Introduction

• Introducing ourselves

• Interaction, discussions, questions

• Examples are from ecology, but data are tabular (i.e. spreadsheets), which applies to many cases. But not all data are initially tabular (HTS, MS-proteomics, microarrays, ... ) but at some point, after processing, they generally become.

• We use sticky notes!

• Etherpad

• An example of bad teaching

Data carpentry

This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data.

By the end of the workshop learners should be able to more effectively manage and analyze data and be able to apply the tools and approaches directly to their ongoing research.

Spreadsheets

• Intro about tidy data, link between semantics and syntax
• Preparation for the task of programming with data, i.e data analysis

1. Data collection
2. Data preparation, cleaning, tidying, QC
3. Analysis: plotting, statistics
4. Interpretation

Q: What is the relative time invested for these steps? What should it be?