All-provider course timetable

iProcurement, or iProc for short, is an internet way of ordering goods and services linked to an electronic Marketplace that contains hundreds and thousands of products. iProcurement is a new application accessed via the Cambridge University Finance System (CUFS). This course is also available on-line.

During the face to face standard course you will:

Set up system defaults Create Requisitions via the Marketplace and Non-Catalogue Approve your own requisitions (if within buying limit) Forward your requisitions for approval (if outside buying limit) Query and extract basic information from the system Make amendments If you are attending the buyers course you will also cover the following:

Suppliers Notifications Month End reports Converting requisitions into orders The course typically consists of small segments of trainer input and demonstration followed by exercises for individuals to work through for practice and to consolidate their understanding.

Much of the session comprises a case study based on a medium sized University department. Delegates will examine University regulations and procedures and decide the actions for various scenarios and correct anomalies for selected sources of funds.

During the course you will:

Process Suppliers' Invoices on the system Match invoices to Purchase Orders and deal with any discrepancies Process Suppliers' Credit notes on the system Process Invoices received from another department Query and extract basic information from the system Become aware of the month-end reports available to you

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

During the course you will:

Process Suppliers' Invoices on the system Match invoices to Purchase Orders and deal with any discrepancies Process Suppliers' Credit notes on the system Process Invoices received from another department Query and extract basic information from the system Become aware of the month-end reports available to you

An introduction to the wide range of resources available at the MML Library and the UL, both in print and online.

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.

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

Would you like to play like Beckham, then this is the course for you!

Would you like to be able to dance like they do on 'Strictly'? If so then this is the course for you!

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

Would you like to play like Beckham, then this is the course for you!

Would you like to play like Beckham, then this is the course for you!

Would you like to be able to dance like they do on 'Strictly'? If so then this is the course for you!

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.