Our Master's in Computer Science Graduates will be able to employ their skills practically in their work environment and will have the competency to apply appropriate techniques and tools for their organisations. They will also develop the ability to improve the existing designs and structures, be adaptable to the changing technological landscape, and stay abreast of the latest research and development in the field of computer science.
Accreditations
Accredited by major accrediting bodies.
Alumni Network
A strong network of 1000+ alumni worldwide.
Scholarships
Merit based scholarship to students
International Students
From 20+ Countries
Programme Highlights
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24 Months - Diverse Specialisation Choices
- International Networking Opportunities.
- Highly Qualified Industry Experienced Faculties.
- Focused & Unique Curriculum.
- Explore and earn industry-demand skills
- State-of-the-Art Campus, Infrastructure and Resources
- Curriculum excellence designed by academicians and Industry Experts
To Know More
Master's in Computer Science
The Master's in Computer Science degree programme is an ideal career path for those seeking a robust foundation to work in computing-based industries. We welcome every interested individual to come on board our comprehensive programme where we encourage and challenge high-achieving students to reach their fullest potential and create impact in society. The programme is open to both beginners and working professionals seeking skill enhancement, and to help them build a foundation of advanced research into the theory and practical computing nuances. Our programme is oriented towards imparting advanced and comprehensive knowledge, robust methods of reasoning, designing and implementing computation.
The Master's in Computer Science students at EIMT may expect to attain an in-depth understanding of the Computer Science field through a disciplined approach, by delving into a range of concepts, tools and techniques, and exploring the principles that underpin the practical techniques. Through this programme, we equip our students with the potential to apply their knowledge in real-world scenarios and bring about novel ideas and situations.
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About EIMT
EIMT is a leading professional educational institution that commits to redefining the paradigm of education on a global scale. We engage students from across the globe in a milieu that encourages not just learning, but the holistic development of skills and competencies essential for leadership in the dynamic landscapes of global industries.
EIMT leverages state-of-the-art technology to deliver a flexible education model. This approach empowers our students to engage with their studies on their terms, facilitating a balance between academic pursuits and professional commitments. EIMT has been focused on a core mission: Excellence in higher education accessible through its network of schools.
Accreditations
Committed to Quality, Dedicated to You.
Why Choose Master's in Computer Science at EIMT France?
Quality teaching & high achievement rates
Experiential Learning
Cultivate practical competencies through the application of skills and knowledge in practical hands-on projects. Develop market-ready skills and build insights based on scientific research.
State-of-the-Art Classrooms
Make the most of the optimal environment designed to offer a seamless learning experience. Our classrooms are furnished with the latest technology, digital amenities, advanced tools and infrastructure for an effective course engagement.
Strategic Campus Locations
Get exposure to some of the world's top companies and investors, where you can leverage the opportunity for internship or employment after your course completion.
Focus on Core Theories and Practice
Our Master's in Computer Science programme curriculum is a blend of theory and practice designed to ensure student's readiness for the competitive market. Students will cultivate the competencies to implement impactful solutions to their organisations.
Ample Networking Opportunities
Develop connections with industry stalwarts, alumni, and faculty members that will prove helpful in your future career path. Get to know people who share similar interests from various parts of the world in different professions through academic colloquia.
Who is Master's in Computer Science for?
Transforming Top Professionals into Thought Leaders.
- Recent Computer Science Graduates: Students who have recently completed their undergraduate degree in computer science or a related field and wish to further their knowledge and skills.
- IT Professionals: Individuals working in the IT industry who want to advance their technical expertise and career prospects by acquiring deeper knowledge in computer science.
- Software Developers and Engineers: Professionals looking to enhance their programming, software development, and engineering skills to take on more complex projects and leadership roles.
- Data Scientists and Analysts: Those interested in specializing in data science, big data, and analytics to develop advanced skills in data management, analysis, and machine learning.
- Cybersecurity Specialists: Individuals aiming to deepen their understanding of cybersecurity principles, practices, and technologies to protect information systems and networks.
