Graduate School of Life Sciences course timetable
July 2020
| Thu 16 |
The Engaged Researcher Online Training | Opening up: engaging with the public on animal research
Finished
This training is for those whose research involves the use of animals in research, and who want to feel more confident to talk about it with those outside the lab. This training will be run by Understanding Animal Research. |
| Mon 20 |
We’ll be looking at the what, why and how of public engagement and introducing researchers to some of the ways to plan an effective public engagement project. Topics: • The what: definitions of public engagement, who are the public, what activities count as engagement, what are the goals? • The why: University commitment to PE, REF, Funders • The how: the Logic Model approach to planning PE, practical considerations, moving engagement online and opportunities at the University. Course structure: Monday 10am-11am: Introduction to PE Wednesday 10am-11am: Evaluation and online PE tips and hints and opportunities at the University Friday 10am-noon: Do you have any questions? 1:1 advice sessions (optional) |
| Wed 22 |
We’ll be looking at the what, why and how of public engagement and introducing researchers to some of the ways to plan an effective public engagement project. Topics: • The what: definitions of public engagement, who are the public, what activities count as engagement, what are the goals? • The why: University commitment to PE, REF, Funders • The how: the Logic Model approach to planning PE, practical considerations, moving engagement online and opportunities at the University. Course structure: Monday 10am-11am: Introduction to PE Wednesday 10am-11am: Evaluation and online PE tips and hints and opportunities at the University Friday 10am-noon: Do you have any questions? 1:1 advice sessions (optional) |
| Fri 24 |
We’ll be looking at the what, why and how of public engagement and introducing researchers to some of the ways to plan an effective public engagement project. Topics: • The what: definitions of public engagement, who are the public, what activities count as engagement, what are the goals? • The why: University commitment to PE, REF, Funders • The how: the Logic Model approach to planning PE, practical considerations, moving engagement online and opportunities at the University. Course structure: Monday 10am-11am: Introduction to PE Wednesday 10am-11am: Evaluation and online PE tips and hints and opportunities at the University Friday 10am-noon: Do you have any questions? 1:1 advice sessions (optional) |
| Wed 29 |
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. This session will be held online via zoom. Joining instructions will be sent to participants two days before the scheduled focus group. |
September 2020
| Tue 8 |
Have you ever imagined how your research might look and sound on stage? This is your opportunity to explore the world of theatre playwriting together with professionals from Menagerie Theatre Company. One participant will have the exclusive opportunity to get a funded place in a workshop for young writers with the chance to see their writing performed before an audience during 'The Hotbed Theatre Festival'. |
November 2020
| Mon 9 |
Core Statistics
Finished
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:
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
Finished
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:
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
Finished
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:
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
Finished
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:
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 23 |
Core Statistics
Finished
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:
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
Finished
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:
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. |