Course Introduction to Python for Data Science

The course Introduction to Python for Data Science is offered by the Graduate School of Natural Sciences/GSNS and Graduate School of Technical Sciences/GSTS, Aarhus University, fall 2025.

Name of course:
Introduction to Python for Data Science

ECTS credits:
1.0 ECTS

Course parameters:
Language: English
Level of course: PhD course
Time of year: September 2025
No. of contact hours/hours in total incl. preparation, assignment(s) or the like: 10 hours / 25 hours in total
Capacity limits: 25

Objectives of the course:
The aim of the course is to introduce the student to the basic use of the programming language Python.
The course is designed to build up the basic skills in Python necessary for attending the course Basic Data Science in Python.

Learning outcomes and competences:
At the end of the course, the student should be able to:

• Read and write data in Python, perform basic operations with variables, data structures
• Use and create simple functions
• Find and use libraries
• Create and work with Jupyter notebooks, data preprocessing cleaning and simple statistics
• Finding help in the community (tutorials, examples, fora).

 
Course contents:

1. Introduction to python, the python interpreter and the compiled code
2. Basic python programming, data structures, functions, lambda functions, classes
3. Virtual environments and conda
4. Jupyter notebooks and jupyterlab for easy data analysis and reporting
5. Installing and using libraries (pip, conda), loading data
6. Main python libraries for data analysis: numpy, pandas, scikit-learn
7. Data pre-processing, data cleaning, simple statistics
8. Displaying results with matplotlib
9. Where to get help (tutorials, examples, community)

Prerequisites:
Basic computer skills

Name of lecturer:
Davide Mottin (Data-Intensive Systems group, Computer Science, Faculty of Natural Sciences)

Type of course/teaching methods:
Lectures interlaced with practical exercises and homework.

Literature:
McKinney, W., 2020. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython.  O'Reilly Media, Inc.. (Free online version available)

Course homepage:
Brightspace

Course assessment:
Attendance to the course and a short assignment in the form of a Jupyter notebook where the students use python to complete basic tasks covered in the course, such as loading data, preprocessing it, computing simple statistics, and displaying results.

Provider:
GSNS / Department of Computer Science

Special comments on this course:
It is assumed that each student works on her/his own computer. The PhD students should install Python and Conda on their computers before the course starts.
PhD students who intend to follow the course Basic Data Science in Python will have priority in the course Introduction to Python for Data Science.

Place:
The Conference Centre, Fredrik Nielsens vej 2-4, 8000 Aarhus C

No show fee:
Course participants on our transferable skills courses, who do not show up at the course or cancel their course participation after the course cancellation deadline (without providing a doctor’s note), may have to pay a no-show fee, unless someone from the waiting list is able to take part in the course instead.

The no-show fee is DKK 1,200 (the price of one ECTS). The no-show fee has been introduced due to many late cancellations, thus preventing people from the waiting lists to have a seat at the courses.

Registration:

• Participation in the course is without cost for PhD students from Aarhus University

Due to an Agreement between Danish Universities that came into force as of 1 January 2011, participants from other universities than Aarhus University will have to pay DKK 1,200 per ECTS. In principle this also applies to external parties, but exemption can be granted under specific circumstances.

Please be aware that your registration for the course not necessarily equals your admission for the course. You will receive an e-mail after the registration deadline regarding whether you are admitted for the course or if you are registered on the waiting list. Please note that seats are allocated on a first-come-first-served basis.

Course dates:

• 09 September 2025 09:00 - 15:00
• 12 September 2025 09:00 - 15:00