SCHOOL OF NATURAL & APPLIED SCIENCES
PROGRAMME
BACHELOR OF SCIENCE (HONS) DATA SCIENCE
Period of study: 1 Year (Full-time); 2 Years (Part-time)
Background
The programme has been designed to specifically focus on computing structures that support “Big Data” challenges. Graduates will contribute immensely in solving analytically complex problems in real life settings such as in industry, Government and other forms of organisations at national and international level. This Honours programme is designed to address challenges of digital transformation that can be attributed to lack of skills and knowledge to efficiently transform data science and its technologies.
Admission requirements
(a) The General Rules of Sol Plaatje University in respect of admission to Bachelor Honours Degrees (aligned with the Higher Education Qualification Sub-Framework: HEQSF) are applicable to this degree.
(b) To be admitted to the Bachelor of Science (Honours) programme, a student must be in possession of an acknowledged Bachelor qualification at NQF Level 7 or cognate qualification, with an average of at least 60% in for major subjects in the final undergraduate year.
(c) The formal university’s Recognition of Prior Learning (RPL) Policy may be applied in instances where applicants do not meet the minimum admission requirements for entry into the Honours Degree.
Programme Structure
The Bachelor of Science Honours in Data Science is a postgraduate qualification at NQF Level 8 and consists of at least 120 Credits.The BSc.Hons in Data Science comprises of seven compulsory modules and one elective module. The Elective Module offered is prescribed by the Head of Department, pursuant to relevant staff, resources and current topic of interest considerations.
show more 1.1.1 Module Information At the end of the module students are expected to be able to: · Conceptualise principles and theory of machine learning for algorithmic design. · Problematise models for supervised, unsupervised, and reinforcement machine learning for analysis of strength and weakness of respective models. · Interpretation and solve mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models. · Design test procedures in order to evaluate a model · Experiment several models in order to gain better results · Analyse and make choices for modelling new machine learning tasks based on reasoned argument. · Introduction to Upstream exploratory analysis · Machine learning and Clusters · Introduction to Scala · Spark Applications · Configuration of Spark Nodes · Machine learning and Spark · Working with Distributed Datasets · Streaming At the end of the module students are expected to be able to: · Investigate and synthesize a data-oriented problem area · Apply specialist knowledge through use of specialized architectures and operations. · Experiment, perform data analysis and demonstrate results through use of upstream programs such as Spark. · Application of log mining, textual entity recognition and collaborative filtering techniques to real-world data questions Less
Module Code NRPJ840 Module Name Research Project Module Description The module is about exploring real world data science challenges and applying relevant research ethics, language and processes such as quantitative or qualitative approaches to address problems. Module Content Students will take full responsibility of work and use appropriate resources where necessary. This module is the research project of the programme Learning Outcomes At the end of the module the students will be expected to demonstrate knowledge in the application of research methodologies, frameworks and research skills acquired from the programme to engage Data Science challenges emanating for industry, government and other organisations. Module Code NCSD841 Module Name Computer systems for big data Module Description The module is an introduction to large-scale distributed systems with an emphasis on big-data processing and storage infrastructures. This course focuses on the computer systems aspects and how various parts of a big data computer system (hardware, system software, and applications) are put together, what are the appropriate approaches to realize high performance, scalability, and reliability in practical big data computer systems. Module Content Content include fundamental tradeoffs in distributed systems, techniques for exploiting parallelism, big-data computation and storage models, design and implementation of various well-known distributed systems infrastructures, and concrete exposure to programming big-data applications on top popular, open-source infrastructures for data processing and storage systems. Learning Outcomes At the end the module, students are expected to synthesis and anayse large scale-data problems emanating from distributed infrastructures for application to real-life scenarios. Module Code NLSO841 Module Name Large scale optimization Module Description This module focuses on optimization techniques used to find solutions of large-scale problems that typically appear in statistical learning / data analysis tasks with big data. Module Content Content include projected gradient methods, accelerated first order algorithms, conjugate gradient methods, quasi-Newton methods, block coordinate descent, proximal point methods, stochastic sub-gradient algorithms, alternating direction method of multipliers, semi-definite programming, interior-point algorithms for conic optimization, interior-point algorithms for conic