Bachelor of Data Science and Artificial Intelligence

Graduates of the program will have the ability to:

1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3. Communicate effectively in a variety of professional contexts.
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5. Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
6. Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs. [DA&AI]
7. Apply knowledge of computing and understanding of wide range of theoretical basics, principles, concepts, and advanced topics of information technology appropriate to the program’s student outcomes and to the discipline.

1. Obtaining the Jordanian General Secondary Education Certificate or equivalent.

2. High school average not less than the minimum required for the major.

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

Prerequisite: 601105
This course introduces the foundations of data science and artificial intelligence. Topics include the role of data science in business intelligence, the BI–data science distinction, data analytics tools, and the data science lifecycle. Students also explore core AI concepts such as NLP, machine learning fundamentals, linear and logistic regression, decision trees, Naive Bayes, kNN, search techniques, neural networks, deep learning, and statistical learning.

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

Prerequisite: 103231
This course develops practical and computational skills in statistical inference and exploratory data analysis. Students learn modern statistical methods, including univariate and multivariate techniques, optimization methods, simulation, sampling distributions, data reduction, estimation, hypothesis testing, correlation, and regression. Emphasis is placed on applying these methods to real data mining problems.

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

Prerequisites: 606271 and 601214
Students learn the foundations of a modern high-level programming language used in big data projects. Topics include object-oriented programming, methods, collections, strings, and package management, providing a strong programming base for large-scale data applications.

606361 – Machine Learning (3:3-0)

Prerequisite: 606360
This course provides theoretical and hands-on exposure to machine learning. Topics include classification, clustering, forecasting, supervised and unsupervised learning, reinforcement learning, and deep learning. Students also learn to work with popular ML tools and libraries such as Apache Mahout, R, Julia, and Python.

606362 – Deep Learning (3:3-0)

Prerequisites: 606361 and 606364
Students learn the core principles of deep learning, including neural network architectures, supervised and unsupervised learning techniques, regression, and classification. The course emphasizes practical implementation using modern deep learning libraries and frameworks.

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

Prerequisites: 606375 and 606361
This course covers key data mining algorithms such as association rules, clustering, classification, regression, decision trees, logistic models, and neural networks. Students also explore data warehouse design and structure, and learn how to evaluate analytical results and generate actionable recommendations.

606464 – Natural Language Processing (3:3-0)

Prerequisite: 606362
Students are introduced to NLP concepts including morphological analysis, finite-state automata, Word2Vec, deep learning for language, phonology, n-grams, POS tagging, parsing, and semantic representation. The course also covers NLP applications in information retrieval, text mining, and conversational systems.

606375 – Data Engineering and Analysis (3:3-2)

Prerequisites: 606272 and 603391
This course builds deep understanding of data engineering for both operational and analytical systems. Students gain hands-on experience designing and implementing end-to-end data pipelines, enabling scalable and sustainable analytical solutions.

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

Prerequisite: 606374
Students learn principles and techniques for effective data visualization, including graphical representation of data, pattern identification, trend analysis, and comparison across categories, space, and time. Emphasis is placed on supporting decision-making through clear visual communication.

606271 – Big Data (3:3-0)

Prerequisites: 606170 and 606272
This course provides a comprehensive overview of big data fundamentals, including terminology, distributed computing, cloud systems, MapReduce, Hadoop, and big data management. Students work with structured and unstructured data using reporting, visualization, and predictive analysis. Practical labs involve Hadoop, Apache Spark, and Python.

606376 – Big Data Analytics (3:3-0)

Prerequisite: 606271
Students learn scalable big data analytics using cloud-based technologies. The course covers pipeline design, large-scale processing with Spark and MapReduce, cloud storage, ETL workflows, and real-time streaming. Emphasis is placed on security, best practices, and real-world applications.

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

Prerequisite: 603391
This course covers advanced and emerging topics selected based on departmental priorities and student interest. Possible areas include advanced programming, databases, networking, information systems, and specialized case studies.

