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Graduate School of Life Sciences course timetable

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Tue 18 Jun – Mon 18 Nov

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July 2019

Thu 4
The Engaged Researcher: Gentle Introduction to Impact Evaluation new Finished 09:30 - 12:30 17 Mill Lane, Seminar Room B

This workshop introduces how to design an effective impact evaluation.

The Engaged Researcher: Questionnaire Design for Impact Evaluation new Finished 14:00 - 17:00 17 Mill Lane, Seminar Room B

This workshop provides top tips and guidance on developing an impact evaluation survey that is robust. This will include helping participants identify and avoid common pitfalls in impact evaluation questionnaire design, as well as accounting for key issues such as representative sampling. Participants will also have the opportunity to develop their own survey questions with feedback and support during the workshop.

Tue 9
The Art of Negotiation and Influence (GSLS) Finished 09:00 - 17:00 17 Mill Lane, Seminar Room B

A one day master class in communication from an external trainer who has previously been employed as a hostage negotiator and detective in the Metropolitan Police Force. Participants will gain a practical insight into how professional communicators communicate, and how it can be applied in everyday life.

At the end of the session participants will:

  • Know how to persuade and influence effectively
  • Understand how to have greater impact when communicating
  • Have practiced the fundamental tools of professional communicators

Topics:

  • Levels of communication
  • Trust
  • Stages of active listening
  • Non-judgmental language
  • Achieving win/win
  • Building rapport
  • Do's and don'ts
Core Statistics (1 of 6) Finished 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 11
Core Statistics (2 of 6) Finished 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.

Tue 16
Core Statistics (3 of 6) Finished 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 18
Core Statistics (4 of 6) Finished 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.

Tue 23
Core Statistics (5 of 6) Finished 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) Finished 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) Finished 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) Finished 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.

October 2019

Mon 14
The Engaged Researcher: Introduction to Social Media Engagement new Finished 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

This course will cover how to use Social Media tools for Public Engagement. The course will be delivered by the Social Media and AV team.

Tue 15
Core Statistics (1 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only 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 linear models and power analyses. 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 17
Core Statistics (2 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 5

This laptop only 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 linear models and power analyses. 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.

Tue 22
Core Statistics (3 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only 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 linear models and power analyses. 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 24
Core Statistics (4 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 5

This laptop only 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 linear models and power analyses. 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.

Mon 28
The Engaged Researcher: Finding Your Research Story new Finished 09:30 - 13:30 Postdoc Centre@ Mill Lane, Eastwood Room

We all love a good story- whether it’s the latest bestselling fiction book or a cheesy soap. And science is full of stories- stories of discovery, of persistence, of hope. Finding these stories can help take your public engagement to the next level, whatever medium you use to communicate.

Tue 29
Core Statistics (5 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 10

This laptop only 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 linear models and power analyses. 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 31

This short course covers the what, why and how of public engagement and communication. The course is for research staff and PhD students who want to gain the skills and confidence required to plan and deliver an impactful public engagement project.

Core Statistics (6 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 5

This laptop only 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 linear models and power analyses. 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.

November 2019

Fri 1
How to Keep a Lab Notebook Finished 14:00 - 16:00 Department of Genetics, Biffen Lecture, Downing Site

Your lab notebook is one of the most important and precious objects you, as a scientist, will ever have. This course will explore how keeping an exemplary laboratory notebook is crucial to good scientific practice in lab research. The course will consist of a short talk, a chance to assess some examples of good and bad practice, with plenty of time for questions and discussion. You might like to bring along your own lab notebook for feedback. (Please note that issues relating to protection of Intellectual Property Rights will not be covered in this course).

Wed 6
The Engaged Researcher: Telling Your Research Story new Finished 09:30 - 13:00 Postdoc Centre@ Mill Lane, Seminar Room

Whether at a conference, a science festival or in the pub, all scientists need to be able to talk about their work in an engaging and understandable way. This practical, hands-on session will help scientists develop their communication skills, so they are confident talking to diverse audiences in a range of environments.

Fri 8
The Engaged Researcher: Media training new Finished 10:00 - 13:00 Postdoc Centre@ Mill Lane, Eastwood Room

This course gives an introduction into how to engage with the public through media. It will cover the differing types of media, what makes research newsworthy, how to work with the communications office to gain media coverage, what to expect from an interview (print, pre-recorded, live) and how to communicate well in interviews. It will be delivered jointly with the University Communications team

Wed 13
The Engaged Researcher: Pathways to Impact and Public Engagement new Finished 12:00 - 14:00 17 Mill Lane, Seminar Room G

What is in Impact? This course is going to disentangle academic and non-academic impact. It will explore the current research environment and impact agenda and help you understand how research is funded. You will get to discuss your research in small groups, and think about the types of impact it could generate. You will also understand where Public Engagement sits in the wider Impact agenda. You will have the opportunity to analyse impact cases studies that featured Public Engagement as a pathway to Impact. (Lunch will be provided)

Mon 18
The Engaged Researcher: Shooting your research video new Finished 09:30 - 16:30 Postdoc Centre@ Mill Lane, Eastwood Room

Why is YouTube popular? Because people love watching videos. A video is a great way to spread the message of your research to different public audiences across the World! Attendees will be equipped with the skills needed to plan and shoot high quality footage for your very own research-video.

It is strongly recommended that you also attend The Engaged Researcher: Editing Your Research Video session.

(Lunch will be provided)