المستوى الأول

First Semester

م

رقم المقرر ورمزه

مسمى المقرر

عدد الوحدات

Course Name

Course Code

No

Contact Hours

1

حاب 601

أساسيات حوكمة البيانات

4(4,0,0)

Data Governance Fundamentals

DMG601

1

2

حاب 602

نمذجة وتصميم البيانات

5(3,0,2)

Data Modeling and Design

DMG 602

2

3

حاب 603

استراتيجية البيانات والأعمال

4(4,0,0)

Data and Business Strategy

DMG 603

3

مجموع الوحدات

13

Total Units

المستوى الثاني

Second Semester

م

رقم المقرر ورمزه

مسمى المقرر

عدد الوحدات

Course Name

Course Code

No

Contact Hours

1

حاب 604

سرية وخصوصية البيانات

4(4,0,0)

Data Security and Privacy

DMG 604

1

2

حاب 605

إدارة جودة البيانات

4(4,0,0)

Data Quality Management

DMG 605

2

3

حاب 606

تخزين وتشغيل البيانات

5(3,0,2)

Data Storage and Operations

DMG 606

3

مجموع الوحدات

13

Total Units

المستوى الثالث

Third Semester

م

رقم المقرر ورمزه

مسمى المقرر

عدد الوحدات

Course Name

Course Code

No

Contact Hours

1

حاب 607

برمجيات إدارة البيانات

5(3,0,2)

Data Management Software 

DMG 607

1

2

حاب 609

تكامل وتوافق البيانات

5(3,0,2)

Data Integration and Interoperability

DMG 609

2

3

حاب 610

مراجعة البيانات والتحقق من صحتها

5(3,0,2)

Data Review, Verification and Validation 

DMG 610

3

مجموع الوحدات

15

Total Units

المستوى الرابع

Fourth Semester

م

رقم المقرر ورمزه

مسمى المقرر

عدد الوحدات

Course Name

Course Code

No

Contact Hours

1

xxxxx

مقرر اختياري

4

Elective

xxxxx

1

2

حاب 611

مشروع تخرج  

4

Capstone Project

DMG 611

2

مجموع الوحدات

8

Total Units

م

رقم المقرر ورمزه

مسمى المقرر

عدد الوحدات

Course Name

Course Code

No

Contact Hours

1

حاب 608

تحديات إدارة البيانات وحلولها

4(4,0,0)

Data Management Challenges and Solutions

DMG 608

1

2

حاب 613

العرض المرئي للبيانات

5(3,0,2)

Data Visualization

DMG 613

2

3

حاب 614

البيانات الضخمة والحوسبة السحابية

4(4,0,0)

Big Data Bases and Cloud Services

DMG 614

3

4

حاب 615

تحليل البيانات وذكاء الاعمال

5(3,0,2)

Data Analytics and Business Intelligence

DMG 615

4

5

حاب 616

إنترنت الأشياء واكتساب البيانات

4(4,0,0)

Internet of Things and Data Acquisition

DMG 616

5

6

حاب 617

تنقيب البيانات

4(4,0,0)

Data Mining

DMG 617

6

7

حاب 618

تحليل البيانات الضخمة

5(3,0,2)

Big Data Analytics

DMG 618

7

8

حاب 619

مواضيع مختارة في إدارة وحوكمة البيانات

4(4,0,0)

Selected Topics in Data Governance and Management

DMG 619

8

9

حاب 620

تحليل البيانات المتقدم

5(3,0,2)

Advanced Data Analytics

DMG 620

9

10

حاب 621

الإحصاء في تحليلات البيانات

4(4,0,0)

Statistics in data Analytics

DMG621

10

11

حاب 622

القيادة الإدارية

4(4,0,0)

Administrative Leadership

DMG622

11

12

حاب 623

صناعة القرارات الإدارية

4(4,0,0)

Managerial Decision Making

DMG623

12

13

حاب 624

إدارة المخاطر والأزمات

4(4,0,0)

Crisis and Risk Management

DMG624

13

Prerequisite

Credits

Course Title

Course Code

-

4

Data Governance

DMG601

1. Objectives:

Upon completion of the course, a student will be able to:

