Upgrade your Python Skills: Data Wrangling & Plotting


The handling of data is a recurring task for data analysts. Reading in experimental data, checking its properties, and creating visualisations may become tedious tasks. Hence, increasing the efficiency in this process is beneficial for many professionals handling data. Spreadsheet-based software lacks the ability to properly support this process, due to the lack of automation and repeatability. The usage of a high-level scripting language such as Python is ideal for these tasks. 

This course trains participants to use Python effectively to do these tasks. The course focuses on data manipulation and cleaning of tabular data, explorative analysis and visualisation using some important packages such as Pandas, Numpy, Matplotlib and Seaborn.

This course is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be

After setting up the programming environment with the required packages using the conda package manager and an introduction of the Jupyter notebook environment, the data analysis package Pandas and the plotting packages Matplotlib and Seaborn are introduced. Advanced usage of Pandas for different data cleaning and manipulation tasks is taught and the acquