University Requirements Student must complete 19 credit hours

Course Code	Course Name	Credit Hours	Prerequests
10032100	Remedial English	0
Remedial english (e10032100) is a three-hour non-credited english course offered to studentswho score poorly (i.e. below 50%) on the placement test. since the major concern of this courseis to improve the students proficiency before starting their ordinary university english basiccourses and major courses taught in english, special emphasis has been placed on enhancing thestudents ability to effectively acquire the four language skills: reading, writing, listening, andspeaking. specifically, the course attempts to ensure an academically acceptable performance onthe part of the students at the level of the english basic courses. moreover, the course aims atexpanding students vocabulary needed for various tasks.
11000101	Islamic Culture	3
This course aims to establish the concept of islamic culture and its position among the other international cultures, its position in the muslim life, its sources, its bases and its characteristics. it also aims to introduce the islamic culture in faith, worship, relations, morals, and knowledge, to discuss the clash between cultures in addition to globalization, human rights, woman rights, democracy and other contemporary issues.
11000102	Arabic Language	3
This course aims to improve the level of students in language skills and various literary, read and absorb and express written, and oral and tasted literary, through texts flags authors and poets in different eras, lessons in grammar and spelling, and brief definition months dictionaries and arab old ones the modern and how to use them. this course aims to implement the arabic language in the areas of reading and expression of both types oral and written communication.
11000103	English Language I	3
University english i (e11000103) is a three credit-hour university-required english language course designed for students who need to work on the four skills of the language: reading, writing, listening, and speaking. the development of vocabulary and skills of comprehension are integral parts of the course. in addition, various reading strategies (making predictions, identifying main ideas, reading for details, relating information in the text to life experience) are introduced and developed through a wide range of topics for reading and writing. the course encourages a more analytical and independent approach to study and helps prepare the students for any subsequent exam preparation.
11000105	Palestinian Studies	3
The course is mandatory for university students from various disciplines, so it provides students with knowledge and `information about the palestinian reality and in particular the political developments of the palestinian cause since its inception until the present day in line social and economic developments and political which constitute the main pillars for the study of the palestinian political reality. this course aims to study the palestinian issue from its beginning until present in social, economic and political issues.
11000109	Community Service and Sustainable Development	1
This course aims to connect university students with charitable, community, and public institutions, while also enhancing students role towards society and familiarizing them with humanitarian needs by providing assistance to targeted groups. it seeks to improve the living conditions of marginalized and impoverished populations. the course prioritizes achieving the greatest possible number of sustainable development goals (sdgs) within the palestinian context. this is not only through raising awareness and introducing these goals, but also by offering students opportunities to engage practically in implementing various sdgs locally. students will participate in programs, projects, and activities aimed at reducing poverty and hunger, providing medical services, treatment, and medication to marginalized and poor groups, supporting gender equality and education, including persons with disabilities and special needs, preserving water resources and natural resources, raising awareness on alternative and clean energy, caring for the environment and agriculture, recycling solid materials, rejecting discrimination, promoting green spaces, and encouraging productive and forestry farming. students enrolled in the course can join different stages designed with alternatives for each phase, allowing them to complete the requirements under flexible conditions. this approach benefits the community while developing students skills and experiences.
11000117	Leadership and Communication Skills	1
The course aims to assist students in acquiring modern concepts in the field of communication and understanding the essential skills for effective communication with oneself and others. this is achieved through the use of effective teaching methods that rely on student engagement and motivation to learn through training and self-directed learning. the course emphasizes skill development through teamwork and interactive methods, helping students improve their verbal and non-verbal communication skills by learning public speaking and the fundamentals of oration. additionally, it helps students develop active listening skills, and contributes to enhancing their abilities in dialogue and persuasion, overcoming public speaking anxiety, self-promotion, negotiation, job interviews, presentation and delivery, and writing. the course also provides students with knowledge about innovative and creative ideas that can be implemented, as well as how to write a resume. furthermore, the course aims to refine students' personalities through participation in group presentations.
