Northwestern University - MS in Data Science
offered by Great Learning, Gurgaon

Private Institute

MS in Data Science
at
Great Learning, Gurgaon
Overview

Duration	18 months
Total fee	₹11.98 Lakh
Mode of learning	Online-Real Time
Schedule type	Weekend - All
Official Website	Go to Website
Course Level	PG Degree

MS in Data Science
at
Great Learning, Gurgaon
Highlights

Earn a degree after completion of course
Fee can be paid in installments

MS in Data Science
at
Great Learning, Gurgaon
Course details

Skills you will learn

Data mining R Python Cyber Security Statistical Analysis Data Warehousing

MS in Data Science
at
Great Learning, Gurgaon
Curriculum

Term 1

MATH FOR DATA SCIENTISTS

Apply linear programming methods to real-world models

Analyze and interpret mathematical models

Calculate and analyze derivatives and integrals of real-world models

Evaluate and interpret probabilistic models

Solve applications involving multivariate calculus

Optimize outcomes modelled by graphs and trees

APPLIED STATISTICS WITH R

Perform statistical analysis

Interpret and evaluate statistical information

Prepare technical reports

Use the R programming language for data analysis

Term 2

DATABASE SYSTEMS

Define key terms, concepts, and issues in data preparation and database management systems

Discuss the nature of data: structured data vs. unstructured data, small data vs. big data, clean data vs. messy data

Review and interpret Entity Relationship Diagrams (ERDs)

Discuss the architectures and technologies for relational database systems, data warehousing, information retrieval, and search engines

BUSINESS PROCESS ANALYTICS

Discuss standards for data mining and Business Process Modeling and Notation (BPMN)

Explain concepts related to data mining, machine learning, and process mining

Use no-code/low-code platforms for predictive analytics and machine learning

Apply analytical and machine learning techniques for decision support

Term 3

FOUNDATIONS OF DATA ENGINEERING

Analyze requirement specification documents for proposed projects or applications

Evaluate data sources and determine appropriate data exchange formats for specific projects or applications

Create comprehensive design documents based on analysis findings

Implement application designs following detailed design documentation

DECISION ANALYTICS

Demonstrate skills and techniques in optimization, simulation, and decision analysis

Select and recommend appropriate modeling techniques based on a business problem

Formulate and develop decision-analytic solutions to a given business problem using software

Present the results of decision-analytic solutions in both oral and written forms

Term 4

DATA GOVERNANCE, ETHICS, AND LAW

Demonstrate a comprehensive understanding of data management concepts, including data quality, integrity, usability, consistency, availability, and security

Propose effective methods for managing enterprise data provenance

Identify and address ethical, legal, and technical challenges in data management

Assess cybersecurity and network security risks and propose appropriate mitigation strategies

PRACTICAL MACHINE LEARNING

Apply an ML framework for model building

Build and interpret regression models

Build and interpret classification models

Apply and interpret regularization and data reduction methods

Build and interpret tree-based models for regression and classification

Term 5

NATURAL LANGUAGE PROCESSING

Identify the role of natural language processing (NLP) and text analytics within the data sciences

Extract entities and concepts, and identify, characterize, and apply methods for entity and concept co-resolution

Select and apply clustering and classification algorithms, as well as other machine learning techniques, including supervised, unsupervised, and generative methods

ARTIFICIAL INTELLIGENCE AND DEEP LEARNING

Identify key phases in the evolution of artificial intelligence (AI), including the emergence of deep learning

Distinguish between supervised, unsupervised, and reinforcement learning methodologies

Describe the structure and functionality of neural networks, including deep learning architectures

Term 6

COMPUTER VISION

Determine the appropriate level of abstraction for applying computer vision to business problems

Build computer vision models from scratch using TensorFlow 2.0

Apply computer vision models to edge-based machine learning hardware, including Intel Movidius

CAPSTONE PROJECT

A comprehensive three-month project where students apply the Data Science and Artificial Intelligence skills they have gained to address a real-world problem as part of a team