January Intake Ongoing
The Bachelor of Science in Data Science and Artificial Intelligence programme (BSDAI) at Cavendish University Uganda (CUU) is a three-year interdisciplinary programme designed to equip students with the knowledge and skills required to thrive in today’s dynamic and data-driven world. This comprehensive programme integrates key concepts from computer science, mathematics, statistics, and artificial intelligence, providing students with a strong foundation for understanding and applying cutting-edge technologies through a blend of theoretical coursework and hands-on projects.
Students will develop expertise in areas such as:
The curriculum is structured to ensure that graduates are well-prepared to tackle the challenges and opportunities presented by the exponential growth of data and the rapid advancements in artificial intelligence. Students will gain proficiency in programming languages, data visualisation tools, and industry-standard software, enabling them to translate their knowledge into practical solutions. Through collaborative projects and real-world case studies, students will learn to work in multidisciplinary teams, fostering the interpersonal skills necessary for success in the data science and AI fields.
Graduates of the BSDAI programme will be equipped to pursue a wide range of career paths, including data scientist, machine learning engineer, business intelligence analyst, and AI research scientist. The program’s strong industry connections and emphasis on practical applications ensure that students are well-prepared to contribute to the growing demand for data-driven decision-making and intelligent systems in various sectors, such as healthcare, finance, marketing, and smart city development.
| COURSE CODE | COURSE NAME |
|---|---|
| YEAR I | SEMESTER I |
| BDA1106 | Basic Statistics |
| BSE1104 | Python Programming |
| BIT1100 | Introduction to Information Communication Technology |
| BDA1101 | Digital Electronics |
| BJC1100 | Communication Skills and Learning skills for Employability |
| ELECTIVES (Select one) | |
| BSE1101 | Calculus for Software Engineering |
| BIT1101 | Discrete Mathematics |
| YEAR I | SEMESTER II |
| BDA1202 | Design Thinking |
| COM1203 | Numerical Analysis and Computation |
| BIT1202 | Computer Networks and Data Communications |
| BDA1204 | R Programming |
| ELECTIVES (Select one) | |
| BSE1201 | Object Oriented Programming |
| BSE1205 | Internet and Web Programming |
| Recess Term | |
| BDA1203 | Data Science Project 1 |
| YEAR II | SEMESTER I |
| BDA2102 | Data Ethics |
| COM2101 | Operating Systems |
| COM2102 | Data Structures and Algorithms |
| BDA2104 | Data Wrangling |
| BIT1201 | Data development and Management I |
| BSE2104 | Internet of Things |
| BDA2103 | ASP.NET & C# |
| YEAR II | SEMESTER II |
| BSE2201 | Advanced Object-Oriented Programming |
| BDA2208 | Microprocessor and Microcontroller |
| BDA2204 | Front End Development |
| BIT2202 | Research Methodology in Computing |
| ELECTIVES (Select One) | |
| BDA2207 | Analysis and Visualization |
| COM2201 | Simulation & Modelling |
| Recess Term | |
| BDA2209 | Data Practicum (Internship) |
| YEAR III | SEMESTER I |
| BDA3105 | Augmented and virtual reality |
| BDA3108 | Business Cloud Computing |
| BDA3107 | Advanced Artificial Intelligence & Robotic |
| BSE3102 | Machine Learning |
| BDA3105 | Research / Minor Project |
| ELECTIVES (Select One) | |
| BSE3102 | Artificial Intelligence & Expert Systems |
| BDA3106 | Advanced Data Science |
| YEAR III | SEMESTER II |
| BDA3206 | Deep Learning |
| BDA3207 | Cyber security |
| BDA3205 | Data Engineering |
| BDA3200 | Research / Graduation Project |
| ELECTIVES (Select One) | |
| BDA3208 | Natural language processing |
| BDA3204 | Blockchain technology |
Students will develop strong technical skills in at least two programming languages and relevant data science frameworks, enabling them to effectively implement and evaluate machine learning models for various data types.
Students will complete industry-relevant projects to simulate challenges faced in professional settings, and gain practical experience through internships or collaborative education opportunities with organisations, preparing them for hands-on real-world applications.
Students will complete coursework on ethical considerations in AI, addressing bias, fairness, transparency, and privacy, to be able to navigate and address ethical dilemmas in AI applications. Case studies or projects, will be applied to foster a responsible and ethical approach to AI.
Students will demonstrate familiarity with and the ability to use the latest tools and technologies in data science and AI, involving emerging trends such as edge computing or federated learning and ensuring they are equipped to adapt to the rapidly evolving field.
Upon completion of the programme, students will demonstrate proficiency in programming languages commonly used in data science and artificial intelligence, such as Python and be able to apply mathematical and statistical concepts to analyze and interpret data effectively. They will also be able to demonstrate knowledge of machine learning algorithms and techniques to solve data-driven problems.
Students will be able to collect, clean, and preprocess data from various sources for analysis, interpret, and communicate insights effectively using data visualization techniques as means of informing decision making processes.
Students will demonstrate knowledge in fundamental machine learning concepts, including supervised or unsupervised learning, regression, classification, clustering, and developing machine learning models using appropriate algorithms and techniques. They will also be able to apply artificial intelligence principles to develop intelligent systems capable of learning from data.
Students will be able to identify and define data-driven problems in diverse domains, design and implement effective solutions using data science and AI techniques, in addition to evaluating and improving the performance of applied solutions.
Upon graduation, students will be able to recognise ethical considerations and biases inherent in data and AI applications. They will be able to apply ethical frameworks to guide decision-making, mitigate potential risks, and design AI systems that prioritize fairness, transparency, and accountability.
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January Intake Ongoing!
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January Intake Ongoing!
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