Department Welcome
Welcome to the Data Science and Artificial Intelligence Department at the American University of Madaba!
Founded in 2020, our department is part of the Faculty of Information Technology and is dedicated to advancing the fields of data science and AI through cutting-edge education and research. Our program provides a robust curriculum in machine learning, big data analytics, AI ethics, and data-driven decision-making, equipping students with the skills necessary to tackle complex real-world challenges.
In our department, we utilize state-of-the-art hardware and software to give students hands-on experience with the latest tools in the industry. Our faculty members, hailing from prestigious academic and research backgrounds, are actively engaged in pioneering research, enriching our curriculum with insights from the forefront of AI and data science. This integration of advanced technology and research ensures that our graduates are exceptionally prepared to excel in the fast-evolving tech landscape.
We look forward to supporting you in your journey to become innovators and leaders in data science and artificial intelligence!
Academic Members
List and details of academic members of the department.
Study Plan
Details about the study plan and curriculum.
Course Curriculum: 134 Credit Hours
Guidance Plan
Guidelines and plan for student guidance.
Course Curriculum: 134 Credit Hours
List of Competencies
Data Science and AI - List of Competencies
- Knowledge and understanding:
- Overview the theoretical and technical concepts related to data science and Artificial Intelligence.
- Analyze a complex data science problem
- Apply principles of data science and AI to identify solutions.
- Understand computing contemporary issues and devise viable solutions for them.
- Understand and engage in continuing professional development.
- Practical Skills:
- Apply data science principles to analyze data of different sources to produce predictive results
- Use a variety of computer programming languages to implement AI solutions for different computing problems.
- Apply AI concepts and methodologies, such as Machine Learning and Deep Learning, to explore the hidden patterns in massive amount of data
- Communication skills:
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Thinking skills:
- Think out-of-the-box and be ready to participate in IT- related business ventures.
- Understand and engage in continuing professional development.
Course Description
Detailed descriptions of the courses offered.
| 402101 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401151 | - | |
| This course is the starting point for students to learn about the data science roadmap, main principles, and techniques. Students will study typical tasks of data science, starting from data collection, data exploration, features extraction, descriptive analysis, predictive analysis, and performance measurement. Several real-world applications will be presented to better-understand the previously mentioned concepts and emphasize on the impact of these applications in real-world situations. | ||||
| 402112 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 903281 | - | |
| This course highlights the importance of applied statistics to the multidisciplinary field of data science by following statistical approaches to investigate and interpret data. Statistical estimates will be reviewed, i.e., estimates of location, and estimates of variability, as well as categorical data exploration, i.e., mode, expected value, and probability. Moreover, correlation measures will be introduced. Additionally, data distributions, statistical inferences and learning, statistical experiments, and significance testing will be studied. Practical applications will be incorporated for a clearer connection between statistics and data science applications using R and/or Python programming languages. | ||||
| 402201 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402101 | - | |
| This course introduces students with the vital role of data engineering in data science applications. Starting with the fundamental practice of data engineering, i.e., data lifecycle management, the course familiarizes students with the lifecycle of data and how to perform its key tasks, namely, the process of extracting, loading, and transforming data (ETL/ELT). Different resources of data are investigated, leading to different data formats, and accordingly, several data engineering tools are presented. Students will be able to collect, pre-process, and prepare datasets that are ready to be explored and used in data science projects and applications. | ||||
| 402213 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | - | 402101 | |
| This course discusses the concept of Linear Algebra for data science. Linear algebra is used in data preprocessing, data transformation, and model evaluation. The topics covered in this course are vectors and matrices, solving matrix-vector equations, solving systems of linear equations, Euclidean vector spaces, performing eigenvalue/eigenvector analyses, and using principal component analysis to do dimension reduction on real-world datasets. To complete the analysis tasks, the student will use a programming language. | ||||
| 402251 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402101 | - | |
| This course introduces the fundamentals of data exploration through analysis and visualizations. On one hand, the course starts by presenting the basic principles for creating aesthetic informative visualizations in terms of design, mapping different kinds of data to appropriate figures, positioning, and choice of color scales. On the other hand, the course focuses on enabling students to apply such principles to explore many datasets, create suitable visualizations, and develop insightful dashboards. Additionally, the course prepares students to communicate the resulting outcomes through storytelling. | ||||
| 402221 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401122 | 402222 | |
| The course is providing a pragmatic and hands-on introduction to Python programming language. This course discusses solving problems and binary computation. The course covers variables and data type, loops, lists, functions, dictionaries, sets, regular expressions, input/output files, JSON files, and object-oriented design. | ||||
| 402222 | 1 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 0 | 3 | - | 402221 | |
