/-

Fees Include:

  • MAKAUT Fees,Development Fees, Regular Exam, Back Log Fees Extra on actual.
  • Late Fine, Yearly Fees,Bank Fees and Student Allowance Fees applied as per norms.
  • Fees include Tution Fees,Complete two set uniform, Tab, Semester Expenses,Summer & Placement Assistance.

  • In addition, all the students have to pay University Fees as prescribed by MAKAUT from time to time. All the fees are payable in Cash or Demand Draft or Cheque in favour of "Institute of Management Study" payable at Kolkata. In general, students have to pay a onetime University Development Fee to MAKAUT and Semester Examination Fee for each semester examination to MAKAUT.
  • Miscellaneous charges include 2 sets of uniform comprising of 2 white shirts, 2 blue trousers, a tile, a belt and a blue blazor. It also include a Welcome Party, a Picnic, Industry Visits and one Seminar.
  • Caution Money is refundable at the end of the programme/transfer/discontinuation after adjusting for any damage of property/fine/penalties/dues etc.
  • Any late payment of fees in subject to late fees as decided by management.

Papers

Semester - I

  • Theory
  • Statiscal Methods.
  • Database Management System
  • Operating system
  • Fundamentals of Analytics
  • Practical
  • Analytics Lab 1
  • DBMS Lab 1
  • OS Lab using Linux/Linux

Semester - II

  • Theory
  • Modeling Techniques
  • Object Oriented Analysis and Design
  • Modern Computing Scenario
  • Data Communication and Networking
  • Cloud Computing
  • Practical
  • Object Oriented Programming Lab
  • Analytics Lab II

Semester - III

  • Theory
  • Time Series Analysis and Forecasting
  • Data Mining
  • Big Data Technology
  • Design and Analysis of Algorithms
  • Machine Learning
  • Practical
  • Data Mining Lab Lab
  • Big Data Technology and OLTP Lab

Semester - IV

  • Elective - I
  • Elective - II
  • Major Project
  • Grand Viva

Electives for Semester V and VI

Elective - IElective - II
  • Internet of Things
  • Green Computing
  • Optimization Techniques
  • Soft Computing
  • Data security and Authentication
  • Game Theory