- Researchers and Academics: Those planning to pursue a career in academia or research, focusing on cutting-edge developments in computer science and contributing to scholarly publications.
- Entrepreneurs and Innovators: Individuals looking to start their own tech ventures or innovate within existing organizations by applying advanced computer science knowledge to create new products and solutions.
- Career Changers: Professionals from non-computer science backgrounds who seek to transition into the field of computer science and gain the technical skills required for new career opportunities.
Programme Outcomes
Master's in Computer Science Career Path & Opportunities
MCA graduates will enjoy a wealth of opportunities. With their Master of Computer Application skills and degrees that display their abilities, they will enjoy high employability in the job market.
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Research and Development
R&D is among the top career opportunities for Master's in Computer Science graduates. They can go on to work for a government agency or private sector, collaborate with industry experts and add to scientific advancements of the industry. They can work across different areas and be at the forefront of innovations.
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Data Science and Analytics
Master's in Computer Science graduates may leverage their skills and expertise to work as Data Scientists or Analytics. With the surging adoption of data and businesses' dependence on data-driven technologies and practices, professionals in the data science field have become highly prominent.
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Cybersecurity Specialist
With the flooding demand for cybersecurity, venturing into this field will prove highly promising and rewarding. Master's in Computer Science graduates with a specialisation in cybersecurity are in high demand across industries.
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Software Development
If you are interested in Software development, can leverage your programming prowess and contribute to the tech world with innovative plans. Software Developers are among the handsomely paid professions in the tech world.
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Artificial Intelligence (AI) and Machine Learning (ML)
Give your expertise in AI, ML and algorithms the right boost by working as a specialist in these emerging and rapidly growing areas of computer science. They are in high demand across the globe.
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IT Management and Consultancy
Another promising path that Master's in Computer Science graduates can venture into is the field of Information Technology as a Manager or consultant. They can propel their career to leadership roles and positions and lead their companies towards innovations. Teaching and Academia If you have a passion for teaching, entering the Academia is another excellent choice. Master's in Computer Science graduates can go on to teach students at schools or colleges and make significant contributions by shaping and moulding future innovators.
The Curriculum
Mandatory Modules
Core Courses (45 Credits)
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Design and Analysis of Algorithms
In this module, you will learn about what is algorithms, analysis and design of algorithms, soting in polynimial and linear time, elementry data structure, advanced data structure, advance design and analytical techniques, graph algorithms (BFS, DFS and many more), randomized algorithms etc.
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Distributed Systems
The primary objective of the module is to teach the fundamental concepts and working details of distributed systems and the underlying technologies. Topics include distributed systems architectures, processes, communication and synchronization, consistency and replication, fault-tolerance and security.
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Media Technologies
Students learn to use computing and multimedia for the film and media industry, the Internet and various production developments, audio and visual media, and film production skills. The media technology curriculum also focuses on creative research and understanding of science and technology. The duration of the Master of Science in Media Technology course is two years and its nature depends on its work that gives them a lot of work.
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Artificial Intelligence
This course covers various aspects related to machine learning and probability theory. In addition, students will learn natural language and computer vision to master the science of using machines to perform tasks that require human intelligence.
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Network Security
You cover topics such as current and future Internet standards, programming networks, and securing the systems. We offer strong value through laboratory programs in software engineering and computer networks; Security lab work involves a special environment where attackers’ methods can be detected and stopped using special security tools.
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Data Mining
Data mining studies algorithms and mathematical techniques that allow computers to find patterns and patterns in databases, make predictions and forecasts, and generally improve their performance by interacting with data. It is now seen as a key part of a general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. Knowledge discovery techniques include data selection, cleaning, encryption, the use of various mathematical techniques and machine learning, and visualization of artifacts. This course will cover all these questions and illustrate the whole process with examples. Special attention will be given to machine learning techniques as they provide good tools for knowledge discovery.
