Statistics for Data Analytics introduces statistical concepts and techniques used in data analytics. Various topics such as Probability Distributions, Sampling and Estimation, Hypothesis Testing, Correlation and Regression Analysis, Analysis of Variance (ANOVA), Time Series Analysis, Nonparametric Statistics, and Data Visualization and Communication. By the end of this course, students will be able to apply statistical techniques to analyze and interpret data sets, use statistical software to perform data analysis and create visualizations, and develop research skills in statistics by applying statistical concepts to real-world problems.

Pre-requisite: None

Students will learn the big data management aspects of an organization aligning with open storage systems of Hadoop based IT (Information Technology) governance framework to provide flexibility of data management. This course covers topics such as Introduction to Big Data, Enterprise Data Governance Directive, Data Risk Management, NoSQL Storage and Security Considerations, The Key Components of Big Data Governance, Big Data Governance Framework, Master Data Management, Big Data Governance Rules, Big Data Governance Best Practices, Big Data Governance Framework Program and Data & Model Risk Management. The technical and management contents will help students to easily adopt the big data management best practices in a real time environment.

Pre-requisite: None

This course will address the broad spectrum suitable for all topics related to basic and advanced research methodology. The course covers the topics that include basics of designing research such as the selection of research approach, review of literature, use of theory, writing strategies and ethical considerations. The course also covers all the components of designing research that includes the problem statement, research questions and hypotheses, quantitative methods, survey design, instrumentation, experimental study methods, putting qualitative research into context, the approach or design (Descriptive methods and Analytical frame works), data collection procedures and analysis, and mixed methods procedures. The course will enable the students to understand and execute research in the area of big data analytics. 

Pre-requisite: None

This course provides students with an in-depth understanding of the concepts and technologies behind Big Data Analytics. Throughout this course, we will explore various topics related to Big Data Analytics, including setting up the Big Data Stack, Big Data Patterns, NoSQL Databases, Data Acquisition, Big Data Storage, Batch Analysis, Real-time Analysis, Interactive Querying, Analytics Algorithms, Data Visualization, and Case Studies. 

Pre-requisite: MDA6000

This course provides a comprehensive introduction to machine learning and data mining techniques. The course covers key topics including data preprocessing, supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, Neural Networks and Deep Learning, Natural language processing (NLP), and Time-series analysis. This course emphasizes mathematical rigor, ensuring students gain a solid theoretical understanding of machine learning and data mining algorithms and methods. Practical applications are integrated throughout the course, providing hands-on experience and illustrating the real-world implications of theoretical concepts. Students will engage with specific algorithms in detail, learning both their theoretical foundations and practical implementations. This course also focus on emerging trends and technologies in the field of machine learning and data mining such as, advanced methods in forecasting, utilization of neural networks, particularly Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks in time-series analysis. 

Pre-requisite: MDA6000

This course on Big Data Visualization provides an introduction to fundamental concepts and techniques of data visualization and their application to big data. Students will learn to analyze complex data sets and select appropriate visualization techniques, design and implement interactive and static data visualizations using modern visualization tools, evaluate existing data visualization techniques and apply principles of design to improve them, and communicate complex data insights through effective data visualizations and written reports. Through captivating storytelling, students will uncover the significance of transforming raw data into compelling narratives that drive insights and decision-making. Additionally, they will delve into the creation of interactive dashboards, exploring how these dynamic interfaces serve as windows into complex data landscapes, empowering users to explore and extract meaningful insights.  This course covers a range of topics such as Introduction to Big Data Visualization, Visualizing Big Data from a Philosophical Perspective, Data Analysis for Visualization, Visual Encoding and Design Principles, Interactive Visualization Design, Multivariate Visualization, Time-Series Visualization, Geospatial Visualization, Network Visualization, Text and Document Visualization, Evaluation and Critique of Visualizations, Visualization in Practice, and the Future of Big Data Visualization. Additionally, ethical considerations in data visualization will be addressed, fostering an understanding of the responsibilities and implications of visualizing data ethically.

Pre-requisite: MDA6001

In this course, we will explore the fundamental and advanced concepts of big data analytics and the role of deep learning techniques in handling large datasets. This course provides an in-depth exploration of various deep learning models and their applications in big data analytics. This course covers range of topics such as Introduction to Big Data Analytics and Deep Learning Techniques, Deep Learning Models, Data Preprocessing and Feature Engineering for Deep Learning, Deep Learning for Text Data, Deep Learning for Image Data, Model Evaluation and Validation for Deep Learning. This course will also cover applications of Deep learning in Big data such as Deep Learning for Health Analytics, Applications of Transfer Learning in Big Data, Deep Learning in Agriculture, Deep Learning for Time Series Data, Deep Learning for Sentiment Analysis, Deep Learning for Speech and Audio Data, Deep Learning for Streaming Data, Deep Learning for Recommender Systems. Students must work on a practical assignment and research assignment on a selected topic related to Deep Learning for Big Data.  

