Introduction

Welcome to the Master of Science in Data Science Handbook! This guide has been carefully designed to provide you with all the essential information needed to navigate the program effectively. Whether you are a new or continuing student, the handbook serves as your primary resource for understanding program structure, academic policies, available resources, and key milestones.

Master of Science in Data Science Program

Degree Title

Master of Science in Data Science

Mission

“To graduate highly skilled Data Science professionals equipped with advanced knowledge, technical expertise, and ethical responsibility to analyze complex data, derive meaningful insights, and contribute to innovation and decision-making across diverse industries and research domains".

Program Goals

o   Equip students with in-depth knowledge of data science methodologies, tools, and technologies, including machine learning, statistical modeling, big data analytics, and data visualization.

o   Enable students to analyze complex datasets, design data-driven solutions, and make informed decisions to address real-world challenges across various industries.

o   Instill a strong understanding of data ethics, privacy, and security to ensure responsible and ethical handling of data in professional and research settings.

o   Prepare students to contribute to cutting-edge research in data science and apply innovative techniques to advance the field.

o   Develop the ability to work effectively in interdisciplinary teams, communicate technical findings to diverse audiences, and apply data science skills in various professional sectors.

Learning Outcomes

Knowledge and Understanding

The graduate will be able to:

1.     Identify and describe in-depth principles, approaches, methodologies, tools and facts that are associated to the theoretical and technical topics in the data science field.

2.     Characterize and explain cutting-edge research and theories in data science domains.

3.     Classify and interpret the influence of data science processes and practices in business intelligence, big data analytics, optimization, personalization, and decision-making methods.

Skills

The graduate will be able to:

1.     Utilize the appropriate and contemporary tools, methods, practices, policies, standards, and procedures in the field of data science.

2.     Develop comprehensive mechanisms for data collection and preprocessing, models training and evaluation methods, and algorithms to enhance the value of data science applications.

3.     Construct and implement integral solutions, models, applications, and projects in the data science field, as well as appraise the resulting outcomes and contributions.

4.     Design, conduct, and evaluate state-of-the-art research and advanced projects in the field of data science.

5.     Apply various techniques, processes, and materials to analyze, interpret, and offer potential solutions for complex problems in the field of data science.

6.     Communicate effectively and skillfully in a range of professional contexts within the realm of data science.

Values

The graduate will be able to:

1.     Demonstrate professional and ethical practices and responsibilities within the domain of data science.

2.     Practice effective, ethical, and professional leadership and communication in all duties, tasks, projects, and teamwork within the field of data science.

3.     Engage proficiency in self-learning, proactive planning, and continuous professional development, while actively contributing to the advancement of the field of data science and society.

Program Design

The Master of Science in Artificial Intelligence program at the University of Tabuk is a course-based program along with a project to be submitted at the end of the program during two semesters.

The students are required to complete a minimum of 45 credit hours by both courses and a project.

Program Design

Master of Science in Data Science students must complete 12 courses and 2 research projects (Research Project 1 and Research Project 2). Students are required to complete a minimum of 45 credit hours, which include:

• 36 course credits from core courses.
• 3 elective course credits.
• 6 project credits: Research Project 1 (3 credits) and Research Project 2 (3 credits).

Admission Requirements

o   The applicant must hold a Bachelor's degree in Computer Science or related field, mathematics, or statistic.

o   The applicant's overall GPA must be at least "Good".

o   The applicant must have passed the General Aptitude Test for University Students.

o   Efficiency in English: An applicant must either submit proof of having completed their bachelor's degree with English as the medium of instruction or provide evidence of English proficiency through standardized tests such as IELTS, TOEFL, STEP, etc.

Degree Requirements

Academic Standards

Students must maintain a cumulative Grade Point Average (GPA) of at least 3.75 out of 5 on graduate degree courses.

Time Limit

The entire work for the Master's degree can be completed within a period of 2 calendar year. But, all articles of the unified law organizing the graduate studies in Saudi universities, and those also enumerated in the Regulations and operating procedures for the Postgraduate Study booklet (University of Tabuk), control time limit for acquiring Master's degree.

Program Duration

2 years (4 Semesters): Full-time

Fees

15,5000 SAR per semester. (No fees for Summer Internship)

62,000 SAR per program.