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Enterprise Cybersecurity
This program aims to develop students in the discipline of cybersecurity and includes theoretical knowledge and advanced skills in technology, communication information management and methods to ensure effective operations in the context of identification and mitigation a threat. Students develop highly practical skills in key areas such as programming, advanced databases, network and system administration, while providing theoretical knowledge in digital encryption and encryption.
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Big Data Analytics
The Big Data Analytics module is designed to ensure that students have all the necessary exposure to cover everything from data science to the use of advanced analytics techniques. This Big Data Analytics module covers a variety of large datasets, which may contain structured, unstructured and unstructured data, and data from multiple sources in sizes ranging from terabytes to zettabytes .
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Web Application Development
This course focuses on the design and development of web applications using various models programming languages and tools. Students will be exposed to online applications walking development. Class projects include business-to-market (B2C) development and business-to-business (B2B) applications, among others.
Elective Courses (Select Any 3) (15 Credits)
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Foundations of Analytics and Data Visualisation
Students will get deeper understanding of patterns and patterns within data to support forecasting and decision making and Understand basic data analysis skills, including preparing and working with data; abstraction and formulation of research questions; and using statistics, learning and research etc.
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Computer Graphics
This module focuses on interactive and non-interactive 2D and 3D graphics. This module studies the principles of creating and displaying 2D and 3D synthetic images. In this module, topics include geometric shapes, 3D visualization and projection, lighting and shading, color, and the use of one or more technologies and packages such as OpenGL and Blender. -
Biometrics
In this module, you will learn about Biometric fundamentals, Biometric technologies – Biometrics vs traditional techniques , Finger-scan – Facial-scan – Irisscan – Voice-scan – components, working principles, competing technologies, Signature-scan – Keystrokescan, Standards in Biometrics – Assessing the Privacy Risks of Biometrics – Designing Privacy – Sympathetic Biometric Systems etc.
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Advanced Programming Techniques
This module begins with explaining object-oriented concepts, including abstraction, encapsulation and polymorphism in the context of the Java programming language. Then, focus shifts to the details of the Java architecture database, especially collections and efficient disk database and file access, including SSTables, LSM trees, bit-level compression, Sliding window, reverse direction, hash structure and tree affect file search.
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Software Design and Patterns
In this module, the use of design software is introduced. Topics included design process (creative process, design and practice), architectural principles, constraints, object-oriented design principles and Program idioms will be discussed. This course will use a long-term project to give students real life hands-on experience and models from building software systems.
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Web Mining and Graph Analytics
Web Mining and Graph Analytics covers aspects of web mining, fundamentals of machine learning, text mining, clustering, and graph analysis. This includes learning the basics of machine learning algorithms, how to evaluate algorithm performance, feature management, content extraction, impact analysis, distance metrics, the basics of clustering algorithms, how to evaluate cluster performance and the basics of graph analysis algorithms.
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Network Forensics
This course provides an introduction to techniques and methods related to digital forensics in a networked environment. Students will develop an understanding of key concepts related to topologies, protocols, and tools necessary to conduct research in network environments. Students will learn the importance of network forensics, forensic analysis, digital evidence analysis, and documentation of investigative processes. The course will include presentations and laboratory activities to reinforce the practical applications of the course and will require an independent research paper related to the topic of the course.
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Advanced Database Management
Candidates will get a detailed explanation of the relationship process and how to do it. Module will also develop candidates’ knowledge of current topics and advances in interactive database systems, object-oriented programming and XML database systems. In addition, the candidates will have to check the new architectures for database management systems and further develop their understanding of the impact Emerging data security standards may contain resources provided by future data security controls system.
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Special Topics in Computer Science
The special series covers some of the most recent and promising research directions. These are often examples of new courses we develop.
Elective Modules - (Select One)
Apart from the below mentioned specialized modules, you will need to submit a Major Project with proper report.
Data Science Specialization (30 Credits)
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Foundations
In this module, you will learn about Introduction to programming using Python (Loops, functions, methods, operators), Introduction to programming using R (documentation, data types, data structure, loops, algorithms), Database Management System using My SQL (DBMS, SQL accessing, MySQL, ETL) etc.
