Bachelor of Science in Data Science and Artificial Intelligence

This course is an overview of expository writing, including the development and revision of paragraphs and essays using various rhetorical strategies, as well as reading and discussion of selected essays, short stories and poems. In addition, the course introduces writing about literature, incorporation and documentation of material from primary sources.

*This course is aligned with the CSUN Curriculum.

Course Syllabus

This course introduces the conventions of research writing and teaches the process of producing well-planned research papers using critical thinking and analytical skills in response to a variety of academic texts. The course is designed to provide guidance in all steps of the research process including choosing a topic, designing a research methodology, analyzing data, and writing up and presenting results.

Prerequisite: ENGL 101

*This course is aligned with the CSUN Curriculum.

Course Syllabus

ARHG 101 Arabic for non-Arabic speakers (3 credits)

This course develops reading and writing skills in Modern Standard Arabic with active speaking and listening skills in both formal Arabic and various Arabic dialects. Authentic materials from the Arabic media will be used in addition to text-related video and audio materials.

Prerequisites: None

ARHG 104 Arabic for Arabic Speakers (3 credits)

A practical language course which aims at developing the language skills of native speakers of Arabic. This course provides the students with a comprehensive knowledge of the linguistic system. It is intended to help learners reach a superior level of proficiency by expanding vocabulary and providing paragraph-level activities in reading, writing, and speaking; through a selection texts by writers from across the Arab world address literary themes and represent a range of genres, styles, and periods, where each text is followed by exercises that measure understanding and comprehension, vocabulary and language applications, including grammar, morphology, spelling, stylistic applications and composition.

Prerequisites: None

The course covers the Modern History of Bahrain since 1500 till 2002. It contains: Chapter I: Introduction geography. Chapter II: A Short History of Bahrain until the beginning of the sixteenth century. Chapter III: Bahrain between European and regional ambitions. Chapter IV: Utub tribe and the establishment of political entities in the Arabian Gulf. Chapter V of Bahrain and British protection. Chapter VI: Bahrain after independence.

Prerequisites: None

Course Syllabus

This course covers human rights historical development, major human rights laws, treaties, and conventions. To learn obligations as citizens and residents of Bahrain as well as a member of the international community and to understand Human Rights Enforcement Mechanisms in Bahrain.

Prerequisites: None

Course Syllabus

This course aims to enhance understanding of concepts and the development of problem-solving skills in the areas of single variable differential calculus and single variable integral calculus. Topics include limits, differentiation, curve sketching, optimization, and introductory integration. Functions studied range from simple algebraic and radical expressions to more sophisticated rational, logarithms, exponentials, and trigonometric functions.

Prerequisites: MATH 099 or placement exam score

Course Syllabus

This course involves applications and techniques of integration, including substitution, by parts, trigonometric substitution, and by partial fractions. The course also introduces improper integrals, numerical integration, sequences and series, geometric series formula, criteria for convergence, power series, and Taylor expansion.

Prerequisites: MATH 153

Course Syllabus

This course is designed to provide students with the core competencies of computing literacy and computational thinking, which are essential skills in the digital information age. The course provides an overview of computer systems—hardware, software and networks. Students will practice using essential computing programs, and will develop computational solutions to basic problems. The course also covers social and ethical issues related to computing.

Prerequisites: None

Course Syllabus

This course is designed to help students function as independent learners within a university environment. The course teaches students various transferable study skills, including time management, dealing with group projects, test preparation, and critical reading. It also intends to raise students’ understanding of themselves as learners.

Prerequisites: None

Course Syllabus

This course introduces the fundamental concepts of Object-Oriented Programming (OOP). Students will learn how to design, implement, and test software using the OOP paradigm. The course covers topics such as encapsulation, inheritance, polymorphism, abstraction, and object-oriented design patterns. The course will be taught using a programming language.

Prerequisites: COSC 101

Course Syllabus

This course focuses on systems of linear equations and matrices, Gauss elimination, matrices, determinants vectors in 2- and 3-dimensional space, norm, dot product, cross product, lines, planes, Euclidean vector spaces, general vector spaces, and matrix diagonalization.

