EVENT : C3BI Training
Main speaker : C3BI Team Autumn session: Date : 19-10-2018 at 09:00 am Location : Retrovirus room – LWOFF (14), Institut Pasteur, ParisWinter session: Date : 11-01-2019 at 09:00 am Location : BFJ 28-01-01A, Institut Pasteur, Paris
This course is addressed to first-year Ph.D. students from the Institut Pasteur: registration is systematic upon joining the institute. Depending on availability, second- and third-year Ph.D. students and postdocs may also apply. First-year PhD students with a background in mathematics or physics will be allowed to ask for an exemption.The course will mix closely theory and practice. It will last four weeks, four days a week with a three-hours lecture per day. We organize two sessions, the first one starting October 19th, 2018 and the second one starting January 11th, 2019. Each session will start by an Introduction to Computer Science to ensure that all students are familiar with essential computer science notions such as computer architecture, file system organization, file format and programming languages. Following the statistics classes, an optional introduction to Image Analysis and Processing will be proposed by the Image Analysis Hub (2 lectures).
- PC – Windows based : Intel i3 / Windows 7 / 4Go RAM / 256 Go HD
- Apple Macintosh : mid-2010 mac book / OSX 10.10 / 4Go RAM / 256 Go HD
- PC – Linux based : Intel i3 / Any distribution (supporting R >= 3.5.1, if possible) / 4Go RAM / 256 Go HD
The form below has to be filled out either to request an exemption or to apply to the course.
- Exemptions will be delivered to students already trained in biostatistics (join a CV and a letter from the supervisor).
- PhD students in 2nd, 3rd years , as well as postdocs working at Pasteur Paris may also apply.
- Presentation of the course – Slides
- Introduction – Computer science 101 – Slides
- Lectures 1 and 2 – First steps with R and RStudio – Online slides (Read the beginning and install R before coming.) – full course archive, including exercise data
- Lecture 3 – Random Variables – Slides – Rcode
- Lecture 4 – Estimation – Slides – Data
- Lecture 5-6 – Confidence intervals & Hypothesis testing – online slides – Data – Exercises
- Practical Session 1 – Exercises – Data – Answers
- Lecture 7 – Correlation and linear regression – Slides – data and Rcode
- Lecture 8 – Multiple linear regression and ANOVA – Slides (same as lecture 7) – data
- Lecture 9 – ROC curves and logistic regression – Slides (same as lecture 7) – data temperature – Correction
- Practical Session 2 – Archive – Correction
- Lecture 10 – Principal Component Analysis – Slides – data – Course_example_PCA PCA_ABC_transporters_correction
- Lecture 11 – Clustering – Slides –Course examples – clustering_ABC_transporters
- Lecture 12
- Practical Session 3 – Introduction Slides
- Image Analysis module – Course material