Pre-requisite: MDA6104 

This course will embark on an extensive exploration of cloud computing as it applies to big data, delving into both foundational and advanced concepts that define this dynamic field. The course is designed to provide a deep dive into the various aspects of cloud computing technologies, architectures, and their application in managing and analyzing large datasets.

This course includes Introduction to Cloud Computing, The Cloud Ecosystem, Parallel Processing and Distributed Computing, Cloud Hardware and Software, Cloud Resource Virtualization, Cloud Access and Interconnection Networks, Cloud Data Storage, Cloud Security, Cloud Resource Management and Scheduling, Concurrency Challenges in cloud computing. Students are required to work on a research project/assignment on a selected topic Cloud Computing for Big Data with a presentation.  

Pre-requisite: MDA6001 

In this course, students will learn about Big Data Analytics in Cybersecurity, This course covers topics such as Introduction of Data Analytics for Cybersecurity, Understanding Sources of Cybersecurity Data, Introduction to Data Mining: Clustering, Classification, and Association Rule Mining, Big Data Analytics and Its Need for Cybersecurity,  Supervised Learning – Regression, Types of Cyberattacks, Anomaly Detection for Cybersecurity, Anomaly Detection Methods, Cybersecurity through Time Series and  Spatial Data, Cybersecurity through Network and Graph Data, Human-Centered Data Analytics for Cybersecurity, Future Directions in Data Analytics for Cybersecurity.  

Pre-requisite: MDA6001

In this Course, students will learn a comprehensive introduction to the synergies between Generative AI and Big Data, laying the Introduction to Generative Deep Learning, Generative Modeling and Deep Learning. This course includes Introduction to Generative Deep Learning: Generative Modeling and Deep Learning, Generative AI Methods: Variational Auto-encoders, Generative Adversarial Networks, Autoregressive Models, Normalizing Flow Models, Energy-Based Models and Diffusion Models. The different generative AI applications include Transformers, Advanced GANs, Music Generation and World Models. Students will have hands-on experiences ranging from training GANs with different datasets to building diffusion models for generating novel varieties of data. Students will develop practical essential skills for the evolving landscape of generative AI and big data.  

Pre-requisite: MDA6104

The master's thesis course encompasses the stages of conducting research. This includes selection of research topic, formulating a problem statement, selecting and reviewing relevant literature, developing a theoretical framework, designing an empirical study including data collection, analysis, developing a prototype, make conclusions and finally writing a master's thesis report.

In the first thesis semester, students will select a research topic with a relevant research background, identify research problems, conduct a literature review, and select an appropriate methodology to carry out the research study. Finally, students will write and present the first three chapters.

In the second thesis semester, students will use a systematic approach for designing, developing and implementing a prototype, presenting, interpreting, and discussing the research results, and giving conclusions. The final report will be reviewed by the examination committee and students will defend the results and conclusions.

Pre-requisite: Department Approval 

This introductory course covers basic programming concepts such as variables, data types, construct, and repetition structures. Students learn how to design the logic of programs and then implement those programs using Python.  By the end of this course, students will be able to write small programs in Python that use variables, and mathematical and logical operators. The course also covers the fundamentals of lists, dictionary, tuples, sets and file handling techniques. A 2-hour/week laboratory is included in the course delivery. This course will be conducted in the Lab with hands-on practical exercises and demonstrations.

This course covers the fundamentals of computer architecture, arithmetic & logical operations, integer, floating-point number representation and design of computer systems, including the architectures of Von Neuman and Turing Machines. The course enables students to get an understanding of computer organization, functions of a processor, multiprocessor architectures, memory systems including virtual memory and Input/output (I/O) subsystems. The course also introduces students to parallel and pipelined architecture, cache optimization techniques, superscalar techniques and covers the basics of assembly language programming. This course will be conducted in the Lab with hands on practical exercises and demonstration.

This course presents an introduction to the essential concepts and techniques of (AI) and its applications’ areas. It provides students with the basic concepts, knowledge and skills required in utilizing Artificial Intelligence techniques in evaluating and solving problems under various conditions and constraints.

Major topics in this course include Knowledge Representation, Intelligent Agents, Problem Solving and Search Algorithms, Uninformed & Heuristic Search, First-order Logic, Constraints Satisfaction, Automated Reasoning & Planning, reasoning Under Uncertainty, and Decision Making.