Master of Data Science: Study Plan

Full Time –2 Year Plan

1^St Semester

2^nd Semester

Summer Semester

3^rd Semester

4^th  Semester

Elective Courses

Appendix A: Courses

CIS 601: Fundamental of Data Science

The Data Science course concentrates on techniques and methods needed in the Data Science project lifecycle, which includes data collection, data management and data preprocessing, analysis, presentation, as well as operationalization. This class aims at giving students an introduction to all phases of the process of data process and using modern tools and real data; they will gain hands-on experience of the process. The course includes topics such as data formats, cleaning, and loading; data analysis, data governance, data storage in non-relational and relational store; topping up using cluster computing; and data visualization. They will also store and access various data through using suitable data management tools, database, control accessibility of data that is sensitive, and implement conversions of data in different formats. Finally, students will be capable of presenting the results of data science project using reports and visualizations to be used as a foundation of operationalization.

CIS 604: Advanced Topics in Databases

This course emphasizes advanced topics in database systems. The course begins with an overview of the associated database analysis, design, and methodology issues. Then, it highlights transaction management, query processing, distributed DBMSs and replication, and object DBMSs. In addition, the course emphasizes web technology and DBMSs, semi-structured data and XML. In particular, the related business intelligence topics such as data warehousing concepts, OLAP, and data mining are also covered and emphasized.

CSC602: Computational Mathematics

This course provides students with theories, fundamental concepts that are needed to study data science. The course will include statistics, probability and probability distributions, calculus, integrals and their applications. The course also will cover topics of linear algebra that are required in data science like matrices, vector spaces, subspaces, bases and dimension, Eigenvectors Eigen spaces, and linear transformations. The course also will include some of the major concepts in multi-variate calculus like Lagrange Multipliers and Constrained Optimization problems. Through the course students will acquire cognitive skills through thinking and problem solving with special focus on applications of the different techniques of data science related topic.

CIS605: Programming for Data Science

This course introduces students to the dominant programming languages for AI/ML and deep learning. It dives deeply into programming tools, libraries, and frameworks for building research projects. This includes reading and loading datasets, preprocessing data, understanding structure using statistical summaries and data visualization, data manipulation, and cleaning techniques. This course also guides students through learning and implementing popular ML and deep learning frameworks such as TensorFlow, Keras, and PyTorch for symbolic math, used to perform differential programming and linear algebra.

CSC603: Machine Learning

This course introduces the fundamental concepts and functioning of machine learning. It covers theoretical foundations as well as essential algorithms for supervised and unsupervised learning. The practical part will focus on the application of machine learning to a range of real-world problems. The topics include: linear and logistic regression, naïve Bayes classifier, k-NN, decision tree, random forest, support vector machine, clustering, dimensionality reduction, and perceptron.

CSC617: Data Mining

In this course we explore data mining interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. We will discuss the main data mining methods currently used, including data warehousing and data cleaning, clustering, classification, association rules mining, query flocks, text indexing and searching algorithms, how search engines rank pages, and recent techniques for web mining. Designing algorithms for these tasks is difficult because the input data sets are very large, and the tasks may be very complex. One of the main focuses in the field is the integration of these algorithms with relational databases and the mining of information from semi-structured data.

CIS608: Research Methods & Semina

The course aims to familiarize students with the fundamental concepts of research and the importance of research and its methodologies, including theory of science and qualitative and quantitative methods. Also, the course aims to give students skills for understanding the structure of a research paper, critical reading of research paper, developing a research proposal for a master's project and writing a research manuscript. Students will use these theoretical concepts of research to begin to critically review literature relevant to the field of artificial intelligent and its applications. Furthermore, the seminar in Data Science is intended to provide students with the latest research and practical experience in the field of data science and how its applied in the real environment. It also aims to give students the nature of daily work of data scientist. Student will attend actual or virtual talks by renowned data scientists and write a review report on such a talk, speakers are from both private sector and academia will be invited along the course.

CIS 606: Data Visualization

The course discusses the use of computer-supported, interactive and visual representations of data in order to amplify cognition, help people reason effectively about information, find patterns and meaning in the data, and easily explore the datasets from different perspectives in particular in data-intensive environment. The course covers the principles of data visualization, techniques and methods needed to provide clear illustrations of data. Specific techniques to display certain types of data such as text or time series data are also covered. Students get practical experience on how to employ and evaluate data visualization software tools and programming libraries, and learn the skills needed to convert raw datasets into meaningful, interactive, dynamic, and insightful graphical dashboards.

CIS611: Big Data Analytics

This course gives an overview of the Big Data phenomenon, focusing then on extracting value from the Big Data using predictive analytics techniques. It also focuses and includes fundamental of big data storage and management related issues, the main Big Data tools and technologies such as e.g. Hadoop & Spark, the potential use in a corporate environment, and the use of predictive analytics on big data.

