Data Science Programs

B.Tech in Computer & Data Science

OBJECTIVE AND DESCRIPTION

The B.Tech in Computer & Data Science programme has been specifically designed to provide a launch-pad to the 10+2 students towards a successful career in the upcoming Data Science Industry, and emerging applications of Big Data. The comprehensive programme covers the fundamentals of Computer Science, Mathematics, Statistics, Data Base Management, PLT, Data Structure and Big Data Technology.


PROGRAMME DELIVERABLE

B.Tech in Computer & Data Science Degree from MUIT

PROGRAMME DURATION

48 months, consisting of 8 semesters

Candidate Eligibility:  Pass in 10+2 or equivalent with 55% minimum marks (Maths + Science/ Statistics, English Medium)

  • Engineering Mathematics – 1
  • Computer Organization & Architecture
  • Operating Systems
  • Concepts of Programming
  • Analytical Problem Solving DS
  • Big Data Analytics DS
  • PDP – I (Communication Skill)
  • Engineering Mathematics – II
  • Computer Networking
  • C Programming
  • Management Information System
  • Data Analysis using Spread Sheet (including VBA)
  • Environmental Studies
  • PDP – II TM
  • Engineering Mathematics – III
  • Data Structure and Algorithm
  • Database Management System (MySQL / Oracle)
  • Cyber Security
  • Statistics – 1
  • R Programming – I
  • PDP – III
  • Engineering Mathematics – IV
  • Linux OS and Programming
  • Data Base Administration IT
  • Statistics – II
  • R Programming – II
  • Visualisation using Tableau
  • PDP – IV
  • Introduction to Data Warehousing and OLAP
  • IoT and Cloud computing
  • Statistics – III
  • Python programming
  • Research methodology
  • Principle of Economics and Management
  • PDP – V
  • Data Mining
  • Mobile Application development (Android and IOS)
  • Linear programming (Operation Research)
  • SAS / SPSS Programming
  • Seminar
  • PDP – VI
    • Big Data Technology with Hadoop
    • Artificial Intelligence
    • Software Project Management
    • Minor Project General
    • Electives
    1. Machine learning DS
    2. Data Science Ethics
    3. Google Analytics
    4. Social Network Analysis
    •  PDP – VII
  • Internship – CS / Data Analytics
  • Dissertation

M.Sc.  in Data Science

OBJECTIVE AND DESCRIPTION

M.Sc Data Science offers focused programmes for creating employable resources in the Data Science industry by developing a vocational sense of analytical and technical skills for those with quantitative aptitude. The Curriculum is developed in close collaboration with Industry and includes in-demand tools that complement the quantitative domain skills like economics, finance, marketing, etc. Each student gets a life time access to class videos hosted on SoDS (School of Data Science) Adaptive Learning Management Portal.Case study based learning, access to real life data sets and leader board based competitive assessment structure to create a sense of competition among the participants, ensures industry ready professionals.


PROGRAMME DELIVERABLE: 

M.Sc. in Data Science, Degree from MUIT

PROGRAMME DURATION:

24 months, consisting of 4 semesters

Candidate Eligibility:

55% aggregate (or 70% in Mathematics, Economics, Statistics)

  • Introduction to Big Data and Analytics Driven Decision Making
  • Business Statistics and Statistical Modeling using R / Excel
  • Data Reporting / Dash-boarding using Advanced Excel, VBA, and Macros
  • Database / Data Warehousing (RDBMS, SQL, ETL) for Analytics
  • Data Handling using SAS Language (Data Handling)
  • Data Visualization using Tableau, Google Charts
  • Econometrics using SAS
  • Market Research & Consumer Behavior Analytics
  • Introduction to Text analytics and Text Mining to Analyze Social Networks data
  • Financial Risk Analytics (PD Modeling)
  • Machine Learning Techniques for Recommendation Engines using Python
  • Excelling Big Data Analytics with Hadoop Ecosystem (HDFS, Hive, Hbase, Zookeeper, Pig, Scoop, Spark, NLTK, etc.)
  • Analytical Problem Solving Skills
  • NoSQL
  • Case studies on :
    • Telecom Analytics (Churn, Spend, etc.)
    • Retail Analytics (Marketing Basket, Churn, Customer Value, etc.)
    • Insurance Analytics (Fraud, Customer Segmentation, Profiling, etc.)
    • HR Analytics (Appraisals, Attrition, etc.)
    • Clinical Trial (Drug Effectiveness, etc.)
  • The participants would specialize in any two of the following industry domains and will work as a research associate in that specific domain:Specialization Options:
    • Marketing Analytics
    • Optimizations Techniques
    • Retail Analytics
    • Web Analytics
    • Financial Risk Analytics
    • Handling Large Unstructured Text Data using Big Data Technologies
    • HR Analytics

P.G. Diploma in Data Science

OBJECTIVE AND DESCRIPTION

Decision Making is a core component of emerging engineering systems. This becomes more of a mandate for the systems that consume large amounts of sensing data from independent sources. Thus data-driven decision making becomes an essential component for the engineering studies. MUIT introduces this course to cover instruments and methodologies imperative for analysis of unstructured, raw and often corrupt data present in real time engineering systems.


PROGRAMME DELIVERABLE

PG Diploma in Data Science from MUIT

PROGRAMME DURATION

12 months, consisting of 2 semesters

Candidate Eligibility:  55% aggregate (or 70% in Mathematics, Economics, Statistics)

  • Introduction to Big Data and Analytics Driven Decision Making
  • Business Statistics and Statistical Modeling using R / Excel
  • Data Reporting / Dash-boarding using Advanced Excel, VBA, and Macros
  • Database / Data Warehousing (RDBMS, SQL, ETL) for Analytics
  • Data Handling using SAS Language (Data Handling)
  • Data Visualization using Tableau, Google Charts
  • Econometrics using SAS
  • Market Research & Consumer Behavior Analytics
  • Introduction to Text analytics and Text Mining to Analyze Social Networks data
  • Financial Risk Analytics (PD Modeling)
  • Machine Learning Techniques for Recommendation Engines using Python
  • Excelling Big Data Analytics with Hadoop Ecosystem (HDFS, Hive, Hbase, Zookeeper, Pig, Scoop, Spark, NLTK, etc.)
  • Analytical Problem Solving Skills
  • NoSQL
  • Case studies on :
    • Telecom Analytics (Churn, Spend, etc.)
    • Retail Analytics (Marketing Basket, Churn, Customer Value, etc.)
    • Insurance Analytics (Fraud, Customer Segmentation, Profiling, etc.)
    • HR Analytics (Appraisals, Attrition, etc.)
    • Clinical Trial (Drug Effectiveness, etc.)

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