• Define the role principles, policies, tools, and responsibilities for data management.
• Define cloud and outsourcing requirements for the organization.
• Define the management activities and business culture development activities
• Monitor and guide data usage and management activities.
• Develop organizational touchpoints and data-centric culture controls.
• Develop the Ethical Data Handling Strategy
• Create and implement data principles and policies
• Make informed judgments in data management practice based on legal and ethical principles

2. Content:

This course covers the planning, supervision and control over data management and use. It should cover the following main topics: data governance for the organization and its operating framework; data principles, policies and roles; business cultural development; data in the cloud; and data handling ethics.

3. Assessment Method

• Assignments, reviews of research papers, reports
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Modeling and Design

DMG 602

1. Objectives:

Upon completion of the course, a student will be able to:

• Explain the role of data and database modeling.
• Distinguish between the basic approaches of data modeling techniques.
• Explain the concepts of Data warehousing, Distributed Database and Big Data
• Implement data modeling in capture the information requirements for an enterprise domain.
• Implement a relational database design using an industrial database management system, including the principles of data type selection and indexing.
• Implement SQL in the context in data definition, data manipulation, and data control.
• Gain experience of designing high‐quality relational databases

2. Content:

This course covers the data modeling and design principles. It should cover the following main topics: Data Modelling Concepts; Conceptual Modelling E-R Model. How to convert business requirements to E-R Diagrams Entities, Relationships, Identifiers, PKs, Cardinality, FKs Relational Database Management Principles; Logical Modelling; Converting a conceptual model to logical model Integrity constraints, Normalization, Physical Modelling SQL practices, Transaction Management Concepts Consistency issues, Databases for Decision Support, Data warehousing Concepts, Distributed Database Concepts; Conceptual understanding of Big Data and NoSQL; New generation Databases (MongoDB, Cassandra, Key-value Databases Wide column/ Column Databases Document Databases Graph Databases). 

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data and Business Strategy

DMG 603

1. Objectives:

Upon completion of the course, a student will be able to:

• Develop proficiency in data and business strategy concepts, tools, and frameworks.
• Develop data-gathering and analytical skills to identify strategic problems and opportunities.
• Develop leadership and interpersonal skills as a team member.
• Develop integrative thinking about the concepts learned in GMD1201 and other courses.

2. Content:

This course introduces concepts and analytical techniques for creating a sustainable advantage in difficult competitive environments. The perspective adopted for this course is that of the top manager who has overall responsibility for the performance of the firm or of a business unit within the firm. Such a manager needs to understand the basis and data for the current performance of the firm and to identify those changes (inside or outside the firm) that are most likely to affect future performance adversely or that provide opportunities for the firm to improve its performance. The manager must then use the firm’s resources to formulate and implement strategies to compete successfully in its new environment. The strategy must define the scope of the firm’s activities, the logic through which the activities result in better performance, and what it is about the firm that allows it to carry out those activities better than its competitors. Having a solid understanding of strategy is not only vital for top managers, but is also important for external consultants, auditors, financial analysts, and bankers in evaluating and valuing other firms

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Security and  Privacy

DMG 604

1. Objectives:

Upon completion of the course, a student will be able to:

• Identify key data protection concepts, principles and obligations;
• Assess stakeholders, roles and responsibilities;
• Assessment of the severity of data breaches and insurance role (liability, requirements and demonstration of compliance);
• Evaluate cybersecurity, data protection audits, privacy & digital sovereignty;
• Justify options and solutions for cross-border data transfer and third-country data transfers;
• Analyze the Electronic Protection Design & Impact Assessment;
• Understand the purposes of the data processing, including legitimate interest and consent;

2. Content:

This course provides knowledge and understanding of data security, data privacy, ways to increase productivity & efficiency, principles of data protection, and the consequences of not adhering to applicable laws and regulations. Students will learn about digital literacy, using the Internet as a productivity tool, managing security threats and protecting data. This will involve applying an interactive and on-the-job (i.e., functional) methodology: role playing, concrete and realistic case studies, with both successful and non-successful outcomes and peer-to-peer discussions. Students will also investigate technology career paths available in the industry.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Quality Management

