| Code | Faculty |
|---|---|
| 12254014 | Fakulteit Ingenieurswese, Bou-omgewing en Inligtingtegnologie |
| Credits | Duration |
|---|---|
| Duur van studie: 2 jaar | Totale krediete: 180 |
Hierdie graadprogram word slegs in Engels aangebied.
Verwys na G Regulasies G.30 tot G.54
Die leergang word in oorleg met die programorganiseerder bepaal.
’n Student sal spesiaal aansoek moet doen by die Dekaan van die Fakulteit Ingenieurswese, Bou-omgewing en Inligting-tegnologie indien hy/sy langer as drie jaar benodig om die graad te voltooi.
Bykomende toelatingsvereistes vir Stroom C: Big Data Science
'n Mimimum semesterpunt van 40% word vereis om toegelaat te word tot die finale eksamen in al die voorgeskrewe modules van die graad. 'n Finale punt van 50% word vereis om alle gedoseerde modules en die miniverhandeling te slaag.
Staking van studies
Die Dekaan mag, op aanbeveling van die toelatingskomitee, die studies van 'n student wat meer as een module druip, kanselleer. 'n Module mag slegs een maal herhaal word.
Deregistrasie van modules
Deregistrasie van modules in Stroom C word slegs toegelaat voor die vroeë afsnypunt.
Toekenning van die graad
Die magistergraad in Inligtingtegnologie Stroom A en Stroom B word toegeken aan ‘n student wat die volgende suksesvol voltooi:
Die magistergraad in Inligtingtegnologie Stroom C word toegeken aan ‘n student wat die volgende suksesvol voltooi:
Die graad word met lof toegeken indien die minimum punt behaal vir die miniverhandeling 75% is en die minimum geweegde gemiddelde punt vir die gedoseerde modules, 75% is.
Minimum krediete: 74
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Students will obtain hands-on experience on the following technologies such as: Python, Spark, Hadoop, R and SAS, Streaming, Data fusion, Distributed file systems; and Data sources such as social media and sensor data.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
This is the first and introductory module for the MIT degree in Big Data Science. Big Data and Data Science will be defined and students will be exposed to different application domains within the participating academic departments in the MIT degree. These departments include: Computer Science, Electrical, Electronic and Computer Engineering (EECE), Informatics, Information Science, Mathematics and Applied Mathematics, Statistics, and Health Science departments. The presentation of this module will be in the format of a two-day workshop.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
In this module students will be introduced to Mathematical Optimization through gaining knowledge about the theory and algorithms to solve optimisation problems. Topics will include: Linear programming, unconstrained optimization, equality constrained optimization, general linearly and nonlinearly constrained optimization, quadratic programming, global optimization, Theory and algorithms to solve these problems.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
The focus in this module is on Information Ethics and its place within the disciplines of Ethics and Philosophy. The following topics will be covered: Information Ethics and PAPAS (privacy, accuracy, property, access, security); Information ethics and the life cycle of big data; Information ethical dilemmas within big data in different disciplines, e.g. science, technology, engineering and mathematics (STEM), health sciences, economics and management sciences, social sciences and the humanities; and Case studies.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Research methodologies applicable to the IT field as preparation for the mini-dissertation for the A Stream students.
Module-inhoud:
Basiese navorsingsmetodologie as voorbereiding vir die miniverhandeling vir die B Stroom-studente.
