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About MSC in DATA SCIENCE

The rate at which we are able to create data is rapidly accelerating. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.

“According to IBM, globally, we currently produce over 2.5 Quintilian bytes of data a day.”
The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to Carry out this analysis on a vast scale.

Aims and outcomes:

The Data Science Masters degree will provide the students with an in-depth understanding of the theory and practice of Data Science and its application in different organisational contexts. The students will also gain practical skills in handling structured and unstructured data, analyzing and visualizing data, data mining, as well as gaining hands-on experience of software tools used and their use in real-world settings.

Students will gain the skills of a “data manager” who understands what the algorithms (e.g., for data mining or handling ‘Big Data’) can do and when to use them for the benefit of the organization.

PROGRAM HIGHLIGHTS


CERTIFICATIONS

Get prepared for globally recognized Certifications

 

GLOBAL PRESENCE

150+ centres spread over 20+ countries

 

LAB FACILITY

State-of-the-art Infrastructure based on latest technologies

 

EXPERIENCED FACULTY

Get trained from industry-experts

 

iSMAC PROGRAMS

Based on ITIMS, Social, Mobility, Analytics and Cloud

 

PLACEMENT ASSISTANCE

Dedicated Placement Cell for IIHT students

Program Structure

YEAR 1

Semester 1:
1. Introduction to Data Science
2. Programming for Data Science with R
3. Set Theory and Mathematical Concepts for DATA Science
4. Fundamentals of Business Statistics
5. Implementing R Tool
6. Programming for DATA Science with SQL

Semester 2:
1. Fundamentals of Data Mining
2. Data Warehousing
3. Business Intelligence
4. Statistical and Mathematical Implementing Business statics and its application
5. Managing and Implementing Data mining tools

Year 2

Semester 3:
1. Operation Management
2. Programming for Data Science with Python
3. Deterministic Models and Optimization
4. Business Modeling for Enterprise
5. Implementing Business Research Methods
6. Machine learning

Semester 4:
1. Big Data
2. Large Scale Data Processing
3. Data Sourcing
4. Hadoop
5. Programming and Data Analysis
6. Hadoop DFS

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