MSc in Data Science (MSc DSc)
Program Overview
Entry Requirements
To be eligible for admission into this programme, a student should satisfy the general University minimum requirements with the following additional requirements:
Direct Entry Qualification
An undergraduate degree in Computer Science, Computer Engineering, Statistics, Mathematics or a degree in related fields from a recognized institution certified by TCU with at least a lower second class award (GPA of at least 2.7 out of 5).
Equivalent Entry Qualification
Advanced Diploma in relevant and respective field with at least lower second class award AND a Postgraduate Diploma in relevant field from a recognized institution certified by TCU with at least an upper second class (GPA of at least 3.5 out of 5) award.
OR
An undergraduate degree with at least a pass in relevant/related field PLUS a Postgraduate Diploma in a relevant and respective field from a recognized institution certified by TCU with at least an upper second class award (GPA of at least 3.5 out of 5).
Registration Process
Detailed information about the registration process and the required supporting documents is available on the main UDSM website under the Directorate of Postgraduate Studies. Open the link https://udsm.ac.tz/directorate-postgraduate-studies
Career Prospects
Graduate of this programme will have the capacity to work as:
- Data Scientists
- Experts in Big Data Analytics
- AI business
- Machine Learning Engineer
- Business Intelligence Developer
- Statistician
Graduates can also venture into research and academia with the prospect of developing research careers in areas such as Artificial Intelligence, Machine Learning, Data Mining, and Data Analytics.
Key Information
- Department
- Department of Computer Science & Engineering (CSE)
- Level
- Postgraduate
- Category
- Postgraduate
Fee Structure
Program Outcomes
Upon completion of the programme, graduates will be able to:
- Understand professional, social, cultural and ethical issues related to data in the context of privacy, confidentiality and data protection.
- Identify and manage scientific and technical risks and uncertainty associated with data science and its applications.
- Demonstrate knowledge and understanding of data science methods and techniques.
- Design and use data visualization tools to communicate information effectively.
- Analyse, design and implement innovative solutions for processing small, medium and large datasets.
- Develop models to improve a particular sector, its planning and its implementation by asking the right questions and answering through the use of data.
- Develop and select robust algorithms and tools that can handle large amounts of data.
- Address technical challenges pertaining to the advent of big data storage and analytics.
- Review and critically evaluate current developments and challenges in data science.