Introduction to Python for Data-analysis

This course will be held in English

Introduction to Python for Data-analysis

Worldwide, Python is the most popular language for data science. Python is free and open source, allowing professionals worldwide to continuously update and add functionality. Although Python is a general programming language at its core, it offers numerous modules specifically designed for data analysis, data science, and machine learning. At the UG, both R and Python are widely used for data science. R is generally preferred for statistics and visualization, while Python is favored for larger programmes and machine learning, including neural networks, deep learning, and large language models.

In this beginner's course, we will guide you through the basics of importing data, cleaning and restructuring data, visualizing and summarizing data, and finally applying statistical models to your data.

Who Should Attend?

This introductory course is designed for anyone who wants to (re)learn the basics of the Python programming language necessary for data handling.

This course is suitable for beginners who are new to Python and those who have some experience with Python but never had the opportunity to grasp the fundamentals.

Prerequisites

None, but an idea of what you would like to do with Python would be nice.

Please note that Python has a steep learning curve. Expect you will need 3-4 hours between sessions to prepare for the next one and plan accordingly. Please be kind to yourself and allow yourself at least 48 hours to complete the course.

What do you learn?

✅ Preparing your Python journey
✅ Getting started with Numpy
✅ Reading and writing data with Pandas
✅ Data transformations with Pandas
✅ Exploratory data analysis with Matplotlib and Seaborn: Graphics
✅ Exploratory data analysis: descriptive statistics
✅ Statistical tests (how to run the tests, not how to interpret them)
✅ What's more (other useful packages and more classical Programming)

Result

At the end of the course, you will not only be able to work with Python, but you will also be able to expand your knowledge for your own specific work field.

If you want to learn more about general programming using Python you can follow the course Introduction to Programming using Python either before or after this course.

The content of this Python for Data-analysis course will be used as a prerequisite for the course Machine Learning Using Python.

Interactive Learning Experience

This course relies heavily on highly interactive (online or hybrid) sessions where we review what you have done in the reader. During a review the teacher will share his screen and go through the code asking you by voting and chatting to find the error or complete the code. These reviews are used to rehearse material, show tips and tricks, warn for common mistakes, explain error messages, show how to use the helpfiles and the programme (IDE) in general, and overall to motivate you to keep up the pace. Participants report they are highly involved during these sessions and the course is consistently highly evaluated. On average this course is rated with an 8.2 (10% gives a 10!) by students, PHD’s and other employees alike.

Date, time and location

Below you will find the dates and times of the upcoming courses. The content of these courses remains the same each time.

If these dates do not suit you and you would like to be notified of future courses, please contact the coordinator, Theo van Mourik (t.j.van.mourik@rug.nl).

September 2025 – 6 week course

All sessions take 4 hours and take 3-4 hours of preparation each.
All presentations will be given via the Kaltura classroom (no account needed) and are fully online.
All presentations will be recorded and recordings will be available for about 6 months after the course.
You will receive a certificate, acknowledging a workload of 48 hours, after attending 5 of 6 sessions or (if you prefer) after completing a final assignment.

This training will also be followed by students from the Data wise minor. During the first session, there will be some additional explanation about assignments and exams. As you do not have to do those, feel free to tune out—or enjoy the moment knowing it does not apply to you. For this minor we also offer a few extra guided working sessions which you are free to join as well. These on-site sessions will take place at the Smitsborg, Zernike, in room 5431.0017 (Zebra).

Session	Date (9 a.m. - 1 p.m.)	Preparation
1	Mon 15 Sept	Install Python and Spyder or start in UWP/VRW
2	Mon 22 Sept	Ch 1 – Ch 2.7 Intro and Basics
3	Mon 29 Sept	Ch 2.8 – Ch 3 Basics and import data
4	Mon 6 Oct	Ch 4 Working with tables
5	Mon 13 Oct	Ch 5 Graphs
6	Mon 20 Oct	Ch 6 – Ch 8 Descriptives, tests, programming

Optional guided working sessions (on-site)

These on-site sessions will take place at the Smitsborg, Zernike, in room 5431.0017 (Zebra).

2nd Week	Thu 25 Sept	13:00 – 17:00
4th Week	Thu 9 Oct	13:00 – 17:00
6th Week	Thu 23 Oct	13:00 – 17:00

March 2026 - 3 week course

All sessions take 4 hours and take 3-4 hours of preparation each.
All presentations will be given via the Kaltura classroom (no account needed) and are fully online.
All presentations will be recorded and recordings will be available for about 6 months after the course.
You will receive a certificate, acknowledging a workload of 48 hours, after attending 5 of 6 sessions or (if you prefer) after completing a final assignment.

Session	Date (from 9 a.m. - 1 p.m.)	Your preparation
1	Tue 24 March	Install Python and Spyder or start in UWP/VRW
2	Fri 27 March	Ch 1 – Ch 2.7 Intro and Basics
3	Tue 31 March	Ch 2.8 – Ch 3 Basics and import data
4	Fri 3 April	Ch 4 Working with tables
5	Tue 7 April	Ch 5 Graphs
6	Fri 10 April	Ch 6 – Ch 8 Descriptives, tests, programming

Enrollment and course fee

Fill out this form to enroll

You can unenroll until 8 days prior to the first session.
Late enrollments are fine, but please also contact the coordinator to avoid the risk of being overlooked.
Participants will receive the course material via email a few days before the course starts.

For more information, you can email Theo van Mourik (t.j.van.mourik@rug.nl).

Prices for individuals

€100 BSc/MSc student at UG, other Dutch University or Hanze Hogeschool
€300 PhD-student at UG or other Dutch University
€650 Employee UG or other Dutch university/UMCG/Hanze Hogeschool
€650 UG Alumni
€1250 Other participants

Prices for groups

20 or more PhD’s €200 pp (minimum price of €4.000)

10 or more employees €400 pp (minimum price of €4.000)

20 or more employees €250 pp (minimum price of €5.000)

5 or more others €1.000 pp (minimum price of €5.000, we will also send a separate contract for this)

When enrolling a group of participants, you need to provide a single financial contact person/cost center number and the mailing addresses of all participants. You can enroll a group using the normal enrollment link.

You can also request a customized course and discuss dates, audience, and content. For more information mail Theo van Mourik (t.j.van.mourik rug.nl). This is also possible within the curriculum.

More information

For more information on this or similar courses, please mail the coordinator, Theo van Mourik (t.j.van.mourik rug.nl).

Last modified:20 October 2025 3.41 p.m.