optimization, Conic optimization and Barrier functions Learning Outcomes At the end of the module the students will be able to conceptualize and synthesize modern optimization techniques suitable for large-scale/big-data problems and be able to apply, and/or modify efficient methods for their own scientific/research problems. Module Code NAML841 Module Name Advanced Machine Learning Module Description The module provides students with advanced machine learning techniques necessary for computational analysis that support various learning algorithms such as those used in robotics, data mining, computer vision, text and web data processing. Module Content Content include Statistical Theory: Maximum likelihood; Bayes, minimax, parametric versus non-parametric methods; Mathematical Underpinning of theories; Utilization of Models; Deep Learning and Comparative analysis Learning Outcomes Module Code NHPC841 Module Name High Performance Computing Module Description This module introduces students to the architecture of several types of high performance computers and their implications on the performance of algorithms on these architectures in order to design and implement efficient algorithms for high-performance computers. Module Content The content include High-performance computer architecture, enhancement of performance on single and multi-processor computers, parallelization overheads; performance evaluation; introduction to parallel algorithms. Learning Outcomes At the end of the module the students will be expected to synthesize and demonstrate theoretical knowledge of the architecture of several types of high performance computers and be able to design and apply efficient algorithms on such architectures. Further students would be able to conceptualize the current state-of-the art in parallel programming environments, portable software libraries and program development. Module Code NDEV842 Module Name Data Exploration and Visualization Module Description The module is to provide students with advanced concepts and roles of data exploration and visualization through use of techniques such as data mining. Module Content Learning Outcomes Module Code NDSC842 Module Name Data Security and Cryptographic Systems Module Description The module introduces students to the theoretical and practical aspects of data security and cryptographic algorithms and protocols. Module Content Content include classical cryptography techniques; mathematical foundations; secret key cryptography; public key cryptography; authentication and digital signature; network cryptographic protocols. Learning Outcomes At the end of the module the student is expected to be able to synthesize theoretical aspects of data security and cryptographic algorithms and protocols and further be able to design and apply techniques, algorithms, architectures and tools used for data security and cryptography in the data science project environments. Module Code NMSP842 Module Name Multidimensional Signal Processing Module Description This module is based introduces students to theory and practical tools used in processing large scale data arising from problems in engineering and computer science. Module Content The content includes processing algorithms suitable for large-scale data tasks involving sparse signals as the Sparse Fourier transform. Other introductory topics in the module are the extension of classical signal processing on data indexed by graphs (discrete signal processing on graphs, DSPG). At the end of each topic, illustrative examples with their respective application scenarios, either PYTHON language or in MATLAB, are provided. Learning Outcomes At the end of the module the students will be expected to synthesize and demonstrate theoretical knowledge in the application of tools and modelling used in processing large scale data arising from problems in engineering and computer science. Module Code NSTD842 Module Name Special Topics in Data Science Module Description Special Topics in Data Science is a unique module based on various emerging technologies of data science. The topics are taught in the last semester of the programme and selected from recent developments and trends in data science or big data technology. The module introduces new or emerging data science or big data technology, and showcase the advanced tool currently used in the industry. Module Content Topics covered in module vary and are based on different fields of data science, some include, Astro-informatics, Advanced Big Data Analytics, Advanced Distributed systems, Statistical Machine Learning, Advance R and Python programming languages, SAS programming environment, Data Mining tools, Internet of Things (IoT), New SQL Database Management Systems, Cloud Computing and Data center Networking, etc. Learning Outcomes At the end of the module the students will be expected to have exposure with current advances of technical industry based tools used in Data science.
An exciting career awaits those who complete a Bachelor of Science (Honours) degree in Data Science. Opportunities with this qualification could lead to a career as a data scientist, data architect, data analyst, business analyst, data/analytics manager, data engineer, intelligence analyst, data mining engineer, or solutions architect.
Please contact the School Registrar Ms Nobulali Mathimba on (053) 491 0369 or postgrad.nas@spu.ac.za
You can download a comprehensive list of all our qualifications that will be offered in 2022 by clicking here.