Prerequisites: MATH 153

This course focuses on logic, methods of proof, set theory, number theory, equivalence and order relations, counting (combinations and permutations), and solving recurrence relations.

Prerequisite: MATH 151 or MATH 153

Data structures are essential building blocks for designing efficient algorithms. This course will introduce the fundamentals of data structures and will provide a thorough understanding of how to systematically organize data in a computer system. In addition, this course will introduce students to analytical tools for comparing data structures in terms of their time and space complexities. Students will appreciate the importance of programming structures, abstractions, and algorithms for improving the efficiency of computer programs. Topics include linked lists, stacks, trees, queues, graphs and analysis of efficiency. The course also covers searching, sorting, and hashing techniques.

Prerequisite: COSC 102

Digital technology has become the core business of almost every manufacturing industry. This course gives an overview of circuitry, logic, and system design for understanding the impact of electrical and computer engineering solutions in a global, economic, and societal context. It focuses on modeling, analysis, and design of digital systems, primarily at the logical design level.

Prerequisite:  MATH 153

This course focuses on practical modelling, analysis, and design of digital systems, primarily at the logic design level. Digital electronic topics include: the basic logic gates, Boolean algebra, number systems, digital arithmetic, combinational logic circuits, multiplexers, decoders and flip-flops, counters, and registers

Co-requisite: CMPE 270

In our lifetimes, we have seen unprecedented expansion of computational capabilities fueled both by advanced processing and architectural innovations to exploit that processing capability. As a result of these capabilities automatic computation is having a huge impact on the way we live, work, communicate, and especially how we do science and engineering. This course examines in-depth the inner-workings of modern digital computer systems and the tradeoffs present at the hardware-software interface. It provides insights in the design process of complex hardware systems. A digital design background is considered fundamental, and it is mandatory.

Prerequisites: COSC 102 and CMPE 270

This course is an introduction to probability and statistics. It emphasizes on operations of sets, counting problems, definition of probability, conditional probability, Bayes’ theorem, one- and two- dimensional random variables, mathematical expectation and variance, basic discrete and continuous probability distributions, moment generating functions, law of large numbers, and central limit theorem. It also includes aspects of descriptive statistics, statistical intervals, hypothesis testing and simple linear regression and correlation.

Pre-requisites: MATH 152 OR MATH 154

This course introduces the fundamental concepts necessary for designing, using, and implementing database systems and database applications. It will teach students about data modeling techniques, relational database design, use of normalization to design normalized relational databases, Structured Query Language’s (SQL), data definition (DDL), data manipulation (DML), and web database development.

Prerequisite: MATH 203 and COSC 125

This course provides an in-depth understanding of modern operating systems, covering core concepts and principles. Key topics include operating system structures, process management, thread management, CPU scheduling, process synchronization, deadlock handling, memory management, virtual memory, mass storage, and file system management. The course aims to teach students about the operating system’s role as an interface between the user and computer hardware, focusing on both high-level functionality and low-level implementation details of CPU scheduling, processes, memory management, file system management, and virtualization. It emphasizes how operating systems act as a critical bridge in computer systems, delving into the intricate workings of these components at a detailed level. 

Prerequisite: CMPE 271

This course covers the Unix system administration and practical concepts and principles that underlie modern operating systems including Processes, Threads, Synchronization, Inter-Process Communication, Deadlock and Disk Scheduling. 

Co-requisite: COSC 372

This course provides students with an overview of programming, problem-solving, testing and debugging. It explores many fundamental programming concepts with emphasis on applying theoretical knowledge to a practical situation. It introduces the fundamental concepts of a computer system, programming languages, and provides a comprehensive introduction to programming for engineering and computer science students. It is designed as an introduction to programming and programming language (Java) for students who have no or very little programming knowledge and experience.

Prerequisite: Passing the Math Placement Test or MATH 099

This course provides students with an overview of Software Engineering, introducing theory and practical exercises with main focus on practical work in teams and individually. Concepts and techniques for systems engineering, requirements analysis, design, implementation and testing of computer systems. Principles of software engineering for production of reliable, maintainable and portable software products. Emphasis on object-oriented analysis and design techniques. This is a lecture portion of a course in software engineering involving the design and partial implementation of a software system as a group project. 

