DATA ANALYTICS REFERENCE DOCUMENT
|Document Title:||Modules for September 2019|
|Author(s):||Gerhard van der Linde|
Details of Modification(s)
Reason for modification
|0||Draft release||Document description here||2019/06/20 14:13||Gerhard van der Linde|
A practical look at the most popular algorithms used in machine learning and the analysis of stochastic processes.
Students cover topics such as incorporating neural networks, support vector machines and large-scale machine learning in their own data analytics workflows.
In this module students will investigate and operate the protocols, standards and architectures used in representing and querying the data that exists across the internet. Students will also gain practical experience in developing applications that interact with such data.
On completion of this module the learner will be able to: explain the basic mechanisms by which data is represented and transmitted; compare the different data models and architectures used in modern web (and offline) applications; design and utilise application programming interfaces in the context of the web and other hosting platforms; write data-centric software applications that adhere to defacto standards and protocols.
This module provides an introduction to programming (using an Object-Oriented approach) and assumes little or no previous experience in programming.
On completion of this module the learner will be able to: demonstrate an understanding of the core concepts of object-oriented programming; implement a software application using an object-oriented programming language utilising core object-oriented programming concepts, and develop problem solving skills as part of this process; design an object-oriented software application; test and debug an object-oriented software application; demonstrate an understanding of the universality of the Object-Oriented paradigm and its applicability to programming for data analytics-centric contexts.