11000129	Introduction to Artificial Intelligence and Data Science	2
11000328	English Language II	3
University english ii is a three-credit hour university-required english language course which is offered to students majoring in sciences, engineering, agriculture, veterinary, and information technology ... etc. students in this course will be exposed to a range of science-based writings in english that supply students with samples of the kind of academic english they are likely to encounter in their textbooks. exercises on grammar, vocabulary and textual organization are geared towards developing students observational and analytical skills that aid comprehension. the course uses an integrated approach which allows for communicative interaction in the class to actively test and broaden the listening and speaking abilities of the students. furthermore, the acquisition of vocabulary items will be reinforced through their use in written sentences. additional training in writing will be given through questions and answers, summaries of principal ideas in a reading passage and the preparation of reports.

Speciality Requirements Student must complete 92 credit hours

Course Code	Course Name	Credit Hours	Prerequests
10211101	Calculus I	3
This course covers the concepts of function, inverse function, models, limits, continuity and derivatives, the differentiation rules and their applications, related rates, linear approximation, and hyperbolic functions. the mean value theorem, indeterminate forms, l' hospital's rule, curve sketching, and optimization problems.
10211102	Calculus II	3	10211101
Definite and indefinite integrals. the fundamental theorem of calculus. the substitution rule. applications of integration (areas and volumes), average value of a function. techniques of integration (integration by parts, trigonometric integrals, trigonometric substitution, integration by partial fractions, improper integrals). applications of integration (arc length, area of a surface). infinite sequences and series (the integral test and estimates of sums, the comparison tests, alternating series, absolute convergence and the ratio and root tests, power series, taylor and maclaurin series)
10211201	Calculus III	3	10211102
Topics covered in this course include: parametric equations and polar coordinates; vectors in r2 and r3 & surfaces; vector-valued functions; partial differentiation with applications; multiple integrals.
10211203	Principles of Differential Equations	3	10211102 10211201 or 10221102
Classifying and solving 1st order odes, solving homogeneous andnon-homogeneous 2nd and higher order linear odes, power seriesand laplace transforms methods to solve linear odes, solving 2ndorder cauchy-euler odes, solving systems of linear 1st-order odes in2 or 3 variables using eigenvalues- eigenvectors as well as laplacetransforms.
10211211	Principles of Mathematics	3	10211101
Topics covered in this course include: logic and proofs; set theory, relations and functions; cardinality and examples on mathematical structures.
10211220	Computer and Mathematics	3	10211102
Introducing a mathematical software with applications through giving a background and fundamentals of programming; flowcharts, algorithms, types of data, control statements, dimensions, functions, subroutines and graphing.
10211241	Linear Algebra I	3	10211102 or 10211211
Matrices and matrix operations. elementary row operations. determinants and inverses of matrices. systems of linear equations and methods of solutions. vector spaces. linear independence and basis. linear transformations. eigen values and eigenvectors.
10211321	Numerical Analysis I	3	10211220 10211241
Topics covered in this course include: numbers, binary, octal and hexadecimal number systems; floating point arithmetic, errors, sources and types; solving nonlinear equations, direct and indirect methods in solving systems of linear equations, solving systems of nonlinear equations; approximation and interpolations, numerical integration.
10211322	Linear Programming	3	10211241
Topics covered in this course include: problem formulation; graphic solution; simplex method; duality theorem; linear sensitivity analysis and algebraic representation; transportation and assignment problems; network (pert and cpm); game theory.
10211491	Seminar	1
This course involves discussion of characteristics of scientific thinking and its relationship with scientific research; it requires students to conduct a research on a specific topic in mathematics, and to deliver it and represent this research in a seminar for evaluation.
10215301	Machine Learning	3	10672224
This course provides a practical introduction to supervised machine learning techniques. students will explore regression, classification, decision trees, ensemble methods, and model evaluation, with a focus on building, evaluating, and optimizing machine learning models. the course also introduces foundational concepts in calculus and linear algebra to support understanding of algorithms. students will apply their knowledge through hands-on projects and learn the complete machine learning lifecycle, from data preprocessing to model deployment using modern tools and techniques.
