Data Science and Artificial Intelligence

Bachelor in Data Science and Artificial Intelligence

The aim of this program is to provide graduates with in-depth understanding of key technologies, analytical tools, and methods in data science and artificial intelligence for extracting knowledge from complex data sets in various domains and effectively communicate the results to domain specialists.

Upon completion of the program, the graduates will be able to:

Demonstrate data science proficiency by analyzing large and complex datasets and produce useful insights.
Apply data mining strategies to extract meaningful information from large real-world datasets.
Implement deep learning algorithms to solve real-world problems.
Understand the concepts and applications of Natural Language Processing technologies and applications in Computer Vision.
Apply business intelligence techniques for information management, analysis, and decision-making for an enterprise.
Build machine learning models to discover patterns and extract meaningful insights from data.
Apply research and communication skills fostering ethical, legal and social responsibility.

Allotment to the Data Science and Artificial Intelligence is based on the CGPA scored by the student in Advance Diploma. Requirements to progress to Bachelor in Data Science and Artificial Intelligence:

Student must complete Advance Diploma in Data Science and Artificial Intelligence successfully with CGPA ≥ 2.5 in scale of 4.
IELTS band Score ≥5 or the FDL Mark should be ≥ 74 .
Lateral entry of students is governed by UTAS bylaws.

Reference: UTAS Bylaws and its Amendments (Reference: 1. MoMP Bylaws of College of Technology & its Amendments)

Big Data Engineer
Machine Learning Engineer
Data scientist
Business Intelligence Developer
Computer Vision Research Engineer
Data Analyst
Data Architect
AI Engineer
Research Scientist

Semester 1

UNEN1102 - English for Academic Purposes

CSCM1101 - Fundamentals of Computing and Information Systems

This course introduces students to various concepts of IT/IS including number systems, operating systems, networks, system development life cycle, algorithms, flow charts, and Information System infrastructure. It will also cover the use of various IT/IS systems in business organizations, careers, and certifications

CSDB1102 - Introduction to Database

This course enables the students to be familiarized with the importance of databases, core concepts of relational databases, techniques of database design and its refinement, and the concepts of non-relational databases.

CSWD1103 - Web Development I

This course introduces the practical knowledge and skills in writing markup language tags, designing style sheets, and scripting in order to design websites according to W3C standards, using the latest web tools and technologies. Fundamental Photoshop skills acquired and practiced in a manner that engages creativity and encourages experimentation.

CSCN1104 - Computer Networking Fundamentals

This course introduces the architectures, models, protocols and networking elements that connect users, devices, applications and data through the internet and across modern computer networks - including IP addressing and Ethernet fundamentals.

Semester 2

MATH1202 - Calculus I

Prerequisite:FPMP0003
This course is a first common math course at diploma level for all specializations except pharmacy and design; it plays an important role in the understanding of science, engineering, economics, computer science, and other disciplines. The course covers the quite familiar basic calculus concepts like limits and continuity, derivatives, optimization, definite integrals, exponential and logarithmic functions, trigonometric functions,and techniques of integration. The course contents are explored to the students through problem solving, to understand them better, and to improve the ability to apply them in real time

CSPG1205 - Introduction to Programming

Prerequisite:CSCM1101
This course provides fundamental programming concepts and techniques using high level programming language. In this course student will create programs which include variables, loops, decision making and different types of functions and data collections. Student will develop programs that can handle data files and include exception handling. In addition, the student will be able to produce a simple web application program.   

UNEN1203 - Technical Writing (English II)

CSOP1207 - Principles of Operating Systems

Prerequisite:CSCM1101
This course discusses the basic facilities provided by the operating system. Students will be familiarized with the functions of operating systems, including process management (processes, threads, context switch, concurrency control, synchronization, scheduling, deadlocks, etc.), primary memory management, virtual memory management, file systems, resource allocation, and information protection. Students will rewrite fundamental parts of the UNIX/Linux user space.

CSSY1208 - Introduction to Information Security

Prerequisite:CSCN1104
This course gives a comprehensive understanding of information security concepts and security services. Students will be familiarized with various types of security threats and attacks on systems and explore security measures that can be used for dealing with risks and security breaches in computer and network systems.

Semester 1

UNXX - General Requirement ( University Elective)

Uncs2215- Communication Skills
Unpr2217- Preparing for Work
Unwe2216- Working Ethics
Unis2206- Islamic Studies
Unoc2207- Oman Civilization and Man
Unct2210- Critical Thinking
Uncl2211- Chinese Language
UNFL2213- French Language
UNFL2215- German Language

CSSE2101  - Introduction to Software Engineering

Prerequisite:CSCM1101

CSDS2101 - Database Systems

Prerequisite: CSDB1102
This course prepares students to gain skills in creating, and using Structured (SQL) and Unstructured databases (NoSQL).