CSC610: Information Retrieval

This course studies the basic principles and practical algorithms used for information retrieval and text mining. It covers the tasks of indexing, searching, and recalling data, particularly text or other unstructured forms. The contents also includes: statistical characteristics of text, several important retrieval models, text categorization, recommendation system, clustering, information extraction, etc. The course emphasizes both the above applications and solid modeling techniques (e.g., probabilistic modeling) that can be extended for other applications.

CIS615: Cloud Computing

The objective of this course is to provide graduate students with the comprehensive and in-depth knowledge of Cloud Computing concepts, technologies, architecture, and applications by introducing and researching in Cloud Computing fundamental issues, technologies, applications, and implementations. Another objective is to expose the students to frontier areas of Cloud Computing and information systems, while providing sufficient foundations to enable further study and research.

CIS690: Data Science Professional and Ethical issues

The objective of this module is to highlight the professional and ethical issues in Data Science field. Thus, this course provides a framework to analyze such issues and examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency.

CSC615: Data Analytics in Healthcare

The objective of this course is to teach the use of healthcare data to make decisions and transform healthcare delivery and the health of individuals and populations. The course concentrates on big and small data, and structured and unstructured data. Tools, applications, and approaches for health data analytics are taught. This course covers topics such as statistical approaches; data; data visualization, simulation, modeling and forecasting. Key regulatory health and healthcare reporting requirements are taught.

CSC618: Edge Computing

This course will explore research, frameworks, and applications in Edge Computing, with a focus on big data analytics. The class will begin with a review of current big data analytics frameworks for Cloud Computing. We will then explore frameworks for computing over edge devices and cloud. Finally, we will study algorithms for distributed data analytics over edge devices.

CIS621: Business Analytics

The course is an introduction to Business Analytics. It covers managerial statistical tools in descriptive analytics and predictive analytics, including regression. Other topics covered include forecasting, risk analysis, simulation, and data mining, and decision analysis. This course provides students with the fundamental concepts and tools needed to understand the emerging role of business analytics in organizations and shows students how to apply basic business analytics tools.

CSC 604: Neural Networks and Deep Learning

This course offers a broad introduction to neural networks and deep learning. It also explores the applications and theories relevant to problem solving using deep learning. The course applies deep learning algorithms to real-life problems in diverse domains such as computer vision, natural language processing, sequence modelling, and more. It covers neural networks, backpropagation, optimization (SGD, RMSprop, and Adam), autoencoders, convolutional neural networks, inception, residual networks, RNNs, LSTM, dropout, batch normalization, Xavier initialization, transfer learning, generative adversarial networks, and deep reinforcement learning.

CIS622: Selected Topics in Data Science

The objective of this module is to highlight the up to date issues/topics in Data Science field. Thus, the main purpose of this course is to highlight and investigate "selected/special topics" in Data Science that are not covered in the other offered courses. Such topics might be interrelated to one or more Data Science disciplines. However, the module should cover/give profound understanding for "the selected topics" including the associated perceptions, techniques, models, and tools.

CIS626: NoSQL Databases

This course will explore the origins of NoSQL databases and the characteristics that distinguish them from traditional relational database management systems. Core concepts of NoSQL databases will be presented, followed by an exploration of how different database technologies implement these core concepts. The course will also cover the four main NoSQL data models (key-value, column family, document, and graph), highlighting the business needs that drive the development and use of each database. Finally, the course will present criteria and techniques for selecting NoSQL database instead of relational databases.

CIS627: Data Warehousing

This course introduces the concepts of database technology used in Business Intelligence. More precisely, the course will cover multidimensional warehouses and gives an introduction to methods and theory for development of data warehouses and data analysis. The course also focuses on problems posed by heterogeneous data integration and data quality and how to measure and maintain data quality in the context of data warehousing. Classical notions of data warehousing, OLAP, architecture, conceptual and logical design, query processing and optimization, efficient design and construction of data warehouse queries are also covered in the course.

CIS624: Research Project 1

This course provides students with an opportunity to gather the knowledge and skills learned from the program coursework and conduct a research project with industrial applications. Students are expected to conduct a review of research literature and develop a set of hypotheses for a research project in data science. A research project explaining the hypotheses and alternative remedies to the problem must be submitted to the faculty supervisor at the end of the semester. Students are evaluated based on their research project and oral presentation.

CIS625: Research Project 2

The research outlined in the CIS624 proposal must be completed during this course. The final report of the research findings and recommendations should be submitted to the advisor and the results presented. The results should have direct practical applications and/or be available for publication in refereed publications. Students are evaluated based on the submitted research and oral presentation.