Prerequisite: COSC 125

This course provides students with the knowledge and skills required to start and manage their own technology-based ventures. The course covers topics such as ideation, business model development, market research, intellectual property, and funding. The course is designed to provide students with an understanding of the entrepreneurial process and to develop their skills in identifying, evaluating, and pursuing entrepreneurial opportunities. 

Prerequisite: ENGR 205

The course covers topics on technical analysis and runtime algorithms, asymptotic notation, some data structures such as graphs, sorting and searching algorithms and algorithm design techniques. The course covers the application of standard algorithmic approaches, including greedy, divide and conquer, and dynamic programming. Students will also learn about basic graph algorithms and NP-completeness. 

Prerequisite: MATH 203, COSC 125

This course serves as an introduction to the multifaceted field of Data Science, providing students with foundational knowledge and skills necessary for working with data, in addition to foundational concepts in machine learning. The course covers essential concepts, techniques, and tools employed in the data science workflow, including data collection, cleaning, exploration, analysis, and visualization. Students will gain hands-on experience with popular data science tools and languages, fostering an understanding of how data science is applied in various domains. 

Prerequisite: MATH 260, COSC 102 

This is a specialized course focusing on the ethical considerations and challenges within the field of data analytics. This course delves into the complex intersection of data, technology, and ethical decision-making. Students will explore the ethical implications of data collection, analysis, and utilization, with a particular emphasis on maintaining privacy, ensuring fairness, and addressing societal impacts. 

Corequisite: DSAI 310

This is a specialized course focusing on the principles, techniques, and applications of data visualization. In an era of vast and complex datasets, effective data visualization is essential for conveying insights and supporting decision-making. This course explores various visualization tools, design principles, and methods for creating compelling visual representations of data. 

Prerequisite: DSAI 310

This laboratory-based course provides students with practical experience in applying software engineering principles and techniques to real-world scenarios. Students will work individually and in teams to engage in various software engineering activities, including requirements analysis, design, implementation, testing, and maintenance of software systems. Through a series of guided projects and exercises, students will develop skills in problem-solving, critical thinking, and effective collaboration within a software engineering context. This laboratory course serves as a valuable opportunity for students to gain practical insights into the application of software engineering concepts and refine their skills in building reliable and maintainable software products. 

Co-requisite: SWEN 360

This course provides an overview of the fundamental concepts and principles of cybersecurity. The course covers topics such as information security, network security, and cybersecurity policies and practices. The course is designed to provide students with an understanding of the importance of cybersecurity in modern society and the tools and techniques used to protect against cyber-attacks. 

Prerequisites: CMPE 215

Artificial Intelligence is a growing area of research and application. This course gives an overview of Artificial Intelligence concepts and techniques, with a focus on solving real-world computational problems. Topics include models of knowledge representation, search-based and knowledge-based problem solving, natural language processing and generative AI. By the end of the course, students will be able to understand and apply appropriate AI algorithms for several problem domains. 

Prerequisites: DSAI 310

This course is a broad and comprehensive introduction to computer vision. The course covers computational techniques and methods for extracting meaningful information from visual inputs. Topics include image analysis, object detection, image segmentation, feature extraction and pattern classification. Students will also be introduced to modern real-world applications of computer vision in various fields such as robotics, graphics, medicine and manufacturing. 

Prerequisite: MATH 260, COSC 125

This is an advanced course that delves into the intricate aspects of computational statistics. It is designed for students seeking a deep understanding of advanced statistical techniques and their computational implementations. Topics include advanced probability distributions, Bayesian statistics, Monte Carlo methods, and advanced statistical modeling. 

Prerequisite: DSAI 310

This is an advanced course that provides an in-depth exploration of the principles, technologies, and practical applications of Big Data. As organizations grapple with the challenges posed by massive volumes of data, this course equips students with the skills to navigate and harness the power of Big Data technologies. The curriculum encompasses key concepts in distributed computing, storage systems, and data processing frameworks. 

