Description
Python started off as a general-purpose programming language, but in the last decade it has become a popular environment for data science. The reason is that the community of Python users have recently created useful add-on packages which are suitable for data manipulation, preparation, visualization and analysis. This practical course introduces both base Python and the most important packages in a hands-on way with many exercises.
The contents of the course are:
- Introduction: Python and the Anaconda distribution
- Data types: numbers, strings, lists, tuples, sets and dictionaries
- Automation: control flow and self-defined functions
- Importing data and exporting results
- Managing data with NumPy and pandas
- Graphs with matplotlib and seaborn
- Statistical analysis with statsmodels
The objective of the course is that you are capable of doing data management, visualization and analysis in Python on your own.
Python is an open-source programming language which you can freely download (i.e. the Anaconda distribution). Python version 3 or higher is recommended.
Target audience
This course targets professionals and investigators from diverse areas with little to no Python-programming experience who wish to start using Python for their data manipulation, data exploration or statistical analysis.
Course prerequisites
The course is open to all interested persons. Knowledge of basic statistical concepts and experience with other programming languages are considered advantages, but not required for learning the Python language.
Exam / Certificate
There is no exam connected to this module. If you attend all five classes you will receive a certificate of attendance via e-mail at the end of the course.
Type of course
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.
Schedule
5 Monday and Thursday evenings in December 2024: December 5, 9, 12, 16 and 19, from 5.30 pm to 9.30 pm
Venue
Faculty of Science, Campus Sterre, Krijgslaan 281, Ghent
Course material
Acces to Python scripts and data files
Book recommendations
A recommended handbook for further study is 'An introduction to statistics with Python' by Haslwanter, Thomas (2016), Vienna: Springer. ISBN 978-3-319-28316-6. Please note that you do not need a copy of this book to follow the course.
Fees
The participation fee is 1000 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations.
- Industry, private sector, profession*: € 1000
- Non profit, government, higher education staff: € 750
- (Doctoral) students, unemployed: € 450
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
Registration
To register, add the course below to your shopping cart and proceed to checkout.
Is this your first registration for a Beta Academy course? In that case, you will need to create an account first. Afterward, you will receive a confirmation email to activate your account on the academy platform. You do not have to click on the activation link but can immediately return to your shopping cart to complete your course registration. If you do not receive a confirmation email for your course order, please contact our Science Academy at science-academy@ugent.be.
Are you currently on the Nova-academy website? To proceed with the registration, simply click on the "More information" box located on the left side.
UGent PhD students
Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.
By registering for a course or event organized by the Science Academy, you agree to the cancellation procedure that you can find on our website
KMO-portefeuille
Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Organisation
Science Academy (IPVW)
Faculty of Sciences
science-academy@ugent.be
Website
