Contact UsIntroduction
The College of Computing and Informatics offers Bachelor of Science in Data Science program that aims to enhances and contributes to the National strategic plans for the data scientists needs in order to localizing job market demands. The program focuses on combining the cognitive and applied aspects in the field of data science, machine learning and artificial intelligence; and practically apply these technologies in problem solving
The Importance and Reasons for Creating the Program
- Data science is considered as the most exciting specialty in the twenty-first century, as a result of the great development in usage of Internet technologies, social networking applications and the Internet of things, therefore, we now have huge amounts of data that are difficult to handle and analyze by the traditional statistical methods. Thus, the specialty of data science has become called the oil of the twenty-first century.
- The labor market is still suffering from a severe shortage of qualified data scientists. Thus, this program is introduced to fill in the gap by graduating highly qualified data scientists, who can make use of the latest artificial intelligence techniques to analyze data and extract knowledge.
- Therefore, the College of Computing and Informatics in the Saudi Electronic University presenting an integrated program for the Bachelor of Data Science, that was implemented based on international standards and conform with the latest techniques and methods.
Program Objectives
1. Development of a technically proficient workforce comprising of Saudi citizens capable of carrying out software development projects to the best of international standards.
2. Developing both academic and professional skills in the domain of data science and AI.
3. Enhancing students' experience by enabling them to solve academic and practical problems in their areas of specialization.
4. Implementing best practices to develop comprehensive data analysis projects plans.
5. Preparing students to meet the labor market requirements in data science domains.
6. Integrating the academic programs by bridging the gap between theoretical advances and practical applications.
Duration of Study in the Program
8 semesters.
Program Learning Outcomes
1. Recognize the concepts of computing and mathematics related to the discipline.
2. Master the current techniques, skills, and tools necessary for the computing practice.
3. Demonstrate algorithmic, computational, and statistical models in data science.
4. Comprehend the local and global impact of computing on individuals, organizations, and the society.
5. Analyze a problem, identify and define the computing requirements appropriate to its solution.
6. Apply mathematical foundations, algorithmic principles, and Data science theories in modeling.
7. Implement theories and principles using cutting edge technologies in the analysis, design, implementation and testing of computer-based systems.
8. Construct machine learning and AI optimization models using problem-solving strategies for data analytics.
9. Function effectively on teamwork activities to accomplish a common goal.
10. Identify the needs for continuous development of professional skills with the ability to engage all group members.
11. Develop projects to visualize data for exploration, analysis, and communication.
12. Communicate effectively with a range of audiences, both orally and in a written form, using appropriate media.
Career Opportunities for Graduates of the Program
Data Administrator.
Computer Systems Analyst.
Data Scientist.
Software Developer.
Data Analyst.
Big Data Analyst.
Financial Data Analyst.
Machine Learning Engineer.
Business Intelligence Analyst.
Big Data Administrator.
Data Mining Analyst.
Big Data Architect.
Data Visualization Developer.
University Requirements are 34 credits
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| CS001 | Computer Essentials | 3 | - |
| ENG001 | English Language Skills | 8 | - |
| CI001 | Academic Skills | 2 | - |
| MATH001 | Fundamentals of Mathematics | 3 | - |
| ENG002 | English Language Skills 2 | 8 | - |
| COMM001 | Communication Skills | 2 | - |
| ISLM101 | ISLAMIC FAITH | 2 | Passing the First Year |
| ISLM102 | PROFESSIONAL CONDUCT & ETHICS IN ISLAM | 2 | Pass First Common Year |
| ISLM103 | ISLAMIC ECONOMIC SYSTEM | 2 | ISLM101 |
| ISLM104 | ISLAMIC SOCIAL SYSTEM | 2 | ISLM102 |
| Total | 34 | ||
College requirements: 27 Credits
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| DS230 | Object Oriented Programming | 3 | |
| MATH150 | Discrete Mathematics | 3 | |
| DS240 | Data Structure | 3 | DS230 |
| MATH251 | Linear Algebra | 3 | MATH150 |
