Master Programs
Master in Data Science
Overview of the Program
Language English
Duration 2 Years
Level Graduate
Approach Hybrid
Overview
The objective of the Master program in Data Science is to train students as the data scientists, researchers, and data analytics developers of tomorrow both in academia and in industry. The program is a response to the lack of experts in this strategic sector in the country, region, and the world. This gap is expected to grow as the digital transformation of our societies produces increasingly massive amounts of data, most of which is now “born digital”. Identifying patterns and predicting trends is crucial for decision makers in all fields, and this makes Data Scientists sought after in all sectors.
Students must be holders of a Licence or Bachelor degree in computer science, computer engineering, electrical engineering, or a related field.
Admission requirements
Students must be holders of a Licence or Bachelor degree in computer science, computer engineering, electrical engineering, or a related field.
Program objectives
Today, major players in the business world are increasingly aware of the potential of their data and they are looking for ways to exploit and extract the maximum amount of useful information from it. To help them in this task, data scientists are employed to retrieve, store, organize, and process this mass of information in order to extract value from it and to create automated decision support tools using artificial intelligence techniques. The maturity and effectiveness of data analytics technology is accelerating its adoption throughout industry and government, with applications being developed also by SMEs and associations, as well as major government agencies.
The objective of the Master program in Data Science is to train students as the data scientists, researchers, and data analytics developers of tomorrow both in academia and in industry. The program aims to fill the lack of experts in this strategic sector in the country, region, and the world.
The specific objectives of the master program in data science include providing our graduates with:
- A solid foundation in data science concepts, techniques, technologies, and tools.
- A solid foundation in the statistical and mathematical foundations of data science.
- An in-depth understanding of the challenges of developing and managing data analytics solutions and their risks and implications.
Target careers
Graduates of the Master program can continue their education to get a PhD in a related field. They can also join the industry to lead data science projects or to develop and manage data analytics solutions for a variety of applications. Graduates can also join multi-disciplinary teams to launch a startup to develop solutions relying on these technologies to serve the needs of clients in a variety of sectors.
Specific potential careers include:
- Data scientist.
- Data analyst / Business analytics consultant.
- Data engineer.
- Research & development engineer in data mining and knowledge extraction.
- Consultant in information-intensive industries.
- Designer of specialized software solutions for the processing & analysis of large amounts of data.
- Project manager in data-analysis industries.
A few years after successfully completing the Master’s degree in “Data Science”, graduates shall be:
- Employed in industry and demonstrating career advancement through leadership responsibility, significant technical achievement, or other recognition of their contributions.
- Continuing their formal education towards a graduate degree or other professional certification in the field or leading their own technology venture.
- Applying gained knowledge and expertise to develop data science (data collection, analysis, and learning) solutions and applications.
- Working as data analysts, scientists, engineers, consultants in data analytics and business intelligence, research engineers, or information system and data analytics system architects, designers, and managers.
- Demonstrating an in-depth understanding of the challenges faced by industry and society in data analysis.
Certifications
Graduates of the Master program often acquire a number of professional certifications in parallel with their Master’s training thanks to MUST’s Professional Certification Programs (PCP).
In particular, and concurrently with their studies, our students have access to free certifications provided by EC-Council, the owner and developer of the world-famous internationally recognized training programs such Certified Ethical Hacker (CEH), Certified Forensics Investigator, Certified Security Analyst (ECSA), Certified Network Defender, Certified Cloud security engineer, Blockchain developer, among others.
They can also acquire professional certifications offered by leading technology companies, including IBM, Google, and Cisco, through MUST’s Coursera For Campus platform. Certifications are available in various areas including system & network management, cloud computing, cybersecurity, data science, artificial intelligence, machine learning, database management & administration, “Communicating and Interacting Effectively”, and “Thinking Critically and Creatively”.
Program course description
| Semester 1 | ||||
|---|---|---|---|---|
| Course Code | Course Title | UE | ||
| CS 482 | Mathematical foundations of data science | UE 1 | ||
| CS 435 | Big data technologies & applications | UE 2 | ||
| CS 431 | Data management for data scientists | UE 3 | ||
| COM 425 | Advanced technical communication | UET 4 | ||
| CS 521 | Software engineering for data scientists | UEO 5 |
| Semester 2 | ||||
|---|---|---|---|---|
| Course Code | Course Title | UE | ||
| CS 483 | Machine Learning | | UE 6 |
| |
| CS 535 | Scalable big data processing | | UE 7 |
| |
| CS 451 | Distributed systems | | UE 8 |
| |
| COM 435 | Effective professional presentations | | UET 9 |
| |
| CS 470 | Data visualization for data scientists | | UEO 10 |
|
| Semester 3 | ||||
|---|---|---|---|---|
| Course Code | Course Title | UE | ||
| CS 585 | Machine learning II | UE 11 | ||
| CS 555 | Cloud computing for data scientists | UE 12 | ||
| CS 581 | Language Models in Python | UE 13 | ||
| PHIL 222 | Ethics & data privacy | UET 14 | ||
| CS 586 | Data analytics for emerging applications | UEO 15 |
| Semester 4 | ||||
|---|---|---|---|---|
| Course Code | Course Title | UE | ||
| ISS 521 | Master Thesis/Project (Mémoire de Stage de fin d’études ) | UEF 16 |
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