| This course highlights the importance of applied statistics to the multidisciplinary field of data science by following statistical approaches to investigate and interpret data. Statistical estimates will be reviewed, i.e., estimates of location, and estimates of variability, as well as categorical data exploration, i.e., mode, expected value, and probability. Moreover, correlation measures will be introduced. Additionally, data distributions, statistical inferences and learning, statistical experiments, and significance testing will be studied. Practical applications will be incorporated for a clearer connection between statistics and data science applications using R and/or Python programming languages. | ||||
| 402321 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402221 | - | |
| This course is for undergraduate students of the Data science and AI department. This course will help students to deeply understand the object-oriented programming approach using Python language. Graphical user interface, network programming, and database using Python will also be covered. In this course, the students will be able to design, develop, and solve a real-life project. The course helps students to use TCP/IP programming and MYSQL databases. The course helps students to demonstrate their research, practical and oral presentation skills. | ||||
| 402331 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402201 | - | |
| This course covers theoretical and practical algorithms for machine learning from a variety of perspectives. In this course, decision tree learning, statistical learning methods, supervised, unsupervised learning and reinforcement learning are covered. The theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning, and Occam s Razor method are also covered. The assignments include hands-on experiments with various learning algorithms. | ||||
| 402451 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402201 | - | |
| This course will introduce an overview of Big Data applications, and market trends. The course will also introduce the fundamental platforms, such as MapReduce, Hadoop ecosystem, Spark, H2O Framework, Apache Storm, and other tools. Afterwards, the course will introduce several data storage methods and how to upload, distribute, and process them. This will include HDFS, HBase, Pig, and Hive, document database, and graph database. The course will go on to explore different ways of handling data analytics algorithms on different platforms. Then, the course will introduce visualization issues on Big Data. It also provides a first hands-on experience in handling and analyzing large, complex structured, semi-structured, and unstructured data. Students will then have fundamental knowledge on Big Data to handle various real-world challenges. | ||||
| 402452 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This course introduces students to the Information Retrieval (IR) systems. IR is the process through which a computer system can respond to a user s query for text-based information on a specific topic. IR systems should only retrieve documents such as text, images, speed or video that is relevant to a user s interest. Basic and advanced techniques for building text-based IR systems such as efficient text indexing and documents clustering and classification will be explored. | ||||
| 402332 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| In this course, the students will learn the foundational concept of neural networks and deep learning. The student will learn how to build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network s architecture, and apply deep learning to their own applications. The course will also cover feed-forward neural networks, convolutional neural networks, recurrent neural networks, deep reinforcement learning, and other fundamental concepts and techniques. This course will also teach the students the mathematical foundation underlying deep learning. At the end, the student will gain a thorough understanding of modern neural network algorithms for the processing of images and time-series forecasting. | ||||
| 402441 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This course focuses on text mining and natural language processing. It covers a variety of topics such as text representation, document classification and clustering. A major focus of the course is on widely used NLP algorithms. This includes topic models, entity tagging, opinion analysis, information extraction, parsing, summarization, machine translation and question answering. The course provides the students with practical hands-on experience on text analytics using open source machine learning libraries such as scikit-learn, and Tensorflow. | ||||
| 402311 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This is an introductory time series course. The goal of the course is to give students a better knowledge of the ideas, methods, and resources used in time series analysis and forecasting. The course builds a thorough set of methods and tools for examining various time series formats and for comprehending the most recent works in applied time series econometrics. The topics include ARMA/ARIMA models, spectra analysis, multivariate time series and forecasting techniques. | ||||
| 402421 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This course gives a basic understanding of what social network and behavioral analysis is and how it can be applied. In this course students will learn about social networks structure and evolution, and how to practically analyze large-scale network data and how to reason about it. Topics covered in this course include graph theory, link prediction, recommendation systems, graph mining, network community detection, graph visualization, graph data science, information propagation on the web, and connections with work in the social sciences and economics. | ||||
| 402435 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | Department Approval | - | |
| Topics will be assigned by the department on evolving Data science and AI techniques and related topics to support the study plan and to encourage further research by students. | ||||
| 402436 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | Department Approval | - | |
| Topics will be assigned by the department on evolving Data science and AI techniques and related topics to support the study plan and to encourage further research by students. | ||||
