Master of Science in Computer Science

Scaler Neovarsity

Tuition costs:

5,09,000 INR

Area:

Computer Science

Duration:

18 months

Language:

English

Mode:

Fully Online

Description:

The course teaches students comprehensive and specialised subjects in computer science; it teaches students cutting-edge engineering skills to solve real-world problems using computational thinking and tools. Most of this program is the case (or) project-based where students learn by solving real-world problems end to end. This program has core courses that focus on computational thinking and problem solving from first principles.



The core courses are followed by specialization courses that teach various aspects of building real-world systems. This is followed by more advanced courses that focus on research-level topics, which cover state-of-the-art methods. The program also has a capstone project at the end, wherein students can either work on building end-to-end solutions to real-world problems (or) work on a research topic. The program also focuses on teaching the students the “ability to learn” so that they can be lifelong learners constantly upgrading their skills. Students can choose from a spectrum of courses to specialize in a specific sub-area of Computer Science like Artificial Intelligence and Machine Learning, Cloud and Full Stack Development, etc.



The overall course objectives of the programme are:



Knowledge

  • Students will have cutting-edge knowledge and understanding of computer science allowing them to solve real-world engineering and specific computational problems using advanced techniques at the forefront of computer science
  • Students will be able to analyse the societal, regulatory, and technological contexts for key computer science applications
  • Students will be able to apply their technological abilities to produce innovative solutions to real-world problems and that implement technique learned in the course
  • Students will display original thinking on the basis of the knowledge they gain in the course



Skills

  • Develop advanced, innovative, and multi-disciplinary problem-solving skills
  • Communicate computer science methods and tools clearly and unambiguously to specialised and non-specialised audiences
  • Develop advanced abilities related to computer science operational procedures and implement them in response to changing environments
  • Critically evaluate alternative approaches to solving real-world engineering and technological problems using cutting edge techniques in computer science on the basis of academic scholarship and case studies, demonstrating reflection on social and ethical responsibilities
  • Formulate technological judgments and plans despite incomplete information by integrating knowledge and approaches from various computer science domains including machine learning, distributed computing, and cloud computing.
  • Enquire critically into the theoretical strategies for solving real-world problems using computational thinking and tools.
  • Develop new skills in response to emerging knowledge and techniques and demonstrate leadership skills and innovation in complex and unpredictable contexts



Competences

  • Formulate research-based solutions to practical problems in environments of incomplete information
  • Manage decisions with autonomy in complex and unpredictable environments
  • Organise projects and people in a way that is responsive to changes in the wider technological environment
  • Demonstrate learning skills needed to maintain continued, self-directed study

Degree structure

In total, Master of Science in Computer Science is 2250 hours to complete; the programme is structured with tiers, which are described below.

Tier 1: Foundational Modules

375 required hours

All students must complete 15 ECTS (3 courses) of Tier 1 foundational modules.

Design and Analysis of Algorithms

Design and Analysis of Algorithms

125h

Data Structures

Data Structures

125h

Mathematics for Computer Science

Mathematics for Computer Science

125h

Introduction to Computer Programming: Part 1

Introduction to Computer Programming: Part 1

125h

Computer Systems and Their Fundamentals

Computer Systems and Their Fundamentals

125h

Relational Databases

Relational Databases

125h


Tier 2: Electives

1625 required hours

Learners should complete 65 ECTS (13 courses) from Tier 2 electives

System Design

System Design

125h

Foundations of Cloud Computing

Foundations of Cloud Computing

Front End Development

Front End Development

125h

Back End Development

Back End Development

125h

Introduction to Computer Programming: Part 2

Introduction to Computer Programming: Part 2

125h

Introduction to Problem-Solving Techniques: Part 1

Introduction to Problem-Solving Techniques: Part 1

125h

Introduction to Problem-Solving Techniques: Part 2

Introduction to Problem-Solving Techniques: Part 2

125h

Advanced Algorithms

Advanced Algorithms

125h

Practical Software Engineering

Practical Software Engineering

125h

Design Patterns

Design Patterns

125h

Data Engineering

Data Engineering

125h

Product Management for Software Engineers

Product Management for Software Engineers

Numerical Programming in Python

Numerical Programming in Python

125h

Applied Statistics

Applied Statistics

125h

Statistical Programming

Statistical Programming

125h

Product Analytics

Product Analytics

125h

Advanced Cloud Computing

Advanced Cloud Computing

Front End UI/UX Development

Front End UI/UX Development

125h

Introduction to Machine Learning

Introduction to Machine Learning

250h

High Dimensional Data Analysis

High Dimensional Data Analysis

125h

Advanced Machine Learning

Advanced Machine Learning

250h

Distributed Machine Learning

Distributed Machine Learning

125h

Introduction to Deep Learning

Introduction to Deep Learning

125h

Deep Learning for Computer Vision

Deep Learning for Computer Vision

125h

Deep Learning for Natural Language Processing (NLP)

Deep Learning for Natural Language Processing (NLP)

125h

Productionization of Machine Learning (ML) systems

Productionization of Machine Learning (ML) systems

125h

DevOps

DevOps

125h

JavaScript

JavaScript

125h

Advanced Back End Development

Advanced Back End Development

125h

Distributed Cloud Computing

Distributed Cloud Computing

NoSQL Cloud Datastores

NoSQL Cloud Datastores

Computer Systems and Their Fundamentals

Computer Systems and Their Fundamentals

125h

Low-Level Design and Design Patterns

Low-Level Design and Design Patterns

Distributed Systems with High-Level System Design

Distributed Systems with High-Level System Design


Tier 3: Capstone

250 required hours

Learners must complete a 10 ECTS (1 course) Capstone project.

Applied Computer Science Project

Applied Computer Science Project