606170 Data Science Fundamentals                                               (3:3-0)

Prerequisite: (601104)

(3 Credit Hours)

This course includes concepts from Statistics, Computer Science and artificial intelligence. Topics covered within the course are data visualization and analyze, exploratory data analysis, statistical inference and modeling, machine learning, and visualization, introduction to R programming, data wrangling, reproducible research, and communicating results.

606272 Statistics for Data Science                                                 (3:3-0)

Prerequisite (606271+ 103231)

(3 Credit Hours)

This course aimed to develop an understanding of modern computationally intensive methods for statistical inference, exploratory data analysis. Advanced computational methods for statistics will be introduced, including univariate, multivariate and combinatorial optimization methods and simulation methods. In addition, this course will show how to make inferences about populations of interest in data mining problems. Finally, other topics that will be covered include: theory of sampling distributions; principles of data reduction; interval and point estimation, sufficient statistics, order statistics, hypothesis testing, correlation and regression.

606315 Programming for Data Science                                          (3:3-0)

Prerequisite (606272 + 606271)

(3 Credit Hours)

This course provides students with a basic introduction to programming and cover topics related to the collection, storage, organization, management, and analysis of data, both structured (record-based) and unstructured (such as text) using Python programming language.

606361 Machine learning                                                                (3:3-0)

Prerequisite (606360)

(3 Credit Hours)

This course presents a theoretical and a practical approach for Machine Learning. Machine learning topics are introduced followed by implementation tools. Topics include: Classification, Clustering Forecasting and Prediction, Supervised Learning, Unsupervised learning, Reinforcement Learning, and Deep Learning. Several machine Learning Algorithms pertaining to these topics are introduced, followed by an introduction to some of the well-known Machine Learning tools, libraries, and frameworks such as Apache Mahout, R, Julia, Python and others.

606362 Deep Learning                                                                    (3:3-0)

Prerequisite )606360)

(3 Credit Hours)

The course discusses the fundamental knowledge of deep learning as deep learning is a subfield of machine learning, topics included within the course are: machine learning algorithms such as supervised and unsupervised learning, regression, classification, artificial neural networks, the course will also focus on the applications and libraries of deep learning such as TenserFlow, Keras, Microsoft Cognitive Toolkit, PyTorch.

606374 Data Mining & Warehousing                                              (3:3-0)

Prerequisite (606373+601281)

(3 Credit Hours)

This course will introduce students to the common data mining algorithms: association and sequence rules discovery, memory-based reasoning, clustering, classification and regression, decision trees, logistic models, and neural network models. In addition, Data Warehouse Design, Structuring Data Warehouse will also be discussed. Furthermore, students will learn how to compare analytical results and give recommendations during the data mining process. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.

(606464) Natural Language Processing

Prerequisite (606362)

(3 Credit Hours)

This course will introduce students to the common concepts of Natural Language Processing (NLP): Morphological Analysis and Finite State Automata, Word2Vec, word embedding and Deep Learning, Computational Phonology, N-grams analysis, Part of Speech Tagging, parsing with Grammars, and representing meaning. Additionally, students will learn the how to apply NLP in different areas of Artificial Intelligence such Information Retrieval, Text mining and dialog systems.

606373 Data Engineering                                                                (3:3-3)

Prerequisite ((606272

(3 Credit Hours)

This course provides students with a thorough understanding of the fundamentals of data engineering platforms, for both operational and analytical use cases, while gaining hands-on expertise in building these platforms in a way to develop analytical solutions effectively. Students will have the opportunity to construct both relational and analytical databases on the cloud or on premise from real-life datasets while using programmatic or configuration driven data pipelines. By the end of the course, students will be able to design and implement an end-to-end data engineering platform capable of supporting sustainable analytics solutions.

606475 Data Exploration and Visualization                                   (3:3-0)

Prerequisite ((606374

(3 Credit Hours)

This course covers the principles and techniques for data visualization. Visualizations are graphical depictions of data that can improve comprehension, communication, and decision-making. In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from data sets across categories, space, and time.

606271 Big Data                                                                              (3:3-3)

Prerequisite ((606170

(3 Credit Hours)

This course introduces the students to the basic knowledge and technologies needed for handling vast amount of data. Topics discussed within the course are Big Data infrastructures, Massive parallelization and Distributed operating systems and Computing, introduction to Hadoop and Spark, and how to apply map-reduce concepts for clustering, similarity search, web analytics and classification. Also, the course covers some of the known applications of NoSQL systems such as JSON stores, object storage and Elasticsearch. Also, the course may include topics such as virtualization and container orchestration, including virtual machines, dockers and Kubernetes, Hive, Python, and PySpark for Big Data applications in client-server environment.