606465 – Application of AI (3:3-0)

Prerequisite: 606362
Students explore real-world applications of artificial intelligence, including speech and language processing and transformer-based models. Hands-on projects focus on applying AI techniques to solve industry problems.

606384 – Computer Vision (3:3-0)

Prerequisite: 606361
This course introduces computer vision fundamentals such as image formation, image processing, 3D reconstruction, and object recognition. Applications include 3D modeling, video analysis, surveillance, and vision-based control.

606476 – Pattern Recognition (3:3-0)

Prerequisites: 606361 and 103241
Students study key pattern recognition techniques including Bayesian decision theory, estimation, linear discriminant functions, nonparametric methods, SVMs, neural networks, decision trees, and clustering.

606467 – Knowledge-Based Systems (3:3-0)

Prerequisite: 606361
This course focuses on building systems that integrate domain knowledge. Topics include AI techniques, case studies, knowledge representation, acquisition, and system development challenges.

606468 – Expert Systems (3:3-0)

Prerequisite: 606361
Students learn the foundations of expert systems including rule-based reasoning, uncertainty handling, knowledge acquisition, and diagnosis. Topics also touch on machine learning, neural networks, and NLP-based dialogue systems.

606363 – Automated Systems in DS & AI (3:3-0)

Prerequisite: 606362
The course covers the principles behind automated systems in data science and AI, including machine learning algorithms and robotics tools used to build intelligent automation systems.

606417 – Robots Programming (3:3-0)

Prerequisite: 606361
Students receive an introduction to robotics programming, including robot types, Arduino and Raspberry Pi programming, IoT cloud integration, and advanced robotic applications.

606360 – Artificial Intelligence (3:3-0)

Prerequisites: 606170 and 601222
This course introduces core AI concepts including problem-solving, search strategies, knowledge representation, logic, inference, and automated reasoning. Additional topics include intelligent agents, expert systems, and machine learning.

606480 – Graduation Project (1) (3:3-0)

Prerequisites: 402201, 606375, and 90 credit hours
In this preparatory course, students form teams, select project topics, define scope, review literature, and gather requirements. The course lays the foundation for implementing the full project in Graduation Project (2).

606485 – Graduation Project (2) (3:0-3)

Prerequisite: 606480
Students design and build a complete information system to address a real-world business problem. The project integrates knowledge from across the program, with faculty supervision throughout development.

606400 – Field Training (3:0-3)

Prerequisites: Department approval and 606375
Students gain hands-on professional experience in public or private organizations. The course enables them to apply academic knowledge in real settings while contributing to workplace projects.

606409 – Innovation & Entrepreneurship in DS (3:3-0)

Prerequisite: 606375
This course covers innovation, entrepreneurship, and creativity in technology. Topics include private/public entrepreneurship, social entrepreneurship, types of innovation, creative processes, and the business model canvas. Students learn how innovation drives economic and organizational impact.

606470 – Latest Advancements in DS (3:3-0)

Prerequisite: 606375
Students explore contemporary developments in data science and information technology through group-based learning. Topics vary based on current industry trends and research.

606488 – Professional Skills in DS & AI (3:3-0)

Prerequisites: 603391 and 402201
This course prepares students for professional roles in data science and AI. Topics include job market insights, résumé development, interview skills, LinkedIn professionalism, and essential workplace competencies.

606273 – Big Data Lab (1:1-2)

Prerequisites: 606272 and 606170
This lab focuses on practical implementation of big data concepts using Hadoop, HDFS, MapReduce, Apache Spark, and Python. Students perform hands-on experiments with large-scale data processing systems.

606364 – Machine Learning Lab (1:1-2)

Prerequisite: 606360
Students apply machine learning techniques using real datasets. The lab includes implementation of classification, clustering, forecasting, and prediction algorithms using Python, scikit-learn, TensorFlow, and PyTorch.

606365 – Deep Learning Lab (1:1-2)

Prerequisites: 606361 and 606364
Students gain practical experience building and training deep learning models, including CNNs, RNNs, and feedforward networks, using TensorFlow, PyTorch, Keras, and related tools.