CSWD2101 - Web Development II

Prerequisite:CSWD1103
This course builds skills needed to develop functional and dynamic websites. Students will learn the client-server architecture, server-side scripts, database manipulation, and web security and authentication of web-based applications.

CSSE2203 - Data Structures and Algorithms

Prerequisite:CSPG1205
This course enables students to implement data structures and do analysis of algorithms. This course is designed to extend the knowledge of cognitive programming and optimization skills.

Semester 2

CSSE2202 - Object Oriented Programming

Prerequisite:CSPG1205
This course covers the basics of Object-Oriented Programming approach to provide great flexibility, modularity, and code reusability in developing computer programs. They will learn programming using objects and classes, abstraction and encapsulation, class inheritance, polymorphism, exception handling, abstract classes, and interfaces. The student will also learn the basics of Graphical User Interfaces (GUI) and event-driven programming.

MATH2101 - Mathematics for Computing

Prerequisite:MATH1200
This course covers the mathematical concepts required for modelling and enhancing mathematical skills to apply in some computer programs. The main objective of the course is to provide basic understanding of structure and construction of numerical algorithms using the following computational techniques: interpolation and approximation of functions, finding the roots of non-linear equations, Integers, applications of modular arithmetic, solution methods for matrix equations and basic introduction to vectors. This course emphasizes on the understanding of the accuracy, efficacy, efficiency and stability of the solutions obtained by the numerical methods.

UNEP2109 - Entrepreneurship

CSSE2204  - Special Topics

Prerequisite:CSSY2101
This course aims to introduce latest emerging and innovation technologies in the field of Computing. This can mean the use of a new programming language, development tool, a new process, a new design method, or targeting a new platform. This course is concerned with the most recent innovations in the field, with a view to bringing the students up to date with modern trends.

CSSE2205 - Diploma Course Project 

Prerequisite:CSSE2101 + { CSWD2101 OR CSPG1205}
In this course, students will apply the software engineering and programming concepts to develop a software application. the students will be able to develop an application for a real world requirement that has passed through the Software Development Life Cycle. It will give the students a valuable teamwork experience and communication skill.

Semester 1

UNEN3104 - Public Speaking

Prerequisite:UNEN1203

STAT3101 - Probability and Statistics

Prerequisite:MATH1202
This is a service course that is intended for students whose mathematical background is Calculus. The course follows a theoretical approach with significant examples of formal mathematical proofs. In addition, fundamental concepts are presented with intuitive examples along with practical examples using a statistical software. The course goal is to deliver an understanding of elementary statistics, fundamental concepts in probability theory, random variables, some discrete and continuous probability distributions, expectation, cumulative distribution function, probability mass function, and essential techniques of parameter estimation, tests of hypothesis, and regression analysis. After taking this course, students will be able to use a statistical package in performing data visualization, analysis, and inferences for small and large sample datasets.

CSDS3101 - Fundamentals of Big Data

Prerequisite:CSDS2101
This course prepares students to understand the fundamentals concepts of big data and its technologies. Students will learn the primary systems used in big data.

MATH3202 - Discrete Structures

Prerequisite:MATH2101
Each module has been designed in such a way that they are elaborated with both concepts and problems involving programs. The credit hours for each module are sufficient for completion of the topics. An oriented approach on practical problems and programming language adaptation will be dealt with some parts of the modules such as set theory, relations, recurrence relations and tree algorithms.  

CSPM3201 - Project Management and Acquisition

Prerequisite:CSSE2101 OR CSIS2101
This course provides in-depth discussions of project management principles and modern software project management practices as well as methodologies such as the Agile methodology. Students will also learn the concepts of acquisition, contract basics, and management skills to successfully deal with acquired technical resources. Through IT-based case studies and role-playing, they will learn about procurement and acquisition activities, develop acquisition strategies, and prepare different acquisition plans.

Major Elective 1

CSSE3104 - Computer Graphics and Games Development - Prerequisite: CSPG1205
CSSE3205 - Fundamentals of Robotics - Prerequisite: CSPG1205
CSSE4106 - DevOps and Continuous Delivery - Prerequisite:CSPM3201
CSSE4107 - Theory of Computation - Prerequisite: MATH3202
CSSE4208 - Innovation and Emerging Technologies - Prerequisite: None
CSNW3203 - System Administration - Prerequisite: CSOP1207
CSNW3202 - Cloud Computing Fundamentals - Prerequisite: NONE
CSIS3101 - Business Process Management - Prerequisite: CSSE2101 OR CSIS2101
CSIS3103 - User Experience Design - Prerequisite: None
CSIS3102 - IS Management and Strategy - Prerequisite: CSCM1101
CSSE3101 - Advanced Web Technologies - Prerequisite: CSWD1103
CSSY3202 - Web Applications Security - Prerequisite: CSWD2101
CSSY3205 - Authentication and Access Control - Prerequisite: CSSY1208
CSDS4111 - Introduction to Blockchain - Prerequisite: NONE
CSDS3205 - Data Visualization - Prerequisite: NONE
CSDS3105 - Database Programming - Prerequisite: CSDS2101
CSDS4212 - Information Retrieval - Prerequisite: CSDS3202
CSDS4210 - Pattern Recognition - Prerequisite: CSDS4104

Semester 2

CSDS3202 - Introduction to Data Science

Prerequisite:CSPG1205+ STAT3101
This course gives students a broad overview of the key steps in data science such as accessing, cleansing, exploring, analyzing, visualizing, and interpreting data.

