MKWAWA UNIVERSITY COLLEGE OF EDUCATION

Programme Title:

Bachelor of Science in Data Science and Artificial Intelligence (BScDSAI)

Offering Department

Department of Mathematics, Physics and Informatics

Entry Requirements

Direct Entry:

1. Certificate of Secondary Education Examination (C.S.E.E.) or equivalent, with passes in FIVE approved subjects THREE of which must be at Credit level obtained prior to sitting for the Advanced Certificate of Secondary Education Examination (A.C.S.E.E.) or equivalent; And 

2. Two principal level passes in appropriate subjects in the A.C.S.E.E. or equivalent with total points from three subjects not below 5 based on the following grade to point conversion scale: A = 5; B = 4; C = 3; D = 2; E = 1; S = 0.5 and F = 0 point. And

3. Equivalent qualifications: Diploma or equivalent in either Computer Science, Information Technology, Statistics, Science, Mathematics and Physics, or Mathematics and Chemistry, and other relevant diploma programmes from an accredited institution such as NACTE recognized by UDSM Senate with an average of “B+’’ or a minimum GPA of 3.5.International students with equivalent qualifications recognized by the University of Dar es Salaam and TCU.

Programme description

The Bachelor of Science in Data Science and Artificial Intelligence (BScDSAI) programme is designed to equip students with the theoretical foundations and practical competencies to collect, analyse, and interpret complex data, as well as to develop intelligent systems that address real-world challenges. By integrating data science, machine learning, artificial intelligence, and statistical modelling, the programme ensures that graduates can harness the power of data to drive innovation and informed decision-making across diverse sectors.

Emphasising computational thinking, ethical AI practices, and applied problem-solving, BSCDSAI trains students in data mining, predictive analytics, natural language processing, computer vision, and deep learning. The curriculum also incorporates emerging technologies such as big data platforms, cloud computing, and Internet of Things (IoT) integration to enhance data-driven solutions and scalable AI applications. A core aspect of the programme is responsible AI development and human-centred design. Students will engage in project-based learning, industry collaborations, and capstone research initiatives, ensuring that AI systems are developed with fairness, transparency, and accountability. Additionally, the programme aligns with global best practices in data governance, cybersecurity, and ethical AI policy-making, preparing graduates for careers in technology firms, financial institutions, healthcare, government agencies, research organisations, and entrepreneurial ventures. Ultimately, BDSAI produces professionals capable of transforming data into actionable insights and building intelligent systems that improve lives, drive economic growth, and address pressing societal needs, while ensuring that technological advancement remains inclusive and ethically sound.

Programme Learning Outcomes

Knowledge: Graduates will be able to:

1. Demonstrate comprehensive understanding of statistical and mathematical foundations underlying data science and artificial intelligence, including probability theory, linear algebra, and calculus.

2. Exhibit proficiency in data analysis and visualisation techniques, utilizing modern tools and libraries to extract meaningful insights from structured and unstructured data.

3. Master a wide range of machine learning algorithms, including supervised, unsupervised, and reinforcement learning methods, and understand their theoretical underpinnings and practical applications.

4. Acquire in-depth knowledge of big data architectures, distributed computing frameworks, and cloud-based data storage and processing systems.
5. Understand the ethical, legal, and social implications of artificial intelligence, including issues of bias, fairness, transparency, accountability, and data privacy.
6. Comprehend the principles of database design, data warehousing, and data governance, ensuring data integrity, security, and regulatory compliance

Skills: Graduates in BScDSAI programme will have specialised skills for:

1. Develop and deploy deep learning and neural network models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, for complex pattern recognition tasks.

2. Implement big data technologies such as Apache Hadoop, Apache Spark, and distributed databases to process and analyse massive datasets efficiently.

3. Build natural language processing (NLP) systems for tasks such as text classification, sentiment analysis, machine translation, named entity recognition, and question-answering.

4. Design and implement data visualisation dashboards and interactive reports using tools such as Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn, Plotly) to communicate findings effectively to diverse stakeholders.

5. Write efficient, scalable, and maintainable code in programming languages such as Python, R, and SQL, and applying software engineering best practices including version control, testing, and documentation.

6. Apply feature engineering, dimensionality reduction, and model selection techniques to optimise predictive performance and generalizability of machine learning models.

Competence: Graduates will demonstrate competence in:

1. Managing end-to-end data science projects, from problem formulation and data acquisition to model deployment and performance monitoring, within real-world organisational contexts.

2. Designing and implementing robust data management systems, including relational and non-relational databases, data lakes, and data pipelines, ensuring data quality, accessibility, and security.

3. Critically evaluating and selecting appropriate AI and machine learning methodologies for diverse application domains, such as healthcare, finance, education, transportation, and agriculture.

4. Communicating complex technical concepts and analytical results clearly and persuasively to both technical and non-technical audiences, including policymakers, business leaders, and community stakeholders.

5. Demonstrating professional and ethical responsibility in the development and deployment of AI systems, ensuring adherence to legal frameworks, organisational policies, and societal values.

6. Leading interdisciplinary teams and collaborating effectively with domain experts, software engineers, and business professionals to deliver innovative data-driven solutions that address real-world challenges.

Programme structure 

BScDSAI is a three-year program structured into six semesters, comprising a blend of core and elective courses, practical training components, and a final-year project.

Assessment 

The programme assessment will be based on a combination of continuous assessment and a final examination. For theory courses, coursework will carry 40%, and the final examination will

carry 60% of the total marks, while for method courses, coursework and final examination both will carry 50% each. Practical Training will also be assessed in accordance with the UDSM practical training regulations and procedures (2023).