PhD in Biological Sciences and Applied Biotechnologies

After registration you will be contacted by the teachers with more details on the course location and timetable.

This page will be updated with new contents

 

 

January 29-31, 2018

9:00 - 18:00

Unix, R and reproducible research

Instructors

Stefano Ghignone - Marco Chiapello

Reproducible research

UNIX

Introduction to R and R data.frames

Aggregating and analyzing data with dplyr

Data visualization with ggplot2

Reproducible research with R

Optional
Version control

Updated details and materials for the Basic module can be found here:https://phd-toolbox-course.github.io/2018_PhD_Toolbox_course/

 

Advanced Module - 5 Credits

Advanced Module I
Guglielmo Lione

13/03/2018, Aula A, h.14-18

Fundamentals of calculus and probability
Dealing with uncertainty in scientific papers
Probability mass and density functions
Understanding P-value and hypothesis tests

Advanced Module II
Guglielmo Lione

14/03/2018, Aula 4, h.9-13

Monte Carlo methods
Simulations and in silico experiments
Resampling methods overview
Permutation and randomization tests
Bootstrap

Advanced Module III
Marco Gamba

20/03/2018, Aula 4, h.14-18

Multivariate ordination techniques

Advanced Module IV-V
Marco Gamba

21/03/2018, Aula 4, h.14-18
22/03/2018, Aula 4, h.14-18
26/03/2018, Aula 4, h.14-18

Advanced unsupervised learning and class prediction with R

Tricks of the trade - 2 credits
Dan Chamberlain - Enrico Caprio

09/05/2018, Aula A, h.14-17

Short description: The first module aims to help students improve the impact of their research, through a better knowledge of the publication process including bibliometrics (H index, impact factor etc.), and how to better frame your paper to get your message across. The second module will focus on web tools for researchers and offer guidelines to enhance the dissemination of your research through social media.

Introduction to GLMs
Dan Chamberlain - Enrico Caprio

16/05/2018, Aula 4, h.14-18
17/05/2018, Aula 4, h.14-18

Short description: The course will provide an introduction to Generalized Linear Modelling in R, which will comprise data exploration, linear regression and extension to more complex linear models, poisson models and we will finish with class exercises.