DMG 605

1. Objectives:

Upon completion of the course, a student will be able to:

• Learn about data quality challenges inherent in data collection, analysis, processing, and integration
• Recognize the critical role of data quality management during data collection and integration
• Recognize the required techniques to identify the data quality problems via root cause analysis during data collection and consolidation
• Learn techniques to monitor and manage the data quality problems during data synchronization among various processes within an information system
• Practice data quality facets, accuracy, and data correction and cleansing practices to ensure high quality data
• Learn and implement various techniques to convert data to appropriate formats both for data analysis and processing and data integration
• Learn and apply various algorithms and performance metrics for ensuring data quality

2. Content:

The enterprises collect corporate data that consists numerous database connected that host data from countless real-time devices in various formats, which frequently changes and incorporates newer formats as the corporate processes are changed. In addition, the enterprises upgrade and redesign their applications to accommodate new processes in order to maintain their systems, hence, mandates for effective and quality data integration. During these processes, the quality of the collected and integrated data deteriorates, which degrades the performance of the information systems. The purpose of this course is to investigate various data quality problems, incorporate various practices to mitigate the data quality issues during data integration, and to maintain and represent high data quality within an information system.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Storage and Operations

DMG 606

1. Objectives:

Upon completion of the course, a student will be able to:

• Understand the concepts of information, data, and data storage
• Understand database technology characteristics
• Manage and monitor database technology
• Manage availability of data throughout the data lifecycle
• Ensure the integrity of data assets
• Manage performance of data transactions

2. Content:

This course covers the essential components of effective Data Storage and Operations Management, including its main activities, Lifecycle Management, Data Technology Management, tools and operations, roles and responsibilities, database environments, availability, recoverability, architectures, and pitfalls. Topics covers in this course are:

• Introduction to Storage
• Data Storage and Processing
• Information Storage and Management
• Database Technology Support
• Database Operations Support
• Database administration

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Management Software

DMG 607

1. Objectives:

Upon completion of the course, a student will be able to:

• Discuss the application of technologies to convert data to an appropriate format for data analysis.
• Discuss the application of algorithms to analyze data.
• Discuss the application of technologies and performance metrics for evaluation of data analysis results.
• Implement and apply technologies to convert data to an appropriate format for data analysis.
• Implement and apply technologies and performance metrics for evaluation of data analysis. results.
• Implement and apply technologies of visualizing and analyzing results.

2. Content:

This course covers following topics:

• Syntax and semantics for programming languages that are particularly suited for data analysis, e.g., Python, R etc.
• Routines for importing, combining, transforming and selecting data.
• Algorithms for handling missing values, discretization and dimensionality reduction.
• Algorithms for supervised machine learning, e.g., naïve Bayes, decision trees, random forests.
• Algorithms for unsupervised machine learning, e.g., k-means clustering.
• Libraries for data analysis.
• Evaluation methods and performance metrics.
• Visualizing and analyzing results...

3. Assessment Method

•  Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Management Challenges and Solutions

DMG 608

1. Objectives:

Upon completion of the course, a student will be able to:

• Write analytics tasks (algorithms and queries) for scalable platforms.
• Run analytics tasks on large clusters of machines.
• Recognize how dividing a large job into parallel tasks to improve performance.

2. Content:

This course addresses the technical issues that emerge during the collection, management, processing, and analytics of large-scale data. In this course we introduce modern approaches to organizing and analyzing large, fast growing and diverse data-sets. We will cover the characteristics and principles of big data analysis and the platforms and tools that are capable of managing big data. Students will be introduced to the technical skills necessary for assessment of current approaches to big data management and analytics and will acquire a hands-on experience using these technologies. The main topics should be covered in this course are: Overview of Research Data Management; Data Types, Stages, and Formats; Metadata; Data Storage, Backup and Security; Legal and Ethical Considerations; Data Sharing and Reuse Policies; Archiving and Preservation

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Integration and Interoperability

