京都大学 京都大学

Data Analysis Practice I-E2

Numbering Code U-LAS11 20005 SE55 Year/Term 2022 ・ First semester
Number of Credits 2 Course Type Seminar
Target Year All students Target Student For all majors
Language English Day/Period Wed.4
Instructor name Martin Robert (Graduate School of Pharmaceutical Sciences Program-Specific Associate Professor)
Outline and Purpose of the Course The world around us, is filled with numbers (data) that range over many scales of space and time and that describe its organization. In biology, traditionally, data feature parts lists and partial views of the connections between those parts. However, there is also a vast amount of quantitative (numerical data) that is accumulating, whether from sequences of DNA, concentrations of various biomolecules, or other types of data.

The ability to handle, process, explore, and visualize data are important skills for all students. While in this course many examples will be derived from biology, the mindset and basic analysis workflows are widely applicable in any domain of science, engineering and beyond.

In this course you will learn how to use R, RStudio, and the Tidyverse packages to clean, process, manipulate, explore, and visualize data. This course is for beginners and there is no specific requirement.
Course Goals By the end to this course participants should be able to:
- Perform basic data processing and analysis using R
- Find and describe different forms of (biological) data
- Elaborate specific questions about the data
- Clean and process raw data
- Transform data
- Draw various types of plots to interpret from its results
- Gain insight into data
- Develop analysis workflows
- Effectively communicate the results of data analysis
Schedule and Contents Week 1 Guidance and introduction
Week 2 What is data? Getting started with R
Week 3 Workflow demonstration
Week 4-5 Importing and cleaning up data
Week 6-7 Data transformation
Week 8 Data visualization
Week 9 Digging deeper into R using dplyr
Week 10 Dealing with specific data (strings, dates, etc.)
Week 11 Getting to grips with ggplot - producing publication-quality figures
Week 12 Working with single variables
Week 13 Exploring relationships among variables
Week 14 Looking and back and looking forward
Week 16 Feedback
Evaluation Methods and Policy 20 % Class attendance/ participation
60 % In-class exercises and homework assignments
20 % Project and presentation
Course Requirements Students should bring a computer to class to complete in-class exercises and tutorials as well as homework assignments.
Study outside of Class (preparation and review) Mainly in the form of assigned reading and homework assignments. Students should expect to spend about 1-2 hours per week preparing for the class and completing assignments.
Textbooks Textbooks/References Insights from Data with R : An Introduction for the Life and Environmental Sciences, Owen L. Petchey, Andrew P. Beckerman, Natalie Cooper, and Dylan Z. Childs, (Oxford University Press USA, 2021)
References, etc. R for data science, Wickham and Grolemund, (O'Reilly Media, 2017)
PAGE TOP