Bachelor of Data Science
Programme Background and Rationale
Data Science (DS) is an inherently interdisciplinary field. The rise of data science is directly connected to the rise of large data sets in nearly every topic domain across the world. The physical sciences, social sciences, business, humanities, and engineering all are seeing opportunities for discovery and decision-making expanded by unprecedented large amounts of raw or structured data, too large to be subjected to effective human analysis without the automation of the processes. Data Science brings together domain data, computer science, and the statistical tools for interrogating the data and extracting useful information in a multi-disciplinary approach.
Big Data and analytics have become invaluable segments of every business in the world but as demand for data scientists grows globally, a talent shortage exists, with its effect being worst felt in Africa. Faced with a myriad of industrial bottlenecks, Africa needs data scientists more than any other continent, in order to create tailored solutions to its challenges. Data sits at the center of the global Fourth Industrial Revolution (4 IR) with analysts believing now is Africa’s opportunity to create its own talent to help businesses make informed decisions, predict markets, and prepare for unforeseen calamities.
Somaliland’s government and industry have recognized data science as a great opportunity for sustainable economic development. Somaliland has all key attributes to develop in this sector except skilled workers including shortage of data scientists. This is a pertinent issue identified by the government and business community.
Amoud University bachelor of data science programme therefore will not only help organisations to become more effective, but will also build capacity and boost industry potential. With its high-speed internet link and enormous potential for solar and wind power, Somaliland has great potential to host data centres. But it will need skilled man power to accompany these prospects. The programme will provide an avenue for Somaliland to bridge the gap in home grown skilled man power and export data science experts to the rest of the region.
The Amoud University bachelor of data science programme is built with strong link to Big Data and data driven technologies to create transformational effect on research and industry domains, and inspires re-thinking and re-design of both traditional educational models and existing courses.
This programme has been developed in consonance with Association for Computer Machinery (ACM) Data Science Task Force (Competencies for Undergraduate Data Science Curricula, https://dl.acm.org/citation.cfm?id=3453538, January 2021)
Programme Objectives
General Objective
The general objective of this programme is to produce Data Science experts with a strong focus on the collection, manipulation and analysis of large volumes of data and how to use this to bring about beneficial insights and changes to an organization and to the wider society.
Specific Objectives
By the end of the programme, students should be able to:
- Use common programming languages such as Python and R or other domain specific libraries to manipulate and analyze large data in both private and public data depositories.
- Efficiently understand gigantic data from multiple sources and derive valuable insights to make smarter data-driven decisions in various industry domains, including marketing, healthcare, finance, banking, policy work.
- Explain and interpret the numerical conclusions in the client’s terminology, and deliver text and graphics ready to be digested by non-technical personnel.
Programme Duration
The programme run on a semester basis consisting of four semesters each lasting 17 weeks, or part-time basis as appropriately structured by the Faculty of Computing and Informatics and department of data science, and approved by Senate.
Programme Modalities
The programme shall be conducted by course work, examinations and project, based on credit units. The last two weeks of each semester of each academic year shall normally be used for end-of-semester examinations. Further,
- A student shall take foundation/general courses in English, mathematics, science, Arabic, Islamic studies during the first one year in the freshmen. The freshman year also acts as a gateway to full admission into the bachelor of data science programme.
- By the end of junior year, a student shall be required to attend at least 300 hours of internship in a real work environment.
- A student shall come up with a well-written project in the area of data science during the senior year of study.
Curriculum Plan
The programme shall run on a semester basis consisting of eight (8) semesters each lasting 17 weeks in a period of four (4) academic years, whether on full time, part-time, sandwich, or online or other modes as structured by the Faculty of Computing and Informatics, and department of data science, and approved by Senate.