| 402437 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | Department Approval | - | |
| Topics will be assigned by the department on evolving Data science and AI techniques and related topics to support the study plan and to encourage further research by students. | ||||
| 402432 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This course introduces students to data regression. Data regression is the most widely used statistical technique. It estimates relationships between independent variables (predictors) and a dependent variable (outcome). Regression models can be used to help understand and explain relationships among variables; they can also be used to predict outcomes. In addition, students will learn how to derive multiple linear regression models, how to use software to implement them, and what assumptions underlie the models. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. | ||||
| 402471 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 402331 | - | |
| This course introduces students to various software tools used to collect and analyze data such as Beautiful soup and pandas libraries. Software tools that help data scientists to build various machine learning models such as scikit-learn will be introduced with hands-on practice. Advanced tools used to build advanced deep learning models such as tensorflow will be covered. | ||||
| 402351 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401212 | - | |
| This course provides an introduction to modeling and simulation. Both the theoretical as well as applied aspects of simulation are covered. Topics include discrete-event simulation, states, transitions, model definition, model quality, input and output analysis, input distributions, experimental design, optimizing models, levels of model detail, cost-quality tradeoffs, verification, and validation. Students will be required to simulate a complex system which necessitates the creation of models. Students will explore and utilize a simulation API. | ||||
| 402341 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401151 | - | |
| This course covers security and privacy concepts in data science and AI applications. The course explores the cybersecurity risk in data science and AI applications and how to mitigate those risks by learning tools and techniques that are designed for it. The course discusses several recent research projects in securing data science applications which helps students to express themselves and demonstrate their knowledge in this field. | ||||
| 402342 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401313 | - | |
| This course examines statistical pattern recognition approaches, technologies, and algorithms from several perspectives. The topic of this course includes feature extraction, Bayesian decision theory, support vector machine, nearest-neighbor rules, nonparametric techniques, neural networks, syntactic pattern recognition techniques, and clustering algorithm. The students will have practical experience in constructing pattern recognition by solving programming assignments and proposing a project that relates to this topic. | ||||
| 402433 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401314 | - | |
| The aim of the course is to enable students to solve problems using explicit knowledge and reasoning techniques and to develop expert systems for simple problems. Students will be able (1) to express knowledge of a simple domain in propositional and/or first-order predicate calculus, (2) to design and develop expert solutions to simple problems where AI techniques can be employed, and (3) to write simple programs that reason about the available knowledge to achieve their goals. Furthermore, students will learn logic, production rules, semantic nets, frames, and formalisms for plausible reasoning. | ||||
| 402434 | 3 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 3 | 0 | 401314 | - | |
| This course serves as an introduction to robotics with a focus on the fundamental components including motors, sensors, and algorithms. Students will gain practical experience designing and constructing robots, integrating sensors and actuators, and creating robot control algorithms. The improvement of students' communication and critical thinking abilities is a clear objective of this course. Discussions, group projects, and laboratory work are used to accomplish this. | ||||
| 402491 | 1 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 1 | 0 | Department Approval+completion of 99 Cr. Hrs. | - | |
| The graduation project is an opportunity for students to employ their four-year experience and skills. The project targets two fundamental skills that students urgently need in their careers: software production and documentation. In part 1 of the project (graduation project 1), students should plan for their project with the guidance of the advisor. The planning includes problem definition, objectives, state-of-the-art, system analysis, solution design, and required technologies to implement the project. | ||||
| 402492 | 2 CH. | Prerequisite | Co-requisite | |
|---|---|---|---|---|
| 2 | 0 | 402491 | - | |
| Graduation project (2) is a continuation of the work accomplished in course number 0402491. This course includes the implementation, software testing, and software documentation. The documentation must follow professional guidelines, which include problem motivation, contribution, how the system addresses the problem, record all the analysis and design artifacts, and a user manual. Upon the consent of the advisor, students must defend their projects to finish the requirement for the graduation project. | ||||
Program Learning Outcomes
Data Science and AI Program Learning Outcomes
Student learning outcomes describe what students are expected to know and be able to do by the time of graduation. By the time of graduation, the Data Science and AI Department's program must enable students to attain an ability to:
PLO1: Apply data science theory and software development fundamentals to produce AI-based solutions.
PLO2: Determine an algorithm’s efficiency, computability, and resource usage.
PLO3: Utilize the latest tools and technologies in data science and AI to develop creative and innovative solutions.
PLO4: Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
PLO5: Conduct scientific research and practical projects that produce significant AI software solutions.