| DS350 | Introduction to Database | 3 | DS240 |
| DS351 | Operating Systems | 3 | DS243 |
| DS360 | Computer Networks | 3 | DS243 |
| DS499 | Practical Training | 3 | Passing 86 Credit Hours |
| ENG103 | Technical Writing | 3 | |
| Total | 27 | ||
Specialization requirements: 72 Credits
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| SCI 101 | General Physics 1 | 3 | Passing the First Year |
| DS231 | Introduction to Data Science Programming | 3 | Passing the First Year |
| DS241 | MATH241 | 3 | MATH150 |
| DS242 | Advanced Data Science Programming | 3 | DS231 |
| DS243 | Computer Architecture and Organization | 3 | - |
| SCI 201 | General Physics 2 | 3 | SCI 101 |
| STAT202 | Introduction to Statistics and Probabilities | 3 | MATH150 |
| DS352 | Design and Analysis of Algorithms | 3 | DS240 |
| DS353 | Project Management in Computing | 3 | - |
| DS361 | System Analysis and Design | 3 | DS240 |
| DS362 | Web Programming | 3 | DS350 |
| DS363 | Artificial Intelligence | 3 | DS352 |
| DS364 | Data Curation (Management and Organization) | 3 | DS350 |
| DS470 | Data Security and Privacy | 3 | DS364 |
| DS471 | Machine Learning | 3 | DS363 |
| DS472 | Data Mining | 3 | DS364 |
| DS479 | Senior Project 1 | 3 | DS361, DS362 |
| DS4xx | Elective 1 | 3 | - |
| DS4xx | Elective 2 | 3 | - |
| DS480 | Data Visualization | 3 | DS472 |
| DS481 | Professional Ethics in Data Science | 3 | - |
| DS489 | Senior Project 2 | 3 | DS479 |
| DS4xx | Elective 3 | 3 | - |
| DS4xx | Elective 4 | 3 | - |
| Total | 72 | ||
Track 1 Courses – Artificial Intelligence
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| DS473 | Computer Vision | 3 | DS363 |
| DS474 | Decision Support Systems | 3 | DS363 |
| DS482 | Deep Learning | 3 | DS471 |
| DS483 | Natural Language Processing | 3 | DS471 |
| Total | |||
Track 2 Courses – Big Data Analytics
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| DS475 | Big Data Modelling | 3 | DS363 |
| DS476 | Big Data Integration and Processing | 3 | DS363 |
| DS484 | Big Data Optimization | 3 | DS475 |
| DS485 | Business Intelligence | 3 | DS475 |
| Total | |||
Second Year ( Third Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| SCI 101 | General Physics 1 | 3 | Passing the First Year |
| DS230 | Object Oriented Programming | 3 | Passing the First Year |
| ENG103 | Technical Writing | 3 | Passing the First Year |
| MATH150 | Discrete Mathematics | 3 | Passing the First Year |
| DS231 | Introduction to Data Science Programming | 3 | Passing the First Year |
| ISLM101 | ISLAMIC FAITH | 2 | Passing the First Year |
| Total | 17 | ||
Second Year ( Fourth Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| MATH251 | Linear Algebra | 3 | MATH150 |
| DS240 | Data Structure | 3 | DS230 |
| MATH241 | MATH241 | 3 | - |
| DS242 | Advanced Data Science Programming | 3 | DS231 |
| DS243 | Computer Architecture and Organization | 3 | - |
| ISLM102 | PROFESSIONAL CONDUCT & ETHICS IN ISLAM | 2 | - |
| Total | 17 | ||
Third Year ( Fifth Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| SCI 201 | General Physics 2 | 3 | SCI 101 |
| DS350 | Introduction to Database | 3 | DS240 |
| DS351 | Operating Systems | 3 | DS243 |
| STAT202 | Introduction to Statistics and Probabilities | 3 | MATH150 |
| DS352 | Design and Analysis of Algorithms | 3 | DS240 |
| DS353 | Project Management in Computing | 3 | - |
| Total | 18 | ||
Third Year ( Sixth Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| DS360 | Computer Networks | 3 | DS243 |
| DS361 | System Analysis and Design | 3 | DS240 |
| DS362 | Web Programming | 3 | DS350 |
| DS363 | Artificial Intelligence | 3 | DS352 |
| DS364 | Data Curation (Management and Organization) | 3 | DS350 |
| ISLM103 | ISLAMIC ECONOMIC SYSTEM | 2 | ISLAM 101 |
| Total | 17 | ||
Fourth Year ( Seventh Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| DS470 | Data Security and Privacy | 3 | DS364 |
| DS471 | Machine Learning | 3 | DS363 |
| DS472 | Data Mining | 3 | DS364 |
| DS479 | Senior Project 1 | 3 | DS361, DS362 |
| DS4xx | Elective 1 | 3 | - |
| DS4xx | Elective 2 | 3 | - |
| Total | 18 | ||
Fourth Year ( Eighth Semester )
| Course Code | Course Name | Credit Hours | Prerequisites |
|---|---|---|---|
| ISLM104 | ISLAMIC SOCIAL SYSTEM | 2 | ISLAM 102 |
| DS480 | Data Visualization | 3 | DS472 |
| DS481 | Professional Ethics in Data Science | 3 | - |
| DS489 | Senior Project 2 | 3 | DS479 |
| DS4xx | Elective 3 | 3 | - |
| DS4xx | Elective 4 | 3 | - |
| DS499 | Practical Training | 3 | Passing 86 Credit Hours |
| Total | 20 | ||
Practical Training
