Data Carpentry workshops teach the fundamental data skills needed to conduct research. The target audience are researchers who have little to no prior computational experience. The workshops build on learners’ existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. The workshop starts with a lesson on organizing data effectively in spreadsheets, followed by a lesson on data cleaning with OpenRefine. The largest part of the workshop consists of a lesson in Python, focusing on data import, exploratory data analysis, and visualization.

This workshop uses a tabular interview dataset from the SAFI Teaching Database and teaches data cleaning, management, analysis and visualization. We use a single dataset throughout the workshop to model the data management and analysis workflow that a researcher would use.

The workshop is open and free to all researchers in the Netherlands. The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the workshop website.

Who: The course is aimed at graduate students and other researchers affiliated with Dutch research institutions.

Prerequired knowledge: There are no pre-requisites, and the materials assume no prior knowledge about the tools.

Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

Syllabus

Data Organization in Spreadsheets (day 1)

• Formatting Tables in Spreadsheets
• Formatting Problems
• Quality Assurance
• Exporting Data

Data Cleaning with OpenRefine (day 1)

• Working with OpenRefine
• Filtering and Sorting
• Examining Numeric Data
• Generating Scripts
• Exporting Data

Introduction to Python (day 2, 3, 4)

• Introduction to Python
• Python basics
• Processing data from a file
• Reading data from a file using Pandas
• Data visualisation using Matplotlib