DMG 609

1. Objectives:

Upon completion of the course, a student will be able to:

• Make data available in the format and timeframe needed by the consumer
• Consolidate data physically and virtually into data hubs
• Lower cost and complexity of solutions by using shared objects
• Identify meaningful events and automatically trigger alerts and actions
• Support business intelligence, analytics, master data management, and operational efficiency efforts

2. Content:

This course covers the management, the movement and consolidation of data within and between applications and organizations. Topics cover in this course are:

• Data Interoperability (Acquire, Move, Transform, Integrate)
• Data Integration (Plan and Analyze, Design Data Integration Solutions, Develop Data Integration Solutions, Integrate and Interoperate Data, and Monitor Data Movement Operation)
• Operational Intelligence Support (Perform Predictive Analytics and Perform Complex Event Processing)

3. Assessment Method

• Assignments and exercises
• Quizzes
• Reports
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Review, Verification and Validation 

DMG   610

1. Objectives:

Upon completion of the course, a student will be able to:

• Situate data verification and validation components in the project life cycle
• Identify the inputs and outputs of the data verification and validation processes
• Understand how to perform data verification and data validation
• Implement proof based best practice methodologies lined up with general objectives
• Interpret the results of the processes of data verification and validation

2. Content:

The course will cover the following main topics:

• Data review concepts and importance
• Introduction to data verification and validation
• Requirements for data verification and verification
• Data verification steps
• Data validation steps
• Tools for data verification and validation
• Data verification and validation best practices

3. Assessment Method

• Assignments
• Quizzes
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Capstone Project (1)

DMG  611

1. Objectives:

Upon completion of the course, a student will be able to:

• Demonstrate that he has acquired specialization and skills in a particular part of the data management and governance field.
• Formulate a moderate sized problem and select and justify an approach to solve the problem within certain constraints.
• Watch ethical principles throughout the work.
• Prepare a written report on the work done
• Make an oral presentation that should accurately summarize the work done.

2. Content:

In this course, the student will use the skills and knowledge gained during his studies to demonstrate the ability to design a data management and governance project from the design stage to the implementation and verification stage. This is done under the supervision of a faculty member in the department.

3. Assessment Method

• Project presentation and discussion in front of a committee
• Evaluation of the project report

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Capstone Project (2)

DMG  612

1. Objectives:

Upon completion of the course, a student will be able to:

• Demonstrate that he has acquired specialization and skills in a particular part of the data management and governance field.
• Formulate a moderate sized problem and select and justify an approach to solve the problem within certain constraints.
• Watch ethical principles throughout the work.
• Prepare a written report on the work done
• Make an oral presentation that should accurately summarize the work done.

2. Content:

In this course, the student will use the skills and knowledge gained during his studies to demonstrate the ability to design a data management and governance project from the design stage to the implementation and verification stage. This is done under the supervision of a faculty member in the department.

3. Assessment Method

• Project presentation and discussion in front of a committee
• Evaluation of the project report

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Visualization

DMG  613

1. Objectives:

Upon completion of the course, a student will be able to:

• Communicate information clearly and efficiently via statistical graphics, plots and information graphics.
• Use some of the key data visualization software and programming languages such as Microsoft Excel and Python.
• Combine data in mashups, create various types of visualizations and share them to the Microsoft Power BI cloud service.
• Explore different visualizations to determine which ones are the most effective for the stakeholders.

2. Content:

This course will help students learn how to make sense of data by creating informative and engaging reports and dashboards. The student will be using tools such as Tableau, Power BI, Excel, R, and/or Python to generate visualizations. By the end of this course, the student will have a clear-cut understanding of how to derive meaningful conclusions from data.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Big Data Bases and Cloud Services

DMG  614

1. Objectives:

Upon completion of the course, a student will be able to:

• Demonstrate a comprehensive understanding of the essential principles, practices, and technologies employed in the effective design and development of systems capable of collecting, storing, and managing Big Data;
• Describe various aspects of the cloud such as applications, services, orchestration, modern infrastructure, business continuity, security, and service management
• Analyze critically a Big Data problem by identifying key requirements, alternative solutions, and evaluation methods
• Select and design scalable cloud services using big data resources
• Demonstrate knowledge to adapt emerging big data and cloud technologies such as MapReduce and NoSQL, to support business applications

