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Graduate School of Life Sciences course timetable

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Tue 18 Feb 2020 – Fri 15 May 2020

February 2020

Tue 18	How to write an academic paper and get it published (Life Sciences) Finished 09:30 - 16:30 Postdoc Centre@ Mill Lane, Seminar Room The course takes an evidence-based approach to writing. Participants will learn that publishing is a game and the more they understand the rules of the game the higher their chances of becoming publishing authors. They will learn that writing an academic article and getting it published may help with their careers but it does not make them better researchers, or cleverer than they were before their paper was accepted; it simply means they have played the game well. Suitable for GSLS postgraduates in any discipline who are keen to learn how to write academic papers and articles efficiently as well as more established researchers who have had papers rejected and are not really sure why. If you want a better chance of your name on a paper, this is for you! Trainer Olivia Timbs is an award-winning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals.
Thu 20	Critical Thinking and Bioethics (1 of 2) Finished 09:30 - 13:30 Student Services Centre, Exams Hall, Room AG03d As scientists, skills of critical thinking are well developed in hypothesis testing, observation and scientific projects. This workshop will incorporate other modes of logic and reason into scientific thinking. This workshop will consist of a set of debates on current bioethical issues. We will then analyse and evaluate the presence and impact of critical thinking within those debates PLEASE NOTE: This course consists of two half day sessions, with a week between sessions.
Fri 21	Understanding Open Data CANCELLED 10:00 - 12:00 Student Services Centre, Exams Hall, Room AG03c Conclusions without supporting data are just claims. More and more researchers are sharing their data to improve reproducibility, get more citations and spark collaborations, yet the process can be daunting. We will explore the benefits of sharing data, as well as any concerns you might have, and give you practical tips and tools to ensure that you make the most of the opportunity to open up your data for the world.
Mon 24	Core Statistics (5 of 6) Finished 10:00 - 13:00 Clinical School, E-learning 1, 2, 3 (Level 2) This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
	Core Statistics (6 of 6) Finished 14:00 - 17:00 Clinical School, E-learning 1, 2, 3 (Level 2) This course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Thu 27	Critical Thinking and Bioethics (2 of 2) Finished 09:30 - 13:30 Student Services Centre, Exams Hall, Room AG03c As scientists, skills of critical thinking are well developed in hypothesis testing, observation and scientific projects. This workshop will incorporate other modes of logic and reason into scientific thinking. This workshop will consist of a set of debates on current bioethical issues. We will then analyse and evaluate the presence and impact of critical thinking within those debates PLEASE NOTE: This course consists of two half day sessions, with a week between sessions.

March 2020

Mon 9	Profile-Raising and Networking Finished 10:00 - 16:00 Postdoc Centre@ Mill Lane, Seminar Room This whole day session is designed to help researchers develop strategies for making networking part of a successful career, whether inside or outside of research. It focuses on thinking about all of the researchers' working life as a route to networking, rather than being a course about "personal impact" in conference coffee breaks.
	Core Statistics (1 of 6) Finished 14:00 - 17:00 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 or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Wed 11	Core Statistics (2 of 6) Finished 14:00 - 17:00 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 or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Mon 16	Core Statistics (3 of 6) Finished 10:00 - 13:00 8 Mill Lane, Lecture Room 7 This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Wed 18	Core Statistics (4 of 6) Finished 14:00 - 17:00 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 or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Fri 20	Finishing Your PhD and Looking Forward (Life Sciences) CANCELLED 09:30 - 16:45 Postdoc Centre@ Mill Lane, Eastwood Room This course will take a complete look at the final year of your PhD. From the core elements of the thesis and viva and the often forgotten administrative tasks that must get done, on to looking at who you have become and what career path you may take.
Mon 23	Core Statistics (5 of 6) Finished 14:00 - 17:00 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 or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Wed 25	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 or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.