CSDS3203 - Introduction to Artificial Intelligence

Prerequisite:CSSE2203
This course explores the foundational principles that drive artificial intelligence (AI) and Machine Learning techniques and practices implementing some AI approaches. Specific topics include search, knowledge representation and reasoning, constraint satisfaction problems, and machine learning. Students will be able to use the tools that allow use of AI in problems encountered in life.

CSRM3202 - Research Methodology

Prerequisite:STAT3101
This course enables students to use key concepts, methods, techniques, and tools in conducting research projects relevant to computing and information science field.

CSSE2203 - Data Warehouse Design

Prerequisite:CSDS2101
This course allows students to understand the principles of data warehouse. Also, to allow students to design, build and test a Data Warehouse solution. 

MATH3203 - Mathematics for Machine Learning

Prerequisite:MATH2101
This course is designed to provide mathematical theory and concepts to understand and build up skills to develop new algorithms of machine/deep learning. This course incorporates important topics from multi-variable calculus, Vector Differential Calculus and some advanced Linear Algebra topics like Matrix Factorization and singular value decomposition. The primary objective of this course is to provide prerequisite mathematics knowledge to understand advanced level machine learning concepts algorithms.

Major Elective 2

Semester 1

CSDS4101  - Big data Analytics  

Prerequisite:CSDS3101

This course allows students to adequately equip with the key industry accepted techniques and tools used in Big Data analytics. It allows students to investigate the big data analytic techniques for useful business applications. To Extract data from different sources including NoSQL database and prepare it for performing Analytics. To explore an open source software to perform exploratory Data Analytics.

CSDS4102 - Natural Language Processing

Prerequisite:CSDS3203

This course focuses on the techniques of NLP. The student will receive the necessary information to be able to apply concepts related to the effective current approaches, tools, and strategies for natural language processing with an emphasis on those that can be adapted to Python. Additionally, throughout this course, the students will be able to use large amounts of textual data to conduct large-scale statistical analyses in a reliable and authoritative manner, as well as identify useful patterns in the data

CSDS4105 - B-Tech Course Project I

Prerequisite:CSDS3203 + CSDS3202 + CSIS3609
This course allows students to identify and design academic project/research in the field of Data Science and Artificial Intelligence. It allows students to demonstrate the abilities and skills required to complete the design of a project/research. It gives students the skills of Organizing and presenting the project/ research work in a consistent approach according to the requirements. It teach students to exhibit interpersonal skills and ethics.

CSDS4103 - Business Intelligence

Prerequisite: CSDS3101
This course focuses on Business Intelligence (BI) which refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The course allows students to understand the goal of the organization and decision making process, and enable data analysis using data visualization.

CSDS4104 - Applied Machine Learning

Prerequisite: CSDS3203 + CSDS3202
This course focuses on the techniques of machine learning. The student will receive the necessary information to be able to apply concepts related to classification and regression, and other advanced learning techniques to solve problems in several application areas. Additionally, the course will discuss the recent applications of machine learning, such as those that deal with text and images.

Major Elective 3 & 4

Semester 2

CSDS4206 - Computer Vision  

Prerequisite: CSDS4104
This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, and recognition.

CSDS4207 - Data Mining

Prerequisite: CSDS3202
This course allows students to gain knowledge of cutting-edge techniques and algorithms for mining knowledge from various data repositories. The course will examine the current techniques, as well as the theories that support them, and explore improved strategies that can be applied to real-world scenarios, including text, and social networks. As a result of the course arrangement, participants will be encouraged to actively participate in class discussions, think creatively, and engage in hands-on projects.

UNAB4105 - Arabic Language Skills

CSDS4208 - Deep Learning

Prerequisite: CSDS4104
This course covers the connectionist architectures commonly associated with deep learning, e.g., basic neural networks, convolutional neural networks and recurrent neural networks. In addition, this course covers different regularization and optimization techniques applied on different deep learning models to improve the performance. In the practical sessions, deep learning models will be applied in different artificial intelligence applications using real word datasets.

CSDS4209 - B-Tech Course Project II

Prerequisite:CSDS4105
This course allows students to implement the academic project /research undertaken in B.Tech. Course Project I. It allows students to demonstrate the abilities and skills required to complete the design of a project/research. It gives students the skills of Organizing and presenting the project/ research work in a consistent approach according to the requirements. It teaches students to exhibit interpersonal skills and ethics.

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