606408 Selected Topics in Data Science & AI                                (3:3-0)

Prerequisite (603391)

(3 Credit Hours)

This course provides insight into selected contemporary relevant topics within Data Science that are not presented in the study plan. Students gain practical experience with data analysis and industry relevant algorithms and technologies for data analysis. The course is based on a novel and unified approach to recent developments in the field. Topics include (as much as time permits) machine learning for prediction & classification, association rule mining, clustering techniques, and text mining. Project reports and seminars will be required from the students to demonstrate their ability in research and oral presentations.

606465 Application of AI                                                                 (3:3-0)

Prerequisite (606362 + 606475)

(3 Credit Hours)

This course describes what is Artificial Intelligence (AI), and introduces the student to the various domains where AI can be applied Examples of relevant applications include finance, robotics, music, agriculture, health and medicine, governments, marketing, media and ecommerce…etc. Some illustrations of AI will be provided for various domains.

606384 Computer Vision                                                                 (3:3-0)

Prerequisite (601325+103241)

(3 Credit Hours)

This course presents an introduction to Computer Vision. Topics covered: basic principles of image formation, imaging processing algorithms, different algorithms for 3D reconstruction and recognition from single or multiple images (video). This course emphasizes the core vision tasks of scene understanding and recognition. Applications to 3D modelling, video analysis, video surveillance, object recognition and vision-based control will be discussed.

606476 Pattern Recognition                                                           (3:3-0)

Prerequisite (606361+103241)

(3 Credit Hours)

This course covers the methodologies, technologies, algorithms, and applications of statistical pattern recognition from a variety of perspectives. Topics covered: Bayesian Decision Theory, Estimation Theory, Linear Discrimination functions, Nonparametric techniques, Support Vector Machines, neural networks, decision trees, and clustering algorithms.

606467 Knowledge Based Systems                                                (3:3-0)

Prerequisite (606361)

(3 Credit Hours)

This course covers the development of programs containing a significant amount of knowledge about their application domain. The course includes a brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; a discussion of technical issues encountered in building a system, including selection of knowledge representation, knowledge acquisition, etc.; and a discussion of current and future research.

606468 Expert Systems                                                                   (3:3-0)

Prerequisite (606361)

(3 Credit Hours)

This course presents an introduction to the fundamental concepts of Expert Systems and their applications. Topics covered: Rule-Based Expert Systems and their applications, reasoning under uncertainty and representing imprecise and/or uncertain knowledge, expert systems technology (knowledge acquisition, design and diagnosis), expert systems languages and tools. Related topics covered include Machine Learning, Artificial Neural Works and Dialog using Natural Language processing.

606466 Augmented & Virtual Reality                                             (3:3-0)

Prerequisite (606362)

(3 Credit Hours)

This course covers the technical and experiential design foundation required for the implementation of immersive environments in the current and future virtual, augmented and mixed reality platforms. The curriculum covers a wide range of literature and practice starting from the original Computer Science and HCI concepts following the evolution of all supporting technologies including visual displays for VR, AR and MR, motion tracking, interactive 3D graphics, multimodal sensory integration, immersive audio, user interfaces, IoT, games and experience design.

606435 Distributed Systems and Cloud Computing                       (3:3-0)

Prerequisite (601432)

(3 Credit Hours)

The course covers Important topics in distributed computing system such as distributed systems design, distributed inter-process communications, platforms (operating systems) integration, management of both “distributed memory” and “distributed operating systems concurrency”, modern distributed systems architectures that utilizes the parallel computing, and clustering infrastructures. The course also introduces the 3rd major wave of computing "Cloud Computing" form technological, security and business perspectives. Covered topics will include: Secure data and computation outsourcing, Cloud computing models, Proof of data possession / retrievability, Virtual machine security, Trusted computing technology and clouds, and application of secure cloud computing.

606417 Robots Programming                                                         (3:3-0)

Prerequisite (606361)

(3 Credit Hours)

This course will learn student the controller's platform and programming language to create robots, interactive art displays, Irrigation System, Fire Fighting System, Security Systems, home automation tools, remotely log data to an Internet of Things (IoT) platform, and use the Internet to control robots from anywhere in the world.

Department Chair's Message, Dr. Abdelraouf Ishtaiwi

Department Chair
I welcome you to the Department of Data Science and Artificial Intelligence, which is one of the new departments within the Faculty of Information Technology. The ...
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