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Python for SCIENCE
Second title: SCIENCE Toolbox Course
Provider: Science

Activity no.: 5197-25-02-01There are 50 available seats 
Enrollment deadline: 13/01/2025
Date and time22.01.2025, at: 09:00 - 31.01.2025, at: 16:00
[antalgange]5
Regular seats50
ECTS credits3.00
Contact personAmanda Lybke Rasmussen    E-mail address: amra@di.ku.dk
Enrolment Handling/Course OrganiserAmanda Lybke Rasmussen    E-mail address: amra@di.ku.dk
Written languageEnglish
Teaching languageEnglish
Exam requirementsActive participation.
Course workload
Course workload categoryHours
Lectures20.00
Exercises35.00
Project work15.00
Course Preparation11.00
Sum81.00
Content

This course is an official Toolbox course at SCIENCE-UCPH and a generic course under the Danish PhD regulations. The course introduces the dominant programming language in data science, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open-source libraries available.

The workshop will teach the basic programming constructs in Python and then provide data science examples, including data import, visualization, and analysis. We will introduce integrated development interfaces such as jupyter. We will introduce libraries from active open-source frameworks (numpy, pandas, matplotlib, sklearn, …).

The course is aimed at PhD students, who need tools for data exploration, data analysis, and data visualization. Post Docs, Professors, and Master's thesis students from SCIENCE may register for participation and will be accepted if space permits.

The course will take place on Wed 22, Fri 24, Mon 27, Wed 29, Fri 31 January 2025, from 9 to 16 all days. 

Formel requirements
The number of participants is limited at 50, and priority will be given to PhD students from UCPH-SCIENCE.

Learning outcome
After course completion, the students are expected to be able to:

Knowledge:
- Understand computational thinking concepts.
- Understand key programming elements (e.g. variables, objects, functions, modules).
- Know useful open-source libraries (e.g. pandas, matplotlib, sklearn).

Skills:
- Develop/adapt/extend a computer-based software program for analysis of relevant data.
- Apply good development principles.

Competences:
- Propose relevant analysis methods for scientific data science problems.
- Consider cross-disciplinary data science methods in their research.

Literature
Course lecture slides and exercises.
We will use data, examples, and other material from publicly available sources.

Teaching and learning methods
The course is composed of sessions combining lectures and exercises. For each topic, the students will get hands-on experience in applying, modifying, and programming analysis methods.

Lecturers
Julius B. Kierkegaard, Tenure-Track Assistant Professor, DIKU
Oswin Krause, Associate Professor, DIKU

Remarks
Participants from SCIENCE are exempt from the course fee, for all other participants the course fee is DKK 3600.


For details for this and other Data Science Lab courses, see: http://datalab.science.ku.dk/english/course/

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