Software Packages

The module explores Good Data Visualisation (GDV) and graph creation using Python.

In this module we demystify the principles of data visualisation, using Python software, to help researchers to better understand and reflect how the “5 Principles” of GDV can be achieved. We also examine how we can develop Python’s application in data visualisation beyond analysis. Students will have the opportunity to apply GDV knowledge and skills to data using Python in an online Zoom, self-paced, practical workshop. In addition there will be post-class exercises and a 1-hour asynchronous Q&A forum on Moodle Forum.

Prerequisites

No previous knowledge or experience required although some programming experience will be an advantage.

Objectives

To explore:

• Why data visualisation is important to trans-disciplinary research(ers); basic concepts/theories on data visualisation and primary challenges in data visualisation so important in today’s big data age

• What can contribute to Good Data Visualisation (GDV), what the “5 Principles” for GDV are and why they are crucial (illustrated with examples)

• How Python can be used for data visualisation to improve data analysis

• How to apply Python to create basic graphs (single line graphs, multiple curves, scattergraphs, etc.) in accordance with

Aims

By the end of this module the trainees will be able to:

• Critically appreciate the ‘5 principles’ of GDV, and be able apply these to specific research contexts

• Understand the features and (dis)advantages of mainstream data visualisation tools (e.g. Power BI) and how to seek out the most appropriate context(s) for their use, especially Python’s potential in data visualisation and graph creation

• Plot simple graphs in Python in accordance with the “5 Principles” of good data visualization

Format

The module requires student to attend two 2-hour sessions that consist of a 2-hour presentation and self-paced preparation and post-class exercises and there will also be a 1 hour asynchronous Q&A session on Moodle Forum.

Session 1: consists of an in-person presentation and discussion, with an introduction to key concepts and problems in data visualization, with examples from case studies. There will also be an opportunity for group discussion on the principles of data visualisation and aspects to consider to successfully communicate information using visual methods.

Session 2: consists of an online Zoom workshop where students have the opportunity to apply Python to visualise data and construct different types of graphs.

System requirements

Students will be expected to have access to their own laptop or PC and ensure a copy of Python software is uploaded before the start of session 2.

Sessions

Michaelmas Term

Lent Term

How to Book

Bookings can be made via the Modules List where you will also find the module dates. Click on the module you want and you will be taken to a booking screen. As soon as you book, you will receive an automated email confirming your place. Please note, some modules (though not all) have multiple iterations in the Michaelmas and Lent Term.

The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging.

Learning objectives:

Overarching goal: Learn how to prepare messy datasets and clean data ready for analysis using R software

· How to process datasets in batch

· How to match, link and merge datasets (combine columns; combine rows)

· Techniques to deal with unwanted cases, including cases that do not meet the inclusion criteria, duplicates, and outliers, etc.

· How to deal with missing data

· How to recode and mutate variables

· How to revise original data files without messing up the format

· Other topics that you are interested in after we finish the above ones

Prerequisites

Some experience using R software. If you have not used R before then it is recommended that you first take the ‘Introduction to R’ module.

Sessions

How to Book

Bookings can be made via the Modules List where you will also find the module dates. Click on the module you want and you will be taken to a booking screen. As soon as you book, you will receive an automated email confirming your place. Please note, some modules (though not all) have multiple iterations in the Michaelmas and Lent Term.

This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled.

The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.)

MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here

More information on the course can be found here

Prerequisites

• No prior knowledge is needed.
• Students will need to bring their own laptop with the latest version of Matlab (see www.mathworks.com) pre-installed. Cambridge students can download and install Matlab from mathworks.com for free with their CRSid. Select and install all packages available to you.
• Before class, please skim through the excellent free resources, Getting Started and Language Fundamentals: http://uk.mathworks.com/help/matlab/index.html

Format

Lectures and labs

System requirements

MatLab installed on student's own laptop.

Sessions

How to Book

Bookings can be made via the Modules List where you will also find the module dates. Click on the module you want and you will be taken to a booking screen. As soon as you book, you will receive an automated email confirming your place. Please note, some modules (though not all) have multiple iterations in the Michaelmas and Lent Term.