10215302	Data Mining	3	10215301
This course explores the fundamental principles and practical methods of data mining. students will learn techniques for discovering patterns, relationships, and insights from large datasets, focusing on clustering, association rules, anomaly detection, and recommender systems. the course emphasizes hands-on experience with tools and real-world applications in areas such as time series analysis, network analysis, and streaming data. by the end of the course, students will understand how to apply data mining techniques to solve practical problems in various domains.
10215402	Graduate Project	3	10211491
The student expected to graduate selects a graduation project topic, conducts a preliminary study, designs the structural framework of the project, identifies the necessary tools for implementation, and masters their use.
10216201	Methods of Statistics I	3
Classifying and describing data, measures of central tendency, measures of dispersion, measures of position, the definition of probability and its properties, counting rules, discrete and continuous random variables, the binomial distribution, poisson distribution, the normal distribution and applications, sampling distributions, confidence intervals and hypothesis testing for one population mean.
10216202	Methods of Statistics II	3	10216201
Sampling distributions, confidence intervals, and hypothesis testing for one population mean, one population proportion, the difference between two means, and the difference between two proportions, simple linear regression, and correlation, one-way and two-way analysis of variance, chi-square tests, nonparametric methods, applications using statistical packages such as minitab, spss, or r.
10216302	Probability Theory I	3	10211201
Random experiments and events, basic probability rules, discrete and continuous random variables, the probability density function and cumulative distribution function for one and two random variables, mathematical expectation, measures of central tendency, measures of dispersion and percentiles, moments and moment-generating functions, conditional probability distributions, correlation coefficient, stochastically independent random variables, some special distributions; binomial, negative-binomial, gamma and normal distributions, transformation method.
10216343	Applied Regression Analysis	3	10211241 10216202
This course covers simple linear regression, multiple regressions, estimation, and goodness of fit tests, residual analysis, using matrices in regression, and factor rotation and applications
10216351	Experimental Design and ANOVA	3	10216202 10211241
Topics covered in this course include: one-way analysis of variance; random column design, latin squares design, two-factors design, multi-factors comparative experiment, testing model accuracy in the analysis of variance, incomplete block design; factorial designs (2k and 3k), and multiple comparisons
10216371	Time Series Analysis	3	10216302
Classical decomposition models, time series regression models, exponential smoothing; models. stationary time series. the autocorrelation and partial; autocorrelation functions. ordinary arma models. seasonal arima models. steps of model building: identification, estimation and diagnostic checking. forecasting.
10221101	General Physics I	3
This course covers the following topics: motion in one and more dimensions, the laws of motion with an application of newtons laws, vector quantities, work and mechanical energy, linear momentum and collisions, and rotational dynamics
10221102	General Physics II	3	10221101
This course is a study of the following topics: electric charges; forces and fields; electric potential and electric potential energy; electrical capacitance electric elements like capacitors, resistors, and conductors; electric current and direct-current circuits; magnetic fields; magnetic force; induction; and rc and rl circuits.
10221107	General Physics 1 Lab.	1	10221105 or 10221101
In this lab., experiments related to mechanics mostly covered in general physics i (10221101) are performed. this includes -measurements -vectors. -acceleration on an inclined plane. -the speed of sound in air -viscosity -newtons second law -conservation of energy and momentum -rotational dynamics -simple harmonic motion. -boyles law.
10671101	Principles of Programming I	3
This course begins with an introduction to computers, hardware and software and problem-solving. this course also includes an introduction to programming using c/c++ including: i/o; expressions and arithmetic; if, while and for statements; one-dimensional arrays, string handling, functions, scope, recursion and matrices.
10671102	Principles of Programming II	3	10671101
This course covers more advanced c/c++ programming features including: pointers, dynamic memory, structures, text files, binary files, classes and objects.
