Program Overview
Earning your master's degree in a pioneering field at the European Institute of Management & Technology is a great way to start and advance a successful career in computer science. The Master of Science in Computer Science is a degree that requires 120 hours of graduate-level work over the span of two years.
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Salient Features
24 Months online Program.
Specially Designed for Working Professionals.
International Networking Opportunities.
Highly Qualified Industry Experienced Faculties.
Focused & Unique Curriculum.
Discover latest data analytics and industry trends.
Develop real life problem solving abilities.
Master of Computer Science
The Master of Computer Science will give you a wide range of advanced knowledge, with special skills in at least one area of knowledge systems, programming languages and distributed computing, information systems, mathematics/statistics, information about science or language.
You may study whenever and wherever you want because our program is fully online. A Master's degree may be earned part-time in around two years, although you can take longer if required. You can also pay your tuition in instalments, module per module.
Computer scientists operate at the cutting edge of technology, researching and developing solutions for complicated computing issues in business, science, medicine, and a variety of other sectors. You'll be in great demand if you have a master's degree in computer science. Year after year, a growing number of computer scientists transform large ideas into cutting-edge technologies.
Computer science is changing the way we live, work, travel, and much more. Many sectors are constantly changing as a result of developments in this subject, and if you become an expert in this topic, you may start an interesting career in a field of your choice. Many people appreciate the interface between computer science and human experience, so they use their degree to land amazing jobs in cutting-edge technology businesses. With this degree, you may play a major role in building and developing new systems and technology for a better, quicker, and more efficient society, whether it's in transportation, medical, design, or communications.
For students who want to investigate numerous advanced areas in computer science as part of their degree program, EIMT provides an alternative, more flexible route. This course is designed for students who desire to improve their knowledge in at least four fundamental areas of computer science, such as artificial intelligence, database and information systems, interactive computing, software engineering, scientific computing, and high-performance computing.
Post-completion of masters, you will be able to:
- To real-world issues, use mathematical underpinnings, algorithmic concepts, and computer science theory.
- Analyze an issue and determine the computational needs needed to solve it.
- Create, test, and improve a computer-based system, method, component, or application.
- Use design and development concepts to build software systems of various levels of complexity.
Our online Master's program will help you develop the theoretical and practical knowledge and skills needed to succeed in the field of computer science. Data analytics or artificial intelligence, Web programming languages, data modeling and analytics, and secure web development are just a few of the subjects covered. Python, JavaScript, Microsoft Azure, Oracle SQL Plus, Pandas, and Python 3 are few of the online programming languages and apps you'll study.
Master of Computer Science
- Eligibility: Master's Degree
- Duration: 24 Months
- Enrollment: Being online - Throughout the Year
Who is this course for ?
General Overview of DBA Program at EIMT
The DBA at EIMT is designed in this sort of way that scholars end within 3 years. Extension years are usually not granted. The program generally comprises mainly to three phases:- 1) Course Work: This phase generally is your first semester and comprises of selection, course work on different aspects of your program like research methods, quantitative and qualitative methods and techniques. This phase ends with finalization of the topic of your research and allocation of supervisor or research faculty. There will be multiple seminars and presentations in this phase. 2) Research Lab: This phase generally is mix of your second and third semester. This generally includes data collection and analysis. This phase includes your synopsis submissions and monthly progress report submission after every six month. This phase also includes multiple seminars and presentations. 3) Doctoral Thesis: This phase is generally your third to sixth semester phase that includes thesis write-up, committee reviews, thesis re-write ups, viva voices (oral defenses) and award of the doctorate. During each of these phases, the scholar will be guided and supervised by supervisor or research faculty.Outcomes of the Program:
Respond to company issues and opportunities in a strategic manner.
Examine the ethical consequences of corporate operations.
Create, execute, and evaluate corporate problem-solving solutions
Formalize business choices and conduct a systematic examination that demonstrates critical thinking.
display good collaboration abilities with a variety of people.
Lead teams to problem-solving solutions and effective project and task completion.
Take responsibility for decisions and include personal beliefs and opinions into issue solutions.
As a consequence of rigorous information analysis, communicate ideas convincingly (both written and spoken).
Important Information Regarding Masters in Computer Science Program
Program Delivery
Professional Professors
Admission Requirements:
Candidates are eligible for admission by satisfying the following admissions criteria: A bachelor’s / Undergraduate degree.
Submission and evaluation of graduate school application including; undergraduate academic transcripts, ID or passport copy, and resume.
Applicants for the Masters in Computer Science program exempted from an English exam provided they completed their schooling years and/or bachelor’s program at a recognized school where the medium of teaching is English.
Application Go Through:
Applications are only accepted online. Once the application form is received, our team looks after past your performance and future potential and will contact you accordingly.What is Next?
Once Admission Committee’s reviews your application with +ve response, you will receive a letter of admission. Having processed the payment, you will receive an email with your login credentials and will be granted access to our Learn Management System.The Curriculum
Develop your knowledge and job skills in the most crucial aspects of computer science.
Modules during your First Year
Core Courses (45 Credits)
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.
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.
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.
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.
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.
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
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.
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 .
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)
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.
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.
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.
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.
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.
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.
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.
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.
The special series covers some of the most recent and promising research directions. These are often examples of new courses we develop.
Modules during your Second Year
Data Science Specialization (30 Credits)
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.
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.
In this module, you will learn about Supervised Learning – Regression, Ensemble Techniques, Machine Learning Model Deployment using Flask, Unsupervised Learning, Supervised Learning – Classification etc.
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.
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.
Cyber Security Specialization (30 Credits)
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.
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.
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.
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.
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.
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.
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)
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
Register Now !.. to study in Next Academic Year 2023.
Frequently Asked Questions
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How much time should I spend on coursework each week?
Expect approximately 16 hours of work per week. This may include lecture videos, readings, discussions and assessments.
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What are the educational goals of the program?
To make you a better thinker, a better programmer, a better language designer, and a better understanding of current technology. Our philosophy is to require students to master core subjects and then give them the opportunity to specialize in an applied area of interest.
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Do you have to be a Computer Science undergraduate major to apply?
No, it is not required that a student have majored in CS but it is important that you have strong quantitative and analytical skills.
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I’m worried about the time zone difference; how will this work for live lectures?
Live sessions will take place according on different time zones.
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What kind of support is available?
As an online student, you will have access to several types of support resources when you need help or guidance, beginning with new student orientation. Other services include a help desk for technical issues, a student services coordinator, financial aid advisers and more.
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Do I need to take the Graduate Record Examination (GRE)?
GRE scores are not required from MS applicants.
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What is the application deadline?
As the program is in online mode, so admission can be made throughout the year.
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Is the TOEFL test required?
No, Its not required provided your schooling is in English.
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Are my units transferable?
No, units are not transferable in this program.
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What is the target audience of this course?
The intended audience for the Program are: IT Professionals Data Professionals Data Scientists Professionals looking for a career shift into Computer Science Sector
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Are the lectures pre-recorded or are there any live lectures?
All online courses are live online not pre-recorded session. Beside that, there are often live workshops/masterclasses organized.