Informatics program
Master
Levels
6
Courses
13
Credits
33
Number of students
0
Overview
The M.Sc. in Data Science program at Jouf University is designed to equip students with advanced knowledge and practical skills in data science, preparing them for careers in various industries such as technology, healthcare, finance, and academia. This program emphasizes statistical analysis, programming, data visualization, machine learning, and big data management. Students engage in both theoretical learning and hands-on projects, focusing on the extraction of knowledge from data, algorithm development, and the implementation of innovative solutions to real-world problems. Graduates are expected to meet the increasing demand for data science professionals, contributing to data-driven decision-making and the advancement of knowledge-based economies, in alignment with Saudi Arabia's Vision 2030. Through rigorous coursework and research opportunities, the program aims to develop experts capable of handling complex datasets, designing integrated solutions, and effectively communicating data insights for business and societal benefit
Program content
Admission requirements
Program levels
المستوى الاول
IS 612 - Programming for Data Science - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course introduces students to essential programming concepts and tools required for data science. It focuses on developing proficiency in programming languages such as Python or R, commonly used for data analysis, visualization, and machine learning. Students will learn to write efficient code, manipulate data, perform exploratory data analysis, and create visualizations to uncover insights. Key topics include data structures, data cleaning, working with libraries like Pandas and NumPy, basic statistical programming, and integrating machine learning models. By the end of the course, students will have the skills to write robust code, automate workflows, and solve real-world data science problems using programming.
IS 611 - Statistics for Data Science - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course provides a solid foundation in statistical concepts and techniques essential for data science and machine learning. Through a combination of theory and practical applications, students will learn to analyze, interpret, and visualize data effectively. Topics include descriptive statistics, probability, hypothesis testing, confidence intervals, regression analysis, and advanced techniques like ANOVA and time series analysis. The course emphasizes the use of statistical programming tools like Python or R for real-world data analysis. By the end of the course, students will be equipped to extract meaningful insights from data, assess the quality of data-driven models, and apply statistical reasoning to solve complex problems.
المستوى الثاني
IS 614 - Applied Machine Learning - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course provides an in-depth study of machine learning, covering key concepts and techniques such as regression, classification, clustering, and dimensionality reduction. Topics include data preprocessing, feature engineering, model evaluation, and hyperparameter tuning, with a focus on implementing models using tools like Scikit-learn, TensorFlow, or PyTorch. Students will explore applications across various domains, gaining the knowledge needed to develop and evaluate machine learning solutions for data-driven decision-making.
IS 613 - Data Visualization - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course provides a comprehensive introduction to data visualization, focusing on the principles and techniques required to transform raw data into meaningful and impactful visual representations. It covers the entire process of designing and creating visualizations, starting from understanding data characteristics and extending to selecting appropriate visualization methods for different types of data. Students will engage in hands-on activities to explore patterns, trends, and relationships in data, develop effective visual storytelling techniques, and convey insights that support data-driven decisions. The course emphasizes practical applications, equipping students with the skills necessary to create dynamic and interactive visual tools for both exploration and communication in real-world scenarios.
المستوى الثالث
IS 621 - Statistical Methods for Discrete Response And Time Series - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course provides a comprehensive introduction to statistical techniques for analyzing discrete response data and time series. It covers methods for modeling and interpreting categorical outcomes, such as logistic regression and Poisson regression, alongside techniques for understanding temporal dependencies in data, including autoregressive, moving average, and state-space models. Students will explore foundational concepts like stationarity, seasonality, and model diagnostics, as well as advanced topics such as forecasting and intervention analysis. The course emphasizes practical applications, equipping students with the skills to apply these methods to real-world datasets using statistical software. By the end of the course, students will be prepared to analyze complex data structures involving both discrete responses and time-dependent patterns.
IS 622 - Data Systems - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course focuses on specialized systems and algorithms designed for large-scale data management and analysis. Topics include parallel database systems, graph systems, and streaming systems. Students will explore data management and analysis using Hadoop, Hive, and Spark, alongside the design and use of Spark systems for data analysis and machine learning. The course also covers column-store database management systems, big data system architectures, graph processing systems, and stream processing system design, providing a comprehensive understanding of cutting-edge technologies in big data processing.