10671103	Principles of Practical Programming I	0
A set of tasks is discussed, and students are asked to solve them in the lab. these tasks coincide weekly with the topics explained in programming principles 1.
10671104	Principles of Practical Programming II	0	10671101
A set of tasks is discussed, and students are asked to solve them in the lab. these tasks coincide weekly with the topics explained in programming principles 2.
10671210	Data Structure	3	10671102
This course is an introduction to the various data structures which use an object-oriented language, such as java.the course covers: lists, stacks, queues, heaps, trees, search trees, hash tables, the analysis and implementation of data structures, recursion, sorting and searching.
10671212	Design and Analysis of Algorithms	3	10671210
In this course, students are introduced to the techniques used in the analysis of algorithms and design methods: divide and conquer, dynamic programming, greedy algorithms, recursive, searching and sorting algorithms and complexity analysis.
10671315	Big Data	3	10671353 10671210
Recognize big data processing techniques and platforms evaluate data using big data hadoop technique develop hadoop algorithms to mine big data illustrate big data analysis to a diverse audience and defend ethical approaches to data analysis of real business problems.
10671353	Database Systems Design	3	10671210
Students are introduced to database system concepts and architecture, data modeling using e-r model, relational model, normalization, operations on relational model, relational constraints and relational algebra, sql-the relational database standard, security in sql and a pl/sql overview. furthermore an overview of the oracle system, distributed databases and client-server architecture will be provided.
10671372	Computer Simulation	3	10671212
This course examines simulation and queuing models, random numbers generation, statistical sampling and analysis of data, simulation languages and selected applications.
10671499	Training	3
320 hours of practical training.
10672224	Fundamentals of Artificial Intelligence	3	10216302 or 10672110 10671210
This course will introduce the fundamental concepts and techniques used to design and build intelligent computer systems. a particular focus will be on the statistical and decision-theoretic modeling paradigm. students will learn the fundamentals of building software agents which are capable of performing intelligently by either accomplishing computation, e.g., searching, or by drawing inferences by learning from data. students will understand what supervised machine learning algorithms are and how they can be employed in classifying handwritten digits and photographs. the techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
10672350	Data Warehouse	3	10672218 or 10671353
This course will introduce students to the major activities involved in a data warehousing project. this course covers dimensional modeling, changing dimension, cube data model, data warehouse requirements, and etl overview are discussed. moreover, students will gain knowledge on ad hoc query tools and online analytical processing olap systems

Speciality Optional Requirements Student must complete 24 credit hours

Course Code	Course Name	Credit Hours	Prerequests
10211212	Modern Analysis I	3	10211211
Topics covered in this course include: properties of real numbers; open and closed sets; sequences; limits and continuity; differentiation; riemann integral.
10211302	Partial Differential Equations I	3	10211202 or 10211203
Topics covered in this course include: the formation of a partial differential equation; methods of solutions of first order linear and nonlinear partial differential equations; methods of solutions of second order linear and nonlinear partial differential equations; fourier series and transforms; wave equation, laplaces equation, potential equation, equation of an infinite wire, heat equation.
10211303	Vector Analysis	3	10211201
Topics covered in this course include: vector algebra, vector products, vectors and scalar fields; the gradient, divergence and curl theorems; line, surface and volume integrals, related theorems; curvilinear coordinates
10211323	Operations Research I	3	10211241
Topics covered in this course include: introduction to operation research; inventory models, queuing models; game theory; markov chains; case studies.
10211343	Number Theory	3	10211211
Topics covered in this course include: divisibility and prime numbers; chinese remainder theorem; congruence; euler's theorem, fermats theorem, wilsons theorem; linear congruence: congruent and non-congruent solutions; arithmetic functions; special numbers: perfect, deficient abundant and mersenne numbers.
10211474	Combinatorics & Graph Theory	3
This course focuses on graphs: simple graphs, directed graphs, components, connected components; blocks, cut-vertices, and bridges; euler graphs; trees, planar and non-planar graphs; graph matrices and coloring.
