ماجستير علم البيانات

Mission

Given the growing needs in industrial, the mission of the Master of Science in Data Science program is to graduate students with a solid knowledge in data science and preparing them to compete successfully for jobs in high-demand D.S. industry.

Educational Objectives

  • The Objectives of the Master of Science in Data Science are:
  • demonstrate knowledge of statistical data analysis techniques utilized in decision making.
  • apply principles of Data Science to the analysis of business problems.
  • prepare students to understand and apply data science techniques and algorithms in achieving organizations missions
  • to be able to solve problems involving large, diverse data sets from different application domains
  • develop programming skills in data analytics, visualization, learning and mining.
  • to predestine substantially trained specialists in data science for the Saudi industry.
  • Prepare students for career advancement in all areas of information science and technology

Learning Outcomes

 

At the end of the program, graduates should be able to:

develop an effective Big Data solution in a real environment

apply computing theory, and algorithms, as well as mathematical and statistical models, to appropriately formulate and use data analyses. 

carry out a cost-benefit analysis

use appropriate models of analysis, from results, and investigate potential issues

obtain a clean data process and transformation data

Program Design

The Master program of Science in Data Science 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 47 credit hours by both courses and a project.

Detailed Program Design

With the approval of a supervising professor, qualified students may be admitted to the program. The students of the Master of Science in Data Science must complete 12 courses, 2 research projects (Research Project 1 and Research Project 2 ) and an internship in one of well-known organizations

In particular, students must complete minimum 47 credit hours, including:

At least 39 course credits that include:

36 course credits of core courses.

3 elective course credit.

8 Project credits: Research Project 1 (4 credits) and Research Project 2 (4 credits).

Master of Science in Data Science: Study Plan

Semester One

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

 

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

أساسيات علم البيانات

3

3

-

-

CIS601

Fundamentals of Data Science

الرياضيات الحاسوبية

3

3

-

-

CSC602

Computational Mathematics

البرمجة لعلم البيانات

3

3

-

-

CIS605

Programming for Data Science

مواضيع متقدمة في قواعد البيانات

3

3

-

-

CIS604

Advanced Topics in Databases

المجموع

12

12

-

 

 

 

 

 

Semester Two

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

التعلم الآلي

3

3

-

CSC602

CSC603

Machine Learning

طرق بحث

3

3

-

-

CIS608

Research Methods

تنقيب البيانات

3

3

-

-

CSC617

Data Mining

عرض البيانات

3

3

-

CIS605

CIS606

Data Visualization     

المجموع

12

12

 

 

 

 

 

 

 

 

 

 

Semester Three

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

أنظمة استرجاع المعلومات

3

3

-

CSC602

CIS610

Information Retrieval

تحليل البيانات الضخمة

3

3

-

CIS604

CIS611

Big Data Analytics 

سمنار في علم البيانات

3

3

 

Completion of 18 credit hours

CIS615

Seminar in Data Science

مشروع بحثي 1

4

4

-

Completion of 21 credit hours

CIS624

Research Project 1

المجموع

13

13

 

 

 

 

 

 

Semester Four

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

قضايا علم البيانات المهنية والأخلاقية

3

3

-

completion of 33 credit hours

CIS620

Data Science Professional and Ethical issues

مقرر اختياري

3

3

-

Completion of 30 credit hours

-

Elective Course

مشروع بحثي 2

4

4

-

CIS624

CIS625

Research Project 2

المجموع

10

10

 

 

 

 

 

 

 

 

 

Elective Courses

المقـــــــــــــرر

Total Credits

Credits

Prerequisite

المقابل اللغوى

Th.

Pr.

Code/No

Course Title

 

 

 

 

Completion of 30 credit hours

 

 

الشبكات العصبية والتعلم العميق

3

3

-

CSC604

Neural Networks and Deep Learning

البيانات الحيوية

3

3

-

CSC615

Bioinformatics

الحوسبة المتوازية

3

3

-

CSC618

Parallel Computing

أنظمة دعم القرار

3

3

-

CIS621

Decision Support Systems

مواضيع مختارة في علم البيانات

3

3

-

CIS622

Selected topics in Data Science

 

 

 

 

Courses

 

Core Courses:

Code

Course Title

Credits

Prerequisite

CIS 601

Fundamental of Data Science

3

None

Description

The Data Science course concentrates on techniques and methods needed to in the Data Science project life-cycle, 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 no-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.

