Reproducible Research

The course consists of computer labs; each class will be split into theoretical and practical part. The following topics will be discussed (not necessarily in the presented order):

1. Introduction

• Importance of reproducibility in the R&D process

• Consequences of lack of reproducibility

• Best practices

2. Reporting tools

• Introduction to RMarkdown and RBookdown

• Best programming practices

3. Networking & Security

• Introduction to linux shell

• Network security on the example of public/private key pairs

• Connecting to remote computers

4. Version control systems

• Introduction to git

• Worst & best practices with git

• Practical remarks

5. Remote repositories & Utility tools

• Introduction to github

• Basics of project workflow

• Project management tools

6. Cloud environment

• Introduction to Amazon Web Services

• Cloud deployment

7. Summary and outlook

Upon the completion of the course, student:

1. understands the general concept of research reproducibility; knows the reproducibility tools classification; understands which tool can be used in a given context;

2. has basic skills in computer tools allowing to achieve research reproducibility and replicability; has basic skills in modern best programming practices; has basic skills in the cloud development environment; is able to employ skills gained during the course while participating in modern scientific and commercial data science projects;

3. is aware of the importance of reproducibility in data science, as well as in science and development in general; is aware that reproducibility tools are evolving rapidly and that constant training in this area is required to keep skills up to date; is aware of the trends in modern data science and IT development;

K_W01, K_U01, K_U02, K_U03, K_U04, K_U05, KS_01, K_U06