Freshman semester I |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
FRM 6111 | Introduction to reading | 45 | 0 | 0 | 0 | 45 | 3 | |||||
FRM 6112 | Introduction to writing | 45 | 0 | 0 | 0 | 45 | 3 | |||||
FRM 6113 | Introduction to biology | 45 | 0 | 0 | 0 | 45 | 3 | |||||
FRM 6114 | Arabic language | 45 | 0 | 0 | 0 | 45 | 3 | |||||
FRM 6115 | Pre-calculus | 45 | 0 | 0 | 0 | 45 | 3 | |||||
Total |
| 225 | 0 | 0 | 0 | 225 | 15 | |||||
Freshman semester II |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
FRM 6121 | English academic writing | 45 | 0 | 0 | 0 | 45 | 3 | |||||
FRM 6122 | Islamic studies | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6121 | Introduction to probability and statistics | 45 | 0 | 45 | 3 | |||||||
BDS 6122 | Fundamentals of information technology | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6123 | Introduction to physics | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6124 | Fundamentals of computer networks | 45 | 0 | 0 | 0 | 45 | 3 | |||||
Total |
| 270 | 0 | 0 | 0 | 270 | 18 | |||||
Sophomore semester I |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6211 | Introduction to data analytics and statistical packages | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6212 | Computer architecture and organization | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6213 | Fundamentals of data science | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6214 | Introduction to computer programming (C ) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6215 | Web design and publishing (HTML/CSS) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6216 | Linear Algebra | 45 | 0 | 0 | 0 | 45 | 3 | |||||
Total |
| 225 | 0 | 135 | 0 | 360 | 18 | |||||
Sophomore semester II |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6221 | Python programming | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6222 | Client-side web programming (JavaScript) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6223 | Operating systems | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6224 | Database management system I (Principles of DBMS in Access) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6225 | Regression modeling and multivariate analysis | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6226 | Discrete mathematics | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6227 | Research methodologies in DS | 45 | 0 | 0 | 0 | 45 | 3 | |||||
Total |
| 255 | 0 | 180 | 0 | 435 | 21 | |||||
Junior semester I |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6311 | Artificial intelligence | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6312 | Java programming | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6313 | Database management systems II (Enterprise database development in SQL server) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6314 | Data collection methods and tools in DS | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6315 | Data structures and algorithms | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6316 | Time series analysis | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6317 | Network design and implementation | 30 | 0 | 45 | 0 | 75 | 3 | |||||
Total |
| 240 | 0 | 225 | 0 | 465 | 21 | |||||
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Junior semester II |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6321 | Cloud computing (MS Azure/ AWS) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6322 | Data visualization (with R) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6323 | Machine learning | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6324 | ANOVA designs for data science | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6325 | Data mining and data warehousing | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6326 | Probability theory | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6327 | Database management system III (Mongo-DB) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
Total |
| 240 | 0 | 225 | 0 | 465 | 21 | |||||
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Junior semester III | ||||||||||||
Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6331 | Industrial attachment and report | 0 | 0 | 0 | 300 | 300 | 5 | |||||
Total |
| 0 | 0 | 0 | 300 | 300 | 5 | |||||
Senior semester I |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6411 | Data science project management | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6412 | Modeling and simulation | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6413 | Mobile app development (React Native) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6414 | Big data and analytics | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6415 | Data science individual project I (Proposal) | 15 | 30 | 0 | 0 | 45 | 2 | |||||
BDS 6416 | Bayesian statistics | 45 | 0 | 0 | 0 | 45 | 3 | |||||
Total |
| 225 | 30 | 135 | 0 | 360 | 17 | |||||
Senior semester II |
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Course Code | Course Title | LH | TH | PH | FH | CH | CU | |||||
BDS 6421 | Professional dashboards (Tableau and Excel) | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6422 | Deep learning and decision making | 30 | 0 | 45 | 0 | 75 | 3 | |||||
BDS 6423 | Data privacy and security | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6424 | Ethics of data analytics | 45 | 0 | 0 | 0 | 45 | 3 | |||||
BDS 6425 | Data science individual project II (Implementation and report) | 30 | 30 | 0 | 0 | 45 | 3 | |||||
BDS 6426 | Advanced machine learning models | 30 | 0 | 45 | 0 | 75 | 3 | |||||
Total |
| 210 | 30 | 135 | 0 | 360 | 18 | |||||
Grand Total |
| 1890 | 60 | 1035 | 300 | 3240 | 154 | |||||
General Regulations
General University regulations and guidelines for undergraduate programmes shall apply with regard to application, registration, teaching, learning, examinations, research and graduation. A student is required to sign a memorandum of understanding during the registration process on pre-scribed forms.
