Course Overview

The ability to extract information from data is increasingly important in all areas of research and is an essential part of evidence based decision making in the world of big data. The aim of the course is provide an interactive experience for researchers from a variety of disciplines to enable them to use data science and statistical techniques to answer questions in their own research.

The course will include both traditional style presentations and ‘hands-on’ computer practical sessions in which participants will be guided through the analysis of a series of case studies from a number of different disciplines. These practical sessions will involve training in R, a free software environment for statistical computing and graphics. During the last decade, the momentum coming from both academia and industry has lifted R to become the single most important tool for statistics, visualisation and data science. Participates will learn how to get their data into R, get it into the most useful structure, transform it, visualise it and model it.

The course will be delivered by a team of statisticians and data scientists from University of Bath. The presenters are Gavin Shaddick, Daniel Simpson, Matthew Thomas, Aoibheann Brady, Robbie Peck and Adwaye Rambojun.

Monday

11:00 - 12:00 - Introductory session. An introduction to the aims and content of the course and a chance to meet the presenters. Pre-course preparation including installation to the open source statistical package R and distribution of course material and datasets.

Tuesday

Theme: Using data and statistics in your research

09:30 - 10:15 - Session 1.1 (L): Using data and statistics in your research
10:15 - 10:45 - Session 1.2 (L): An introduction to R
10:45 - 11:00 - Coffee
11:00 - 12:00 - Session 1.3 (P): Using R with your data
12:00 - 13:00 - Lunch
13:00 - 13:45 - Session 1.4 (L): Data and variables
13:45 - 14:45 - Session 1.5 (P): Initial data analysis
14:45 - 15:15 - Coffee
15:15 - 16:00 - Session 1.6 (L): Visualising data
16:00 - 17:00 - Session 1.7 (P): Is your data ready to analyse?

Wednesday

Theme: Developing and testing your hypotheses

09:30 - 10:00 - Session 2.1 (L): Discussion and recap from previous day
10:00 - 10:45 - Session 2.2 (L): Hypothesis testing I
10:45 - 11:00 - Coffee
11:00 - 12:00 - Session 2.3 (P): How significant are your results?
12:00 - 13:00 - Lunch
13:00 - 13:45 - Session 2.4 (L): Hypothesis testing II
13:45 - 14:45 - Session 2.5 (P): Choosing the right tests for your data
14:45 - 15:15 - Coffee
15:15 - 16:00 - Session 2.6 (L): Non-parametric statistics
16:00 - 17:00 - Session 2.7 (P): When and how to use non-parametric tests

Thursday

Theme: Embedding statistics in your research papers

09:30 - 10:00 - Session 3.1 (L): Discussion and recap from previous day
10:00 - 10:45 - Session 3.2 (L): Linear regression
10:45 - 11:00 - Coffee
11:00 - 12:00 - Session 3.3 (P): Fitting a model to your data
12:00 - 13:00 - Lunch
13:00 - 13:45 - Session 3.4 (L): Extending the linear model
13:45 - 14:45 - Session 3.5 (P): Fitting more complex models to your data
14:45 - 15:15 - Coffee
15:15 - 16:00 - Session 3.5 (L): Discussion and wrap up of the course

(L) - Lecture, (P) - Practical

Data Science and Statistics in Research: unlocking the power of your data

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