Introduction to Statistics

Aims

Statistics is fundamental for arriving at scientific conclusions and data-driven decision making. This course offers a hands-on introduction to statistical analyses covering foundational statistical concepts and techniques. The aim of the course is to equip participants with the practical skills to implement analyses in R and the statistical understanding to interpret the results.

Completing the course will enable participants to:

· Explore and describe data
· Code and run analyses in R
· Understand and implement basic statistical models
· Interpret results from statistical analyses

The focus of the course is on practical applications and conceptual understanding, emphasised through simulation-based and interactive learning material.

The course is ideal for doctoral candidates and applied researchers in quantitative disciplines who wish to implement and understand their own elementary statistical analyses.

Content

· How to code with R to handle data and run analyses
· Descriptive statistics and how to compute and visualise them
· Elementary inferential statistics: sampling distributions, confidence intervals, p-values and hypothesis testing
· Basics of statistical modelling to describe relationships between variables

Methods

The course combines self-study workbooks to introduce R programming, instructor-led interactive sessions for introducing concepts, hands-on exercises for experience in practical application and home assignments to reinforce skills.

Target Group

All Doctoral Candidates

Requirements

The course will use R within Rstudio with interactive slides using Rshiny. Working on practical exercises and assignments will require a computer with R and Rstudio installed, and participants should bring a laptop with these running. The pre-course material covers R programming and needs to be completed before the in-person sessions, which will also include installing relevant R packages.

About the Trainer

The instructor for this workshop is Dr. Jack Kuipers, a lecturer and senior scientist at the D-BSSE of the ETH Zurich. His research is in the areas of computational oncology and computational statistics, with an emphasis on probabilistic graphical models for complex high-dimensional data, and their application to tumour evolution. He holds a PhD from the School of Mathematics at the University of Bristol and has extensive experience in method development and data analysis, and in teaching introductory statistics at the D-BSSE and for the Swiss Institute of Bioinformatics.

Workload

Total of 30 hours

The pre-course material covering R programming will require 8 hours of self-study. The course over 2 days consists of 16 hours of learning. Along with practical work during these days, additional assignments each week taking 3 hours are to be completed as part of the course.

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.