Admission Requirements
This bachelor of data science programme is best suited to students with interest in becoming future data scientists or big data analysts in business tech firms. To qualify for admission into the degree of bachelor of data science, an applicant shall:
- Hold a Somaliland GCSE of at least Grade “C”, or its equivalent from a recognized examination body.
- Successfully complete a one-year freshman programme at Amoud University, and attain a GPA of at least 2.00.
- Meet other conditions as spelt out by the Faculty of Computing and Informatics and the department of data science, with approval of Senate.
Enrolment Types
There shall be three (3) categories of enrolments as follows:
- Full-Time Day face-to-face enrolled students who shall attend classes between 7.30 am 4.30 pm from Saturday to Thursday.
- Full-Time Evening face-to-face enrolled students who shall attend classes between 4.00 pm and 10.00 pm from Saturday to Thursday.
- Sandwich, part-time, and other modes enrolled students who shall attend classes during specific periods as specified by the Faculty of Science and department of data science, and approved by Senate.
Staff, Facilities and Equipment
The faculty of computing and informatics, and the department of data science have adequate resources to offer this programme. The faculty has three (3) functional and well equipped computer laboratories fitted with over-head digital projectors and personal computers. In addition, the faculty has an engineering and computational laboratory, with state-of-the-art telecommunications equipment, including transport nodes, data packet routers, voice over IP gears, and a cluster of Linux workstations for protocols development and testing, that provides extensive facilities for research in telecommunications, microelectronics, and computer science. The telecommunication laboratory has training kits for Fiber optics, Antennas, microwaves, among other areas. Students attend extended industrial attachment training periods to gain experience get exposed to the real equipment used in software engineering.
There is free WiFi 24/7 on campus internet connections in all computer laboratories and surrounding areas hosted by two local internet service providers, with a total of 20Mbps download and 20Mbps upload internet speeds. The main university library has an array of information materials for further reading, and several electronic books and reference materials are also available through Amoud University Google classroom suite. The faculty has a well balanced team of highly competent local and non-local staff, trained locally and abroad. The teaching staff is readily available to help students as needs arise. The programme is allocated sufficient time and with efficient management, the faculty ensures that students complete their courses on time.
Funding
The programme of bachelor of data science is sustained largely by funds from tuition fees and other relevant levies. The student must show proof of ability to pay tuition fees on time before being enrolled into the programme. There are no special funding arrangements for the students enrolled in the programme of bachelor of data science. However, if and when, additional funds are available, they shall be dispensed according to the university financial policies.
Target Groups
The target group for the bachelor of data science programme includes but is not limited to:
- Students who have successfully completed Somaliland General Secondary School Certificate (SGCSE) or an equivalent from a recognized examination body and who wish to pursue a career.
- People already working in the field of data science or related fields such as machine learning, business intelligence, statistics or data mining but would like to add new skills set to their professional work and attain formal advanced certification in data science.
- Other persons with insight in data science and would like to pursue the challenge of data science at the undergraduate degree level.
Expected Programme Outcomes
The Graduates of the bachelor of data science programme should be persons who are:
- Consultants in a range of data-driven industries and organisations as; analytics specialists; business intelligence developer; data analysts ; data architects; data engineers; data miners; data scientists; machine learning engineers; programmers; research data scientists or web analysts.
- Statistical analysts who will apply statistical and computational tools to applied problems, and clearly communicate the results in both written reports and oral presentations.
- Data management specialists who develop and implement strategies for data collection, storage, preservation, and availability for further processing in organizations.