IS 615 - Big Data Analysis - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course delves into the challenges and opportunities of big data, focusing on the storage, organization, and processing of massive datasets that exceed the capabilities of traditional information technologies. Students will explore cutting-edge algorithms, techniques, and tools essential for efficient big data management and processing. The course also examines real-world applications requiring large-scale data analysis, highlighting how they can be implemented and optimized on modern big data platforms. Through hands-on projects and case studies, students will gain the practical skills necessary to harness the power of big data in diverse domains.
المستوى الرابع
IS 625 - NoSQL Database Systems - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course explores the fundamentals of NoSQL databases, designed to handle unstructured data and meet the performance, scalability, and flexibility demands of data-intensive applications and big data processing. Students will learn about key NoSQL systems, including key-value stores, graph databases, and document databases. The course focuses on practical skills for working with MongoDB, covering topics such as document management, data querying, indexing, aggregation techniques, and sharding, providing students with the tools to develop scalable and efficient data-driven applications.
IS 616 - Generalized Linear Models - mandatory
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course focuses on the analysis of linear and non-linear effects of continuous and categorical predictor variables on discrete or continuous dependent variables using Generalized Linear Models (GLMs). Students will explore how the structural form of these models describes patterns of interactions and associations, while the model parameters quantify the strength of these relationships. Emphasis is placed on estimating model parameters and applying foundational inference tools, such as point estimation, hypothesis testing, and confidence intervals, to interpret and validate results. Through practical applications, students will gain the skills to effectively use GLMs for analyzing complex data relationships in real-world scenarios.
IS 624 - Selected Topics in Data Science - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course intends to introduce special topics of current trends in data science and. Topics covered in this course should be approved by the department council. These topics cover a range of foundational, intermediate, and advanced topics to provide students with a comprehensive understanding of the field of data science. Such possible topics include: feature engineering, time series analysis, natural language processing (NLP), big data technologies, incorporating tools like Spark, Hadoop, and transformer-based models such as BERT and GPT, explainable AI (XAI), causal inference, reinforcement learning, sustainability-focused data science, geospatial analysis.
المستوى الخامس
IS 626 - Data Engineering - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This course focuses on analyzing complex, real-world datasets to make predictions using statistical and machine learning methods. It integrates five essential facets of data investigation: (1) Data Collection – wrangling, cleaning, and sampling to prepare suitable datasets; (2) Data Management – ensuring fast and reliable data access; (3) Exploratory Data Analysis – generating hypotheses and building insights; (4) Prediction and Statistical Learning – applying models to make accurate predictions; and (5) Communication – presenting findings effectively through visualizations, narratives, and interpretable summaries. The course emphasizes practical, hands-on approaches to mastering these critical data science skills.
IS 623 - Deep Learning - optional 1
Credits
3
Theoretical
3
Pratical
Training
Total Content
3
Prerequisite
Course Description:
This advanced course focuses on recent developments in deep learning with neural networks, including CNNs, RNNs, and Deep Reinforcement Learning networks. The course emphasizes applications in computer vision and natural language processing (NLP), where neural networks have driven significant advancements, sparking both academic and commercial interest. Students will explore the mathematical foundations of these models and their associated optimization algorithms. Topics include applications in NLP, such as analyzing latent dimensions in text, language translation, and question answering, providing a thorough understanding of cutting-edge techniques in deep learning.
المستوى السادس
IS 698 - Research Project - mandatory
Credits
6
Theoretical
6
Pratical
Training
Total Content
6
Prerequisite
Course Description:
This is an advanced research project conducted individually under the guidance of an academic supervisor. It gives students the chance to investigate and contribute to an area at the cutting edge of data science. As part of the project students will present their work to an audience and write a major report detailing their results. Project topics vary from year to year depending on staff availability and research focus.