Basic research methodology as preparation for the mini-dissertation for the B Stream students.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
In this module students will be exposed to different categories of machine and statistical learning algorithms that can be used to manipulate big data, identify trends from the data, modelling trends for prediction purposes as well as modelling for the detection of hidden knowledge. Students will be exposed to various machine and statistical learning algorithms/methods and they will learn how to make the right choice with regard to these. Learning, in a supervised and unsupervised mode will be covered. Furthermore students will develop a practical understanding of methods that can aid the learning process, such as, new developments in regression and classification, probabilistic graphical models, numerical Bayesian and Monte Carlo methods, neural networks, decision trees, deep learning and other computational methods. This module also includes a visualisation component focusing on the encoding of information, such as patterns, into visual objects.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
This module focuses on tools for Big Data processing. The focus is on the 3 V- characteristics of Big Data namely volume, velocity and variety. Students will learn about the different architectures available for Big Data processing. The map-reduce algorithm will be studied in detail as well as graphical models for Big Data. The module will include a significant component of practical work (hands-on) where students will be exposed to real use cases that are or can be implemented on Big Data platforms.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Big data management is the governance, administration and organization of large volumes of both structured and unstructured data. Aspects included in big data management are: big data as organizational asset, harnessing big data as disruptive technology for competitive advantage, big data quality and accessibility; management strategies for large and fast-growing internal and external data, big data infrastructure and platform management, and big data policy, strategy and compliance.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Similar to MIT 862; which has the following description: Research methodologies applicable to the IT field as preparation for the mini-dissertation for the A Stream students.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Example courses, amongst others, may include: Cyber-security, Digital Forensics, Deep Machine Learning, Image and sound analysis, Feature extraction, and Graph Modelling. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Example courses may include: Intelligent systems and Internet of Things. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Example courses, amongst others, may include: Cyber-security, Digital Forensics, Deep Machine Learning, Image and sound analysis, Feature extraction, and Graph Modelling. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
See existing electives from MIT modules in Stream A and B. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Five 5 credits of an elective course can be drawn from the Department of Statistics. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Five 5 credits of an elective course can be drawn from the Department of Statistics. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Example courses may include: Intelligent systems and Internet of Things. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Five credits of an elective course can be drawn from Information Science. A course in Research Data Management (RDM) is available as an elective course. The following topics would typically be covered: Open Science and the dependency on open (big) data, The research process and the life cycle of big data (data management plans to publishing derivative data sets, licensing and legal implications), Managing (curating) big vs long tail data; Solving problems with research data vs the business value of big data (data-intensive decision making); Managing data as an asset (also data citation); Issues and challenges involved in the management of big data (principles and best practices for effective big data governance); Trusted data repositories; Data stewardship frameworks for big data; and The data steward's tool box.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Five 5 credits of an elective course can be drawn from Mathematics and Applied Mathematics. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Five 5 credits of an elective course can be drawn from Mathematics and Applied Mathematics. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
See existing electives from MIT modules in Stream A and B. In addition to study-leader approval, elective course selection may be subject to course pre-requisites, course availability, and internal departmental regulations as decided by the Head of the Department.
Minimum krediete: 106
Module-inhoud:
Let wel: Slegs vir die Departement Inligtingkunde-studente.
Please note: Only for the department of Information Science students.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
This module provides the opportunity to students for demonstrating the application of the theoretical Big Data Science knowledge gained in the core part of this degree. Students are expected to identify and work with a collaborator who is taking ownership for the project. This collaborator can either be an industry partner or a researcher within one of the participating departments. Projects will be based on the entire big data lifecycle as discussed in this degree programme. This includes the gathering of data of a significant size as well as a final technical report describing the process followed and the deliverables. Depending on the complexity of the project, students can apply to work in groups with a maximum of two members. The proposed project will be subject to approval by the Department Computer Science.
Module-inhoud:
Let wel: Alle A Stroom-studente registreer eers vir MIT 840. Sodra 'n studieleier aangewys is vir 'n student, sal die student oorgeplaas word na die ooreenstemmende module om die korrekte departement aan te dui. Informatika-studente sal vir MIT 840 geregistreered bly.
Module-inhoud:
*Hierdie inligting is slegs in Engels beskikbaar.
Students may choose a supervisor/co-supervisor from any of the participating departments, which includes, but are not limited to: Electrical, Electronic & Computer Engineering (EECE), Informatics, Information Science, Mathematics and Applied Mathematics, and Faculty of Health Science departments (Computational biology, Family Medicine, Radiology). Additionally to the last mentioned, a supervisor/co-supervisor will also be allocated to all students from a department in the School of Information Technology. It is expected that a submission to a relevant journal is made during the course of the study. All the other faculty and university regulations for a master’s degree will also be applicable over and above those listed at the beginning of this paragraph.
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