 

 

Code

Course Title

Credits

Prerequisite

CIS 604

Advanced Topics in Databases

3

None

Description

This course emphases on 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 emphases on web technology and DBMSs, semi-structured data and XML. In Particular, the related business intelligence topics such data warehousing concepts, OLAP, and data mining also covered and emphasized. 

 

Code

Course Title

Credits

Prerequisite

CSC602

Computational Mathematics

 

3

None

Description

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 and 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.

 

Code

Course Title

Credits

Prerequisite

CIS605

Programming for Data Science

3

None

Description

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.

 

 

Code

Course Title

Credits

Prerequisite

CSC603

Machine Learning

3

CSC602

Description

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.

 

 

Code

Course Title

Credits

Prerequisite

CSC617

Data Mining

3

CSC641

Description

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.

 

 

Code

Course Title

Credits

Prerequisite

CIS608

Research Methods

3

None

Description

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.

 

Code

Course Title

Credits

Prerequisite

CIS606

Data Visualization     

3

CIS605

Description

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

 

Code

Course Title

Credits

Prerequisite

CIS611

Big Data Analytics 

3

CIS604

Description

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. 

 

 

 

 

 

Code

Course Title

Credits

Prerequisite

CSC610

Information Retrieval

3

CSC602

Description

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.

 

 

Code

Course Title

Credits

Prerequisite

CIS615

Seminar in Data Science

3

completion of 18 credit hours

Description

The Seminar in Data Science course 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. Every student will be given a real case study and work on it during the course to apply the knowledge they gain during the course and present the finding.

 

 

 

Code

Course Title

Credits

Prerequisite

CIS690

Data Science Professional and Ethical issues

3

completion of 33 credit hours

Description

The objective of this module is to highlights 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

 

 

 

Elective Courses:

Code

Course Title

Credits

Prerequisite

CSC615

Bioinformatics

3

completion of 30 credit hours

Description

This course provides an introduction to key concepts and methods in bioinformatics. Emphasis will be put on efficient algorithms and techniques used in common applications for the analysis of genetic sequences. Topics covered: comparison and alignment of two or more sequences, indexing and searching of sequence databases, motif discovery, searching with sequence patterns, gene prediction as well as mapping and assembly of data from genome sequencing. Necessary basic knowledge of molecular biology will be communicated throughout

 

 

Code

Course Title

Credits

Prerequisite

CSC616

Self-driving Vehicles

3

completion of 30 credit hours

Description

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. They depend on Deep Learning algorithms and they create new opportunities in the mobility sector. This course is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.

 

Code

Course Title

Credits

Prerequisite

CSC618

Parallel Computing

3

completion of 30 credit hours

Description

This course provides an introduction to the field of computational linguistics, also called natural language processing (NLP) - the creation of computer programs that can understand and generate natural languages (such as English). We will use natural language understanding as a vehicle to introduce the three major subfields of NLP: syntax (which concerns itself with determining the structure of an utterance), semantics (which concerns itself with determining the explicit truth-functional meaning of a single utterance), and pragmatics (which concerns itself with deriving the context-dependent meaning of an utterance when it is used in a specific discourse context). The course will introduce both linguistic (knowledge-based) and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems.

 

Code

Course Title

Credits

Prerequisite

CIS621

Decision Support Systems

3

completion of 30 credit hours

Description

The objective of this course to provide learns with fundamental knowledge on decision support systems for

managers and IS developers. This course explores topics in computer-based Decision Support Systems with a practical focus on the application of information technology to the solution of management problems. Topics include Management Support Systems, decision making systems, Data .mining for business and intelligent system.

 

 

Code

Course Title

Credits

Prerequisite

CSC 604

Neural Networks and Deep Learning

3

completion of 30 credit hours

Description

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.

 

Code

Course Title

Credits

Prerequisite

CIS622

Selected topics in Data Science

3

completion of 30 credit hours

Description

The objective of this module is to highlights 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.

 

 

Research Projects:

Code

Course Title

Credits

Prerequisite

CIS624

Research Project 1

4

completion of 21 credit hours

Description

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.  

 

Code

Course Title

Credits

Prerequisite

CIS625

Research Project 2

4

CIS624

Description

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.