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Bachelor of Science in Data Science and Artificial Intelligence



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:

  • Data acquisition, processing, and analysis
  • Machine learning and deep learning algorithms
  • Natural language processing and computer vision
  • Predictive modeling and decision-making
  • Ethical and responsible data practices

 

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.



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Course Overview

COURSE CODECOURSE NAME
YEAR ISEMESTER I
BDA1106Basic Statistics
BSE1104Python Programming
BIT1100Introduction to Information Communication Technology
BDA1101Digital Electronics
BJC1100Communication Skills and Learning skills for Employability
  
ELECTIVES (Select one)
BSE1101Calculus for Software Engineering
BIT1101Discrete Mathematics
  
YEAR ISEMESTER II
BDA1202Design Thinking
COM1203Numerical Analysis and Computation
BIT1202Computer Networks and Data Communications
BDA1204R Programming
  
ELECTIVES (Select one)
BSE1201Object Oriented Programming
BSE1205Internet and Web Programming
  
Recess Term
BDA1203Data Science Project 1
  
YEAR IISEMESTER I
BDA2102Data Ethics
COM2101Operating Systems
COM2102Data Structures and Algorithms
BDA2104Data Wrangling
BIT1201Data development and Management I
BSE2104Internet of Things
BDA2103ASP.NET & C#
  
YEAR IISEMESTER II
BSE2201Advanced Object-Oriented Programming
BDA2208Microprocessor and Microcontroller
BDA2204Front End Development
BIT2202Research Methodology in Computing
  
ELECTIVES (Select One)
BDA2207Analysis and Visualization
COM2201Simulation & Modelling
  
Recess Term
BDA2209Data Practicum (Internship)
  
YEAR IIISEMESTER I
BDA3105Augmented and virtual reality
BDA3108Business Cloud Computing
BDA3107Advanced Artificial Intelligence & Robotic
BSE3102Machine Learning
BDA3105Research / Minor Project
  
ELECTIVES (Select One)
BSE3102Artificial Intelligence & Expert Systems
BDA3106Advanced Data Science
  
YEAR IIISEMESTER II
BDA3206Deep Learning
BDA3207Cyber security
BDA3205Data Engineering
BDA3200Research / Graduation Project
  
ELECTIVES (Select One)
BDA3208Natural language processing
BDA3204Blockchain technology


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  • Technical Proficiency

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.

  • Hands-on Application

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.

  • Ethical Considerations

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.

  • Adaptability to Emerging Technologies

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.



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  • Technical Skills

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.

  • Data Analysis and Interpretation

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.

  • Machine Learning and AI

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.

  • Problem-solving Skills

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.

  • Ethical Awareness and Responsible AI

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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