2. Content:

This course will aim to provide the students with foundation knowledge and understanding of Big Data and distributed computing systems, especially in the context of cloud storage. At the same time, the course will provide a sound understanding of the designing and engineering of systems for handling big data in a distributed environment based on dynamically scalable architectures. The course will also explain how the business models of enterprises are changing with cloud computing, which can provide large storage and computation space without purchasing expensive computer systems. Additionally, the course will educate the students to examine recent technological solutions and research in cloud computing and big data, with a focus on bridging the gap between business continuity and digital technology advancement, and business development.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Analytics and Business Intelligence

DMG  615

1. Objectives:

Upon completion of the course, a student will be able to:

• Determine how different business intelligence systems might help to corporate performance
• use standard approaches used in business intelligence

2. Content:

This course finds out how to put your data analytics talents to use in the field of business analytics (BI). The course focuses on the many components of the business intelligence project lifecycle, including project planning, BI tool selection, data modeling, ETL design, BI application design and deployment, and reporting. This course is intended for those who are interested in business intelligence methods and analysis, but who do not need a deep understanding of statistical analysis or computer programming techniques.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Internet of Things and Data Acquisition

DMG  616

1. Objectives:

Upon completion of the course, a student will be able to:

• Identify and define the data acquisition requirements and specifying data acquisition tasks to be performed;
• Identify how IoT differs from traditional data collection systems;
• Demonstrate an ability to collect, create, and process spatial data within a variety of environments;
• Describe and explain the similarities and differences between data models as well as how data is treated differently within each format, to include the conversion of data between different formats.

2. Content:

This course will explain the technology used to build IoT devices, how they aggregate, communicate, store data, and the types of data acquisition systems needed to support them. Important topics include, but not limited to:

1. Generating Data with Devices;
2. Data Acquisition Techniques (i.e. manual, logical and physical);
3. Common Data Acquisition Considerations (i.e. Business Needs, Business Rules, Data Standards, Accuracy Requirements, Cost, Currency of Data, Time Constraints, Format, etc.);
4. Data Acquisition applied Processes & Tools;
5. Data Conversion;
6. Data Stream Analysis;
7. Data Acquisition using Emerging Technologies (i.e. Machine Learning, etc.);
8. Modern Data Loggers.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Data Mining

DMG  617

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize what Is Data Mining, what kinds of data can be mined, what kinds of patterns can be mined, and what kinds of applications are targeted.
• Specifies data preprocessing and data quality in Data Mining
• Discover Advanced data mining: main concepts, methods and subdomains
• Understand and apply the foundations of modelling approaches such as linear regression, linear classifiers, decision tree models and clustering.
• Recognize and apply the mathematical statistics foundations used in Data Mining
• Gain experience of doing independent study and research.

2. Content:

This course will provide knowledge of theoretical background to several of the commonly used data mining techniques and will examine data techniques for the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data mining methods such as classification, Cluster Analysis, association rules, Anomaly Detection and Avoiding False Discoveries.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Big Data Analytics

DMG  618

1. Objectives:

Upon completion of the course, a student will be able to:

• Demonstrate a critical understanding of concepts and activities for Big Data Analytics
• Reframe a business challenge as an analytics challenge
• Explain how advanced analytics can be leveraged to create a competitive advantage from the available big stores of business data
• Apply appropriate advanced analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
• Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences

2. Content:

This course will aim to educate the students about how to apply the growing body of machine learning (ML) algorithms to various Big Data sources in a business context. By the end of this course, students will have a better understanding of processes, methodologies, and tools used to transform a large amount of business data available into useful information and support business decision-making by applying ML algorithms. The focus of the course is less on the technical aspects of ML algorithms and more on the application of ML algorithms to Big Data available in different domains. The course will use recent technologies such as python as the primary data analysis platform for the execution and deployment of ML projects. 