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Data Analysis
In this module, you will learn about Statistics For Data Science (Probability distribution, Normal distribution, Poisson’s distribution, Type 1 and Type 2 errors, Hypothesis testing), Exploring Data Analysis (reading, cleaning data, Seaborn, matplotlib, Univariate and Multivariate statistics) etc.
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Machine Learning Techniques
In this module, you will learn about Supervised Learning – Regression, Ensemble Techniques, Machine Learning Model Deployment using Flask, Unsupervised Learning, Supervised Learning – Classification etc.
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Data Visualization
In this module, you will learn about Data Visualization Using Tableau, Working with Continuous and Discrete, Data Using Filters, Data Visualization Using Google Data Studio, Using Calculated Fields and parameters, Creating Tables and Charts, Data Visualization Using Power Bi, key features of Power BI workflow etc.
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Introduction To Artificial Intelligence
In this module, you will learn about Time Series Forecasting, Text Mining And Sentimental Analysis, Introduction to Natural Language Processing, Reinforcement Learning, Introduction to Neural Networks and Deep Learning, Computer vision etc.
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Hacking Wireless Networks, Mobile platforms & IoT hacking
In this module, you will learn about Wireless Concepts, Wi-Fi Authentication modes, WEP vs.WPA vs.WPA2, WEP issues, Wi-Fi Sniffer, Mobile attack vectors, Apps and boxing issues, Hacking with z ANTI, Hacking iOS, Mobile Pen Testing, IoT Concepts, Challenges of IoT, IoT threats, IoT hacking tools etc.
Cyber Security Specialization (30 Credits)
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Fundamentals of Cyber Security, Linux & Networking
In this module, you will learn about What is Cybersecurity?, What is the Impact of Cybercrime?, Difference Between Linux and Windows, Basic commands, Linux Boot process, b Scheduling Tasks, Advanced Shell Scripting, Linux Networking, Information over open source projects etc.
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Ethical Hacking, Footprint & Reconnaissance
In this module, you will learn about Ethical Hacking Concepts, Scope and limitation sof Ethical Hacking, Defense-in-Depth, Why penetration testing?, Footprinting through Search Engines, Footprinting through Web Services, Website Footprinting, Mirroring the entire website, Email Footprinting, Network Footprinting, Footprinting Tools etc. -
Enumeration, Vulnerability Analysis, System Hacking
In this module, you will learn about Enumeration Concepts, Net BIOS Enumeration, LDAP, NTP, SMTP, DNS, Vulnerability Assessment Concepts, Vulnerability Scoring Systems, System Hacking Concepts, Password cracking tools, NTFS Data Stream, What is steganography?, Covering tracks tools etc.
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Malware Threats, Network Attacks, Social Engineering
In this module, you will learn about Malware Concepts, Wrappers, Crypters, Stages of virus life, Ransomware, Malware Analysis, What is Social Engineering?, Insider Threats, Anti-phishing tool bar, Identity Theft, Wireless Encryption, Wireless Threats, Denial-of-Service attack, Wi-Fi Sniffer, How to blue Jack a victim etc.
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Denial-of-Service, Honeypots & Hacking Web Servers
In this module, you will learn about DoS/DDoS Concepts, HTTP GET/POST and slow loris attacks, Fragmentation attack, Peer-to-peer attacks, IDS, Firewall and Honeypot Concepts, Evading IDS, Detecting Honeypots, Web Server Concepts, Web Server Attacks, Web cache poisoning attack, Website defacement, Website mirroring etc.
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Cloud security & Cryptography
In this module, you will learn about Cloud Computing Concepts, Cloud Computing Threats, Cloud Computing attacks, Domain Name System (DNS) attacks, Wrapping attack, Session Hijackingusing session riding, Cloud security control layers, Cloud Penetration Testing, Cryptography Concepts, Cryptography Tools, Disk Encryption, Cryptanalysis etc.