April 2020

Wed 8	FOCUS GROUP: Is Entrepreneurship and Enterprise at Cambridge Working for you? CANCELLED 12:30 - 13:30 Student Services Centre, Exams Hall, Room AG03b We are running a series of focus groups to gain a better understanding of the entrepreneurship and enterprise landscape at Cambridge for STEMM postgraduates. We welcome everyone to come along and share their experiences and thoughts about this subject with us. Whether you have previously gained entrepreneurship and enterprise experience or thought this is an area to build on as part of your post graduate training, your contribution to these sessions would be most valuable. Lunch will be provided as a thank you for your time and contribution.
Mon 20	FOCUS GROUP: Is Entrepreneurship and Enterprise at Cambridge Working for you? CANCELLED 12:30 - 13:30 Student Services Centre, Meeting Room CG18 We are running a series of focus groups to gain a better understanding of the entrepreneurship and enterprise landscape at Cambridge for STEMM postgraduates. We welcome everyone to come along and share their experiences and thoughts about this subject with us. Whether you have previously gained entrepreneurship and enterprise experience or thought this is an area to build on as part of your post graduate training, your contribution to these sessions would be most valuable. Lunch will be provided as a thank you for your time and contribution.
Tue 28	How to write an academic paper and get it published (Life Sciences) CANCELLED 09:30 - 16:30 Postdoc Centre@ Mill Lane, Seminar Room The course takes an evidence-based approach to writing. Participants will learn that publishing is a game and the more they understand the rules of the game the higher their chances of becoming publishing authors. They will learn that writing an academic article and getting it published may help with their careers but it does not make them better researchers, or cleverer than they were before their paper was accepted; it simply means they have played the game well. Suitable for GSLS postgraduates in any discipline who are keen to learn how to write academic papers and articles efficiently as well as more established researchers who have had papers rejected and are not really sure why. If you want a better chance of your name on a paper, this is for you! Trainer Olivia Timbs is an award-winning editor and journalist with over 30 years' experience gained from working on national newspapers and for a range of specialist health and medical journals.
	Understanding Open Data CANCELLED 10:00 - 12:00 Postdoc Centre@ Mill Lane, Eastwood Room Conclusions without supporting data are just claims. More and more researchers are sharing their data to improve reproducibility, get more citations and spark collaborations, yet the process can be daunting. We will explore the benefits of sharing data, as well as any concerns you might have, and give you practical tips and tools to ensure that you make the most of the opportunity to open up your data for the world.

May 2020

Mon 4	Core Statistics (1 of 6) Finished 14:00 - 17:00 GSLS Online Live Training PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions. This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Wed 6	Core Statistics (2 of 6) Finished 14:00 - 17:00 GSLS Online Live Training PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions. This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Mon 11	Innovation and Enterprise - a commercial perspective CANCELLED 09:30 - 16:30 Postdoc Centre@ Mill Lane, Eastwood Room Provides an understanding of the UK and European landscape for researchers in the context of future careers and collaborations with industry. Also valuable for academics looking for a career move into industry. Provides an insight into what innovation really means and introduces the practical project management tools to implement innovative projects.
	Profile-Raising and Networking CANCELLED 10:00 - 16:00 Postdoc Centre@ Mill Lane, Seminar Room This whole day session is designed to help researchers develop strategies for making networking part of a successful career, whether inside or outside of research. It focuses on thinking about all of the researchers' working life as a route to networking, rather than being a course about "personal impact" in conference coffee breaks.
	Core Statistics (3 of 6) Finished 14:00 - 17:00 GSLS Online Live Training PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions. This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Wed 13	Core Statistics (4 of 6) Finished 14:00 - 17:00 GSLS Online Live Training PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions. This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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: Use R or Python confidently for statistics and data analysis Be able to analyse datasets using standard statistical techniques Know which tests are and are not appropriate Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques.
Fri 15	Problem Solving and Innovation in a Research intensive Environment CANCELLED 10:00 - 16:00 Postdoc Centre@ Mill Lane, Eastwood Room This course has been designed to help graduates students and ECRs to develop their understanding of available tools and techniques which can aid with problem solving and innovation in a research-intensive environment.