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Graduate School of Life Sciences course timetable

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Mon 22 Jul – Tue 24 Sep

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July 2019

Tue 23
Core Statistics (5 of 6) In progress 10:00 - 13:00 eLearning 2&3 - School of Clinical Medicine

This course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

Thu 25
Core Statistics (6 of 6) In progress 10:00 - 13:00 eLearning 2&3 - School of Clinical Medicine

This course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

  1. Use R confidently for statistics and data analysis
  2. Be able to analyse datasets using standard statistical techniques
  3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.

September 2019

Mon 23
Managing Your Final Year and Preparing to Move On new (1 of 2) [Full] 09:30 - 17:00 Postdoc Centre@ Mill Lane, Eastwood Room

Your final year is an exciting, yet unsettling time. You need to finish experiments, start to write your thesis and begin to think about the next chapter of your career. This two-day linked workshop is designed to help you make sense of the year ahead.

You will be given practical tips on planning your final year, as well as discuss the administration of your final year, writing your thesis and preparation for your viva. In addition, you will explore the career opportunities that are best suited to you, by thinking about your expertise, suitability and personal values. Finally, you will get the chance to review your C.V and experience the interview process.

Tue 24
Managing Your Final Year and Preparing to Move On new (2 of 2) [Full] 09:30 - 17:00 Postdoc Centre@ Mill Lane, Eastwood Room

Your final year is an exciting, yet unsettling time. You need to finish experiments, start to write your thesis and begin to think about the next chapter of your career. This two-day linked workshop is designed to help you make sense of the year ahead.

You will be given practical tips on planning your final year, as well as discuss the administration of your final year, writing your thesis and preparation for your viva. In addition, you will explore the career opportunities that are best suited to you, by thinking about your expertise, suitability and personal values. Finally, you will get the chance to review your C.V and experience the interview process.