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Selected Topics in Data Governance and Management

DMG  619

1. Objectives:

Upon completion of the course, a student will be able to:

• Learn about the state of the art topics that arise in the Data Governance and Management field.
• Recognize how data and information can be managed more efficiently and effectively within organizations to tackle Privacy, Data Protection and Cyber Security threats and risks
• Recognize Data Governance controls, policies and strategies to capture, manage and securely dispose of business data and records
• Develop action plans to manage information compliance, audits, legislation and regulations through review of information and information management controls
• Set up and implement a Data Governance project, including addressing roles and responsibilities, risk management and improving business processes 

2. Content:

In this course, a topic or a set of topics that will be determined and approved by the department to reflect the most recent issues in the field of Data Governance and Management that might appear after approval of the study plan.

3. Assessment Method

• Assignments and exercises
• quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Advanced Data Analytics

DMG 620

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize how to utilize data and analytics concepts in the organization.
• Gain knowledge and skills of data analytics, data visualization, and data science approaches.

2. Content:

Advanced data analytics studies how intelligent agents can learn from and adapt to experience, and how to realize such capabilities on digital computers. It is used in many domains of business, industry, and science. This topic focuses on machine learning. A key component of machine learning is knowledge discovery. This topic builds on earlier data analytics subjects to teach fundamental and advanced algorithms. It includes both hands-on experience and basic theory. Students learn fundamental field methods via practice and theory. The class also addresses practical applications of machine learning, particularly in AI.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

 Statistics in Data Analytics

DMG621

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize the concepts of descriptive and inferential statistics.
• Gain knowledge about basic probability theory, probability distributions for both discrete and continuous random variables.
• Apply nonparametric statistical methods in business applications.
• Use statistical programs to analyze data.

2. Content:

The course provides an introduction to administrative statistics and discusses descriptive and inferential statistics. It also discusses basic probability theory, probability distributions for both discrete and continuous random variables, predictions, probability distributions for functions of random variables, sampling distributions, population parameter estimates, hypothesis tests, nonparametric statistical methods and their applications in business. Where R, EXCEL, SAS or SPSS statistical programs are used to analyze the data.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Administrative Leadership

DMG622

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize foundations, theories and development of leadership.
• study of the role of administrative leadership in achieving the organization's goals.
• Acquire the required skills to develop the administrative leader.
• Analysis of the current status of administrative leaders and the problems that intercept them in the public sector
• Learn about models and mechanisms used to achieve the government leadership.

2. Content:

The course deals with the foundations, theories and development of leadership, the study of prevailing leadership techniques and styles, the study of the role of administrative leadership in achieving the organization's goals, the importance of leadership in the organization and the differences between the leader and the manager, the compulsory skills to develop the administrative leader to carry out his role to the fullest, and an analysis of the current status of administrative leaders and the problems that intercept them in the public sector. The concept of government leadership, its models and mechanisms for achieving it.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Managerial Decision Making

DMG 623

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize how to apply prescriptive analytics such as optimization models and decision analysis in management.
• Use decision models in different applications including data management applications.
• Use software tools to apply decision making concepts.

2. Content:

This course deals with prescriptive analytics including: optimization models, decision analysis, and their applications in management sciences. The course focuses on deterministic and probabilistic decision models. Areas of application include, data management, digital transformation management, corporate planning, finance, marketing, production and operations management, distribution, and project management. Concepts are applied through team projects and tutorials using computer software.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description

Prerequisite

Credits

Course Title

Course Code

-

4

Crisis and Risk Management

DMG624

1. Objectives:

Upon completion of the course, a student will be able to:

• Recognize the most important scientific theories related to risk prediction and crisis response.
• Learn about many case studies related to risks and crises.
• Acquire the necessary skills to manage the risks and crises in the organization.

2. Content:

The course includes an introduction to risks and crises. In addition, the course reviews the most important scientific theories related to risk prediction and crisis response. During the study of the course, many case studies related to risks and crises and how to manage them will be addressed.

3. Assessment Method

• Assignments and exercises
• Quizzes
• Project and Seminars
• Midterm Exam
• Final Exam.

Course Description