606170
Data Science Fundamentals (3:3-0)
(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.