Refers to a course in Bachelor Data Science programs in the college. Practical training as a course makes students from the college of Computing and Informatics responsible for having to spend a 560 training hours for six months. in their fields of specialization either in private or public organization. At the completion of training, student will have to show his grasp of most relevant and state of the art technologies in their relevant specialization
Terms of Cooperative Training
- The training organization should fill in the form and stamp it.
- The training start date must be consistent with what was specified in the announcement.
- The total training hours must not be less than 560 hours.
- The training tasks written in the form must be related to Computer Science , and not less than five different tasks.
- That the training be attended at the training headquarters, not remotely, and the party is approved through the registration form
- Any training initiated by the student directly without obtaining final approval to start training from the Training Committee in the college will not be considered.
- Failure to submit any of these reports on the specified dates leads to Fail in the course.
Registration Terms:
1. The sgister for the cooperative training course before the start of the semester allocated for training.
2. The student must have passed (the basic courses and completed
the number of hours) required to register for the cooperative training
course in his/her specialization.
3. The student can register courses with the cooperative training
course not exceeding the maximum limit for registering academic
units.
4. The number of hours earned must be at least 86 hours for the
semester in which the student wishes to register for the cooperative training course.
Registration Steps
- First, each student issues a letter addressed to the training organization in which the student will train from the icon in Student Services.
- The student prints the letter directly from Student Services. (You must make sure that the written semester in the letter is “the Second Semester” - the letter will be updated starting from the start of registration date)
- The student should take the letter and registration form to the training organization.
- The training organization should fill in the form and stamp it.
- The total training hours must not be less than 560 hours.
- The training tasks written in the form must be related to Data Science , and not less than five different tasks.
- The student will be contacted if the data in the form is rejected or needs to be changed.
- The training committee reviews the files, and if acceptance is granted, the student receives an email with the initial approval. If a student didn’t receive the initial approval, this means that the approval has not been granted and the student has not been registered for practical training for this semester.
- Approval of the registration is preliminary one, and the final approval is obtained only if the student exceeds 86 credit hours earned by the end of the current semester. If students didn’t earn 86 credit hours by the end of this semester, the registration of the training is canceled automatically.
- The final approval will be sent to the student on his email after publishing this semesters’ results.
Course Requirements (Summary for important points from the practical training guide)
- Attendance at the training organization that was approved by the college.
- Complete all tasks assigned by the training organization.
- Attending weekly lectures with the training supervisor from the college on the blackboard.
- Required Reports:
- A report from the supervisor at the training organization on the student’s performance in the middle of the training period.
- A report from the supervisor in the training organization on the student’s performance at the end of the training period.
- A report from the student about what has been achieved, learned and the challenges he/she faced during the training period.
- A research report from the student on the latest trends in the field of Data Science (this topic will be discussed and explained in detail in weekly lectures with the supervisor from the college).
- Failure to submit any of these reports on the specified dates leads to Fail in the course.
Cooperative Training Documents and Forms
- Practical Training Guide Click here
- Registration form Click here
- Student training forms Click here
- Cooperative training letter (from student services - other - request for field and practical training).
Contact us
For any inquiries about cooperative training program please, contact Cooperative Training Unit cci.ds.spt@seu.edu.sa
For study plan files
Study Plan for Bachelor of Data Science Program 2023 Click here