This course introduces students to basic concepts of data structure. Data structures concepts such as Arrays, Stacks, queues and Linked lists, Priority Queues, Sorting Algorithms, Trees and Graphs, Heap Data Structure, Recursion & Recursive functions, Search algorithms, Search Trees, and Hash Tables and Functions.  Students will learn how to create and perform different operations on data structures.  The students will attend scheduled lab sessions to solve problems, practice the learned data structure, and analyze the various data structure techniques. A 2-hour/week laboratory is included in the course delivery.

The course covers operating systems concepts such as, Process control, Threads, concurrency, synchronization, deadlock, starvation, memory management, process scheduling, input/output management, disk scheduling, file Management and security features of operating systems. The course also covers advanced concepts such as Embedded Operating Systems, Virtual Machines and the usage of Operating Systems in Cloud Environment. The lab sessions are planned to make students skilled in operating system programming. A 2-hour/week laboratory is included in the course delivery.

This course introduces the basic concepts of database management systems, conceptual data modeling techniques, architecture and schema. Entity Relationship (ER) is precisely illustrated with various categories of relations. Relational algebra and Relational database concepts are explained for query processing. Various data normalization techniques are used to get structured databases from complex databases. Concepts of storage architecture and database security are discussed, followed by a description of the latest trends in database management. Hands on lab exercises are included using structured query language (SQL) to practice on various application areas.  A 2-hour/week laboratory is included in the course delivery.

An introductory course in probability and statistics, including statistical terminology, descriptive data, linear regression, probabilities, probability distributions, discrete and random variables, sampling distributions, point, and interval estimation, and hypothesis testing.

This course will embark on an extensive exploration of cloud computing as it applies to big data, delving into both foundational and advanced concepts that define this dynamic field. The course is designed to provide a deep dive into the various aspects of cloud computing technologies, architectures, and their application in managing and analyzing large datasets.

This course includes Introduction to Cloud Computing, The Cloud Ecosystem, Parallel Processing and Distributed Computing, Cloud Hardware and Software, Cloud Resource Virtualization, Cloud Access and Interconnection Networks, Cloud Data Storage, Cloud Security, Cloud Resource Management and Scheduling, Concurrency Challenges in cloud computing. Students are required to work on a research project/assignment on a selected topic Cloud Computing for Big Data with a presentation.  

Pre-requisite: MDA6001 

In this course, students will learn about Big Data Analytics in Cybersecurity, This course covers topics such as Introduction of Data Analytics for Cybersecurity, Understanding Sources of Cybersecurity Data, Introduction to Data Mining: Clustering, Classification, and Association Rule Mining, Big Data Analytics and Its Need for Cybersecurity,  Supervised Learning – Regression, Types of Cyberattacks, Anomaly Detection for Cybersecurity, Anomaly Detection Methods, Cybersecurity through Time Series and  Spatial Data, Cybersecurity through Network and Graph Data, Human-Centered Data Analytics for Cybersecurity, Future Directions in Data Analytics for Cybersecurity.  

Pre-requisite: MDA6001

In this Course, students will learn a comprehensive introduction to the synergies between Generative AI and Big Data, laying the Introduction to Generative Deep Learning, Generative Modeling and Deep Learning. This course includes Introduction to Generative Deep Learning: Generative Modeling and Deep Learning, Generative AI Methods: Variational Auto-encoders, Generative Adversarial Networks, Autoregressive Models, Normalizing Flow Models, Energy-Based Models and Diffusion Models. The different generative AI applications include Transformers, Advanced GANs, Music Generation and World Models. Students will have hands-on experiences ranging from training GANs with different datasets to building diffusion models for generating novel varieties of data. Students will develop practical essential skills for the evolving landscape of generative AI and big data.  

Pre-requisite: MDA6104

The master's thesis course encompasses the stages of conducting research. This includes selection of research topic, formulating a problem statement, selecting and reviewing relevant literature, developing a theoretical framework, designing an empirical study including data collection, analysis, developing a prototype, make conclusions and finally writing a master's thesis report.

In the first thesis semester, students will select a research topic with a relevant research background, identify research problems, conduct a literature review, and select an appropriate methodology to carry out the research study. Finally, students will write and present the first three chapters.

In the second thesis semester, students will use a systematic approach for designing, developing and implementing a prototype, presenting, interpreting, and discussing the research results, and giving conclusions. The final report will be reviewed by the examination committee and students will defend the results and conclusions.

Pre-requisite: Department Approval