10216303	Probability Theory II	3	10216302 10211212
This course includes a review of some properties of random variables and probability distributions, multinomial distributions, bivariate-normal distribution; multivariate hyper-geometric distribution; limiting distributions, types of convergence, and characteristic functions are also examined
10216304	Mathematical Statistics I	3	10216302
Review for the probability density functions for one random variable or more, distribution of functions of random variables, t-distribution, f-distribution, order statistics, estimation, , efficient and maximum likelihood estimation, confidence intervals, testing statistical hypotheses: best test, uniformly most powerful test, likelihood ratio test, sufficient and complete statistics, the rao-blackwell theorem and rao-cramir inequality.
10216305	Mathematical Statistics II	3	10216304
This course covers an introduction to decision theory, risk and loss functions, the exponential family of distributions, sufficiency and completeness, bayesian estimation, estimation, and testing hypotheses for linear models. chi-square tests.
10216311	Samplint Methodology	3	10216201
Topics covered in this course include: census and sample surveys. population and sample design simple random samples, stratified sampling, cluster sampling, systematic sampling; estimation of means totals and proportions, ratio and regression estimators; other methods of sampling.
10216331	Stochastic Process	3	10216302
The course introduces fundamental concepts in stochastic processes with a focus on applications in mathematics and data science. key topics include discrete and continuous markov chains, the poisson process, and an introduction to stationary processes and brownian motion, linking stochastic processes to machine learning and differential equations. software tools will be used throughout to simulate these processes, deepening practical understanding.
10631300	Innovation and Entrepreneurship	3
This course is designed for students to help them be involved in creative, innovative, entrepreneurial, and corporate ventures in the future. subjects covered include introduction to entrepreneurship & creativity; developing successful business ideas; managing and growing an entrepreneurial firm; technical and financial feasibility studies; business models; market surveys; and business plan preparation.
10671325	Computer Vision	3
Demonstrate the basic concepts and problems of visual analytics, evaluate the data assigned using visual thinking and visual analytics techniques, as well as developing visual analytics applications
10671371	Computer Graphics	3	10671210 10671244
This course covers basic graphics operations and their implementations in 2 dimensions, introduction to opengl, devices for construction and display of computer-generated images, widowing and clipping, 2d geometric, transformation and viewing, 3d object representation, transformation and viewing.
10671374	Digital Image Processing	3	10671210
Image formats, image recognition, image extraction, image processing primitives, and image indexing. clustering: hierarchical and non-hierarchical methods, clustering using neural networks and genetic algorithms. classifications: nearest neighbors, neural nets, and genetic methods. image enhancement, segmentation, measurement, fourier analysis, image storage and retrieval.
10671415	Social Netweork Analysis	3
Demonstrate the basic concepts applications and problems of web and social networks analyze website, traffic and apply social media strategies. evaluate the limitations of web-based data and appraise large sensor and network datasets. collaborate an a team in web analytics project, and demonstrate web mining solutions to a diverse audience, in addition to apply ethical principles to real-life business problems.
10671453	Database Management Systems	3	10671353
Students will study advanced concepts in creating and managing tables, storage access and index structure.. in addition, they will learn distributed db concepts, create and maintain constraints, and create views, pl/sql block and its sections. they also learn about triggers, functions, procedure and packages, along with database connectivity (odbc, ole, and ado), managing users. practical tools are used to implement the different concepts. form builder, report builder and oracle 10g are used.
10671491	Special Topics	3
Students are introduced to advanced selected topics in different areas of computing.
10672346	Ontology Engineering for the Semantic Web	3
This course introduces the concept of ontology and its applications in the semantic web. youll learn how to design and develop knowledge models that represent concepts and relationships between data on the web.
10672352	Natural Language Processing	3	10215301 or 10672311
This course provides an introduction to fundamental topics on natural language processing, where students study the interaction between the computer system and natural languages. in this course, students learn how to build computer solutions for tasks where natural (human) language is the main input. by the end of the course, students will have adequate knowledge, skills, and technologies that enable them to properly handle large amounts of textual content and extract useful information.