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Course description

This three-week E-learning gives practical insights on Data Management for scientists. Basic knowledge of relational databases, entity-relationships models, relational models and SQL with MySQL is provided during the course. The programming language used to process data from and to the database is Python.

Proper management of research data is a requirement by funding agencies, publishers or academic institutions. This course provides the technical keys to understand how to model, structure and query data. Benefits of having these skills are numerous: a better insight on how to manage research data and comply with research data management policies, more efficiently store and re-use important data for computational experiments and awareness of the current techniques available to make these tasks easier. The modeling part of the course is focused on communicating the important aspects of datasets to colleagues or an audience via simple models that can be included in posters or other types of publications.  

Learning objectives

The course is divided into six modules:

• Research Data Management and Databases
• Data and Models
• Starting with MySQL and Workbench
• Structuring and Querying Data
• Storing and Processing Data with Python
• Working with data repositories

Next, more practical insights are given, mainly about:

• Data modeling with E-R and relational schemas
• SQL (mainly DML)
• Working with MySQL and Workbench (modeling)
• Working with publicly available data by modeling
• Importing and integrating data into relational databases
• Working with data schemas and public repositories

Instructional method

You will get access to the online learning platform on which all teaching material is available. Here the chat option can be used to contact the trainer. During the three weeks of the course there will be a set of tests, leading to a final grade:

• After week 1: Online quizzes (10%), three attempts per quiz. Min. score is 6 per quiz.
• After week 2: Two minor assignments (20%). No minimum score. There is one opportunity to resubmit one of the two minor assignments to improve its grade
• After week 3: A final assignment (70%). Min. score is 5. There is one opportunity to resubmit the final assignment if the grade is less than 6. It is mandatory to attend the final assignment.

Trainer

You will be trained by one of the professionals of Utrecht ľ¹Ï¸£ÀûÓ°ÊÓ.

Group size            

25 to 35 participants

Number of credits                                 

3.0 EC

Course schedule

Starting November 7, 2022, every week one new session will be online for you to follow, up until November 25, 2022.
You are free to decide what time in the week you will complete the learning unit.

Study load

The total study load is approximately 84 hours (depending on previous experience).

Course certificate                                

You will receive a course certificate after completing all units and being an active participant on the online platform.

Cancellation and No-show policy

This course is free for GSLS PhD candidates. However: free of charge does not mean free of responsibility. Once you have signed up for a course, we expect you to attend. For every late cancellation or no-show we have had to disappoint others who would have liked to attend. This is our policy:

• You may cancel free of charge up to 4 weeks before the start of the course. After this date you can only cancel if you have a GSLS PhD candidate to replace you in the course. Send the name and contact information of your replacement to pcc@uu.nl, at least 2 working days before the start of the course;

• We expect that you actively attend the full course. You have to complete the total E-learning.

• Not meeting the above requirements means you will be charged a no-show fee (€ 74). We will send the invoice after the course has ended. We are unable to make any exceptions, unless you have a valid reason (i.e., illness or death in the family 1^st/2^nd degree or partner). Your supervisor has to send an e-mail to pcc@uu.nl indicating the reason.

Unfortunately we don’t offer this course for participants not part of the GSLS. Our courses tend to be fully booked by GSLS PhD candidates.