All-provider course timetable

« Description not available »

The Cisco Certified Network Associate (CCNA) programme is open to University IT Supporters. It covers network technology, protocols and theory at deeper levels reflective of university practices. There is a fee to attend this course.

You will learn the basics of routing, switching, and advanced technologies to acquire the skills required to provide a robust and secure network in your institution and it prepares you for CCNA certification.

We offer this program as instructor led with remote access to the curriculum and an online networks laboratory called NETLAB. There is a mix of lecture, demonstrations and a heavy emphasis on practical activities using live lab equipment and a simulation package. Further details and pricing information are available.

This is the first module of four modules in CCNA.

1. Networking Fundamentals 2. LAN Switching and Wireless 3. Routing Protocols and Concepts 4. Accessing the WAN

Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is concerned with greater knowledge of regression, through extension of the simple linear model; enabling students to assess the models they use, testing for problems such as collinearity, outliers/leverage, and heteroskdasticity.

« Description not available »

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

« Description not available »

This course is part of the Scientific Computing series.

This course is aimed at those new to programming, or who have never been formally taught the principles and basic concepts of programming. It provides an introduction to the basic concepts common to most high level languages (including Python, Java, Fortran, C, C++, Visual Basic). The aim of the course is to equip attendees with the background knowledge and confidence necessary to tackle many on-line and printed programming tutorials. It may also help attendees in deciding which programming language is suitable for their programming task.

Knowledge of the concepts presented in this course is a pre-requisite for many of the other courses in the Scientific Computing series of courses (although not for the "Python for Absolute Beginners" course).

« Description not available »

This course is part of the Scientific Computing series.

This course is aimed at those new to programming, or who have never been formally taught the principles and basic concepts of programming. It provides an introduction to the basic concepts common to most high level languages (including Python, Java, Fortran, C, C++, Visual Basic). The aim of the course is to equip attendees with the background knowledge and confidence necessary to tackle many on-line and printed programming tutorials. It may also help attendees in deciding which programming language is suitable for their programming task.

Knowledge of the concepts presented in this course is a pre-requisite for many of the other courses in the Scientific Computing series of courses (although not for the "Python for Absolute Beginners" course).

Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is concerned with greater knowledge of regression, through extension of the simple linear model; enabling students to assess the models they use, testing for problems such as collinearity, outliers/leverage, and heteroskdasticity.