Master of Computer Science

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.

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.

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.

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.

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 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 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 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.

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.

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 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.