Prerequisite: DSAI 310

This course is designed to prepare students for a Data Science Professional Certificate. The course delves into the fundamental concepts, methodologies, and practical applications of data science. It covers a range of topics related to data science and artificial intelligence, including data analysis, machine learning, and data visualization. The course also includes hands-on laboratory exercises and real-world case studies to provide students with practical experience in data science and artificial intelligence. 

Corequisite: Senior Level (90 Credits) 

This is a supervised internship course that provides data science and artificial intelligence students with the opportunity to gain practical, hands-on experience in a professional work environment. The internship allows students to apply and further develop their knowledge and skills acquired throughout their academic studies. Under the guidance of industry professionals and academic supervisors, students engage in real-world projects, tasks, and responsibilities relevant to the field of data science and artificial intelligence. 

Prerequisites: Senior standing (completing 90 credits), CGPA 2.0

This course is a project-based course that allows students to apply their knowledge and skills in data science and artificial intelligence to a practical project. The course focuses on developing a data science and artificial intelligence solution to a real-world problem, and students work in teams to design and implement a comprehensive data science solution. 

Prerequisite: Senior Level (90 credits), CGPA 2.0, SWEN 360/L 

This course is a continuation of Data Science Design Project A, where students continue to work on their data science and artificial intelligence project from the previous course. The focus of the course is on implementing and testing the data science and artificial intelligence solution designed in data science Design Project A, as well as evaluating the performance and effectiveness of the implemented solution. 

Prerequisite: DSAI 499A

This is an advanced course that delves into the field of Data Mining, exploring techniques and methodologies for discovering patterns, trends, and valuable insights within large datasets. The course covers a range of data mining algorithms, statistical models, and machine learning approaches to extract meaningful information from complex data sources. Students will engage in hands-on projects to apply data mining techniques in various domains and gain practical experience in uncovering hidden knowledge from diverse datasets. 

Prerequisite: DSAI 310 

This course introduces the concepts and methods of IoT analytics, which is the analysis of data generated by connected devices and sensors in the Internet of Things (IoT). The course covers topics such as IoT data sources, types, and characteristics, IoT data collection, storage, and processing, IoT data quality, security, and privacy, IoT data analysis techniques and tools, IoT data visualization and communication, and IoT data applications and use cases. The course provides hands-on experience with various IoT data sets, platforms, and software. 

Prerequisites: DSAI 310 

This course introduces the concepts and methods of sports analytics, which is the use of data and quantitative techniques to analyze performance and make decisions in sports. The course covers topics such as measuring and predicting player and team performance, decision-making and strategy in sports, artificial intelligence, and machine learning in sports. The course also provides hands-on experience with various data sources, tools, and software for sports analytics. 

Prerequisite: DSAI 310

This is an advanced course that provides an in-depth exploration of concepts and methods of marketing analytics. Students will learn how to apply data science techniques to measure and optimize marketing performance and outcomes. The course covers topics such as customer analysis, segmentation, targeting, preference measurement, demand estimation, customer lifetime value, retention, price optimization, advertising effectiveness, A/B testing and personalization. The course provides hands-on experience with various data sources, tools, and software for marketing analytics. 

Prerequisite: DSAI 310 

The course aims to provide students with a comprehensive understanding of the principles, data sources and methods of social and behavioral data analytics. Students will learn to use data science methods to obtain digital social data and answer research questions in psychology and social sciences. The course will cover topics such as research design, data acquisition through APIs and web scraping, data cleaning and processing, data analysis with advanced techniques, and the interpretation and presentation of results. The course will also focus on research integrity and ethics in social data science projects. 

Prerequisite: DSAI 310 

This Business Intelligence course provides an in-depth exploration of BI’s role in modern organizations, focusing on practical applications and current technologies. Students will engage with BI tools and advanced techniques, developing skills in data visualization and predictive analytics. The syllabus covers real-time analytics, mobile BI applications, and cloud computing in BI, enhancing students’ understanding of dynamic data analysis and decision-making processes. Additionally, the course addresses data quality management, ethical considerations, and BI governance, preparing students to effectively manage and utilize BI systems across various industries. Through practical exercises and industry-specific case studies, the course equips students with the skills to apply BI concepts effectively, ensuring a comprehensive and practical understanding of Business Intelligence in a real-world context. 

Prerequisites: CMPE 465