Full Stack Specialization (30 Credits)
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Introduction & Preparatory
In this module, you will learn about Program Structure & Basic Principles, course jounrey mapping, Programming Constructs – Loops, Functions, Arrays, An Introduction to Version Control, Git, Command-line Scripting, Basic HTML, CSS etc.
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Front End Development
In this module, you will learn about HTML & CSS Interaction, CSS: Styling, Selectors, Box Model, Border, Margin, Padding, Bootstrap 3,4,5, JavaScript Fundamentals, Hoisting, Callbacks, Promises, Asynchronous JavaScript, DOM Manipulation, JSON, AJAX Calls, Communication with Server, Event Listeners, Local and Session Storage, Advanced JavaScript , JAVASCRIPT FRAMEWORKS – Angular or react etc.
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Back End Development
In this module, you will learn about Object-Oriented Paradigms of Java Programming, Design – Interfaces| Abstract Classes | polymorphism , Arrays, Strings, Stacks, Queues, Linked Lists, Binary Trees and Binary Search Trees, Tree traversals, Graphs, Dynamic Programming, Hashing Algorithms, Recursion, Searching and Sorting Algorithms, Greedy Algorithms, Tables, Views, SQL Queries – Simple & Complex, JSP & Servlets, Servlet Lifecycle, Rest APIs, Backend Development Using Springboot Framework etc.
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Mobile Application Development React Native
In this module, you will learn about Understanding Native Mobile Apps Development, Android fundamentals – activities, views, layouts, resources, manifest, iOS fundamentals – Storyboard, Segues, Views, View Controllers, Layouts, Installing the React Native CLI, Installing IDE: VS Code, React Native Elements: React Native UI Toolkit, Native Modules and APIs etc.
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Cloud Computing & Devops
In this module, you will learn about Basics of Virtual Machines – Process Virtual Machines, Virtualization Management, Comprehensive Analysis Resource Pool – Testing Environment, virtualization of CPU, Memory and I/O devices, Cloud deployment models: public, private, hybrid, community, Architectural Design Challenges – Public Cloud Platforms: GAE, AWS, Programming models, cloud security, cloud & devops etc.
Artificial Intelligence & Machine Learning (30 Credits)
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Foundations
In this module, you will learn about Python Basics, Python Functions and Packages, Working with Data Structures, Arrays, Vectors & Data Frames, Jupyter Notebook – Installation & function, Pandas, NumPy, Matplotlib, Seaborn, Descriptive Statistics, etc.
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Machine Learning
In this module, you will learn about Supervised Learning – Linear Regression, Multiple Variable Linear Regression, Logistic Regression, Naive Bayes Classifiers, K-NN Classification, Support Vector Machines, Unsupervised learning – K-means Clustering, Hierarchical Clustering, Dimension Reduction-PCA, Ensemble Techniques, Recommendation Systems etc. -
Introduction to Neural Networks and Deep Learning
In this module, you will learn about Supervised Learning – Linear Regression, Multiple Variable Linear Regression, Logistic Regression, Naive Bayes Classifiers, K-NN Classification, Support Vector Machines, Unsupervised learning – K-means Clustering, Hierarchical Clustering, Dimension Reduction-PCA, Ensemble Techniques, Recommendation Systems etc.
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Introduction to Sequential data
In this module, you will learn about RNNs and its mechanisms Vanishing & Exploding gradients in RNNs LSTMs – Long short-term memory GRUs – Gated recurrent unit LSTMs Applications Time series analysis LSTMs with attention mechanism Neural Machine Translation Advanced Language Models: Transformers, BERT, XLNet Computer vision etc.
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Introduction to GANs & its Applications
In this module, you will learn about Introduction to GANs, How GANs work?, DCGANs – Deep Convolution GANs, Introduction to Reinforcement Learning (RL) RL Framework Component of RL Framework Examples of RL Systems Types of RL Systems Q-learning, LANGUAGES AND TOOLS- Python ,Python ML library ,Scikit-learn ,NLP library ,NLTK ,Keras, Pandas Numpy ,Scipy, Matplotlib ,TensorFlow etc.
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