Decision making is increasingly becoming data driven within the commercial sector and the government. Data scientists are a new breed of analytics professionals who are responsible for collecting, analyzing and interpreting data to help and drive decision-making in an organization. The data scientist role combines elements of several technical skills to solve complex problems and learn to explore competencies of mathematicians, scientists, statisticians and computer programmers.

Purpose Of Programme

BSc in Data Science aims to expose the learner to the holistic approach of gathering, analyzing, and interpreting data for a variety of problems. The programme designed to highlights a balanced coverage of modules in artificial intelligence, machine learning, computer science and statistics with focus on data science with science and business, exploring the potential of data and enhancing the creativity of learners in developing innovative solutions. This formal academic programme in data science is only just emerging, there is a huge gap between the demand for data scientists and the supply of suitable qualified applicants in local and global job markets.

Learning Outcomes

By the end of the Course period learners should be able to:

  • Understanding of how to use dynamic and probabilistic models to make decisions.

  • Apply a range of specialized technical data processing models to get insight to enhance organizational performance through the effective use of data.

  • Enhance organisational performance through the effective use of data.

  • Adopt high standards of ethical and professional use of data.

  • Understanding of the algorithmic principles behind data mining and machine learning, including optimization.

  • Mastery of skills needed to manage and analyze massive structured and unstructured datasets.

  • Mastery of pre-process data using specialized data mining techniques in a small to medium organisation.

  • Ability to design experiments and identify data and features needed to test hypotheses and report outcomes.

  • Apply foundational methods to address challenging problems in at least one domain.

  • Interpret and explain results, including developing narratives and data visualizations.

  • Assess the social impacts of data centered methods, including ethical considerations, fairness, bias, privacy, security, and ethical norms.

  • Ability to collaborate and communicate with teammates from domain disciplines, including the ability to deliver a significant project focused on a particular domain.

Enrolment Criteria

Normal Entry

  • BGCSE or equivalent (Best 6 subjects, 36 points and above)
  • A pass in at least six (6) subjects in BGCSE or IGCSE or equi valent. These subjects must include a minimum of pass in Mathematics and in English plus a pass in one of following science subjects -Biology, Chemistry, Physics, Additional Mathematics.
  • OVC / RAC / SEN (Best 6 subjects, 31 points and above)

  • NCQF Level 4 in TVET (in relevant certificate IV)
  • Discontinued OVC / RAC / SEN.

Mature Entry

  • Mature Entry is provided for candidates who meet the necessary criteria.
  • Progression / Reinstatements students considered based on recognition of prior learning (RPL).

Modules Covered

  • Computational Thinking 1

  • Programming Skill (Phython)

  • Mathematics for Data Science 1

  • Writing and Communication

  • Conceive, Launch and Grow Start-Ups

  • Computational thinking 2

  • Mathematics for Data Science 2

  • Programming Skill (R)

  • Computer Architecture

  • Business and Entrepreneurship

  • Computer Networks

  • Data Structures and Algorithms

  • Operating Systems

  • Discrete Mathematics for Computer Science

  • Calculus

  • Ethics and Professional Growth Strategies

  • Programming skill (Scala)

  • Machine Learning (Foundation)

  • Computational Statistics

  • Research Methodology

  • Computational Finance (Elective Module)

  • Computational Biology (Elective Module)

  • Machine Learning (Techniques)

  • Data Visualisation

  • Multivariate Statistics

  • Project Management

  • Databases & Business Data Management

  • Linear Algebra

  • Probability and Statistics

  • Machine Learning (Practice)

  • Deep learning

  • Data Analytics

  • Industry 4.0 (Elective)

  • Data Science for Engineers (Elective)

  • Reinforcement Learning

  • Data Mining

  • Artificial Intelligence: Search Methods for Problem Solving

  • Big Data Technologies

  • Individual Project in Data Science

  • Industrial Attachment

Career Pathways

A rapidly growing field providing learners with exciting career paths and opportunities for advanced study, Data Science combines the computational and inferential ways of thinking. Data science experts are needed in almost every field. Millions of businesses and government departments rely on big data to succeed and better serve their customers. Data science careers are in high demand and this trend will not be slowing down any time soon, if ever. The learning programme is an ideal way to take a start in this highly demanding and lucrative field of Data Science assistance with the following jobs titles:

  • Data Scientist

  • Machine Learning Engineer

  • Machine Learning Scientist

  • Enterprise Architect

  • Data Analyst

  • Data Architect

  • Data Engineer

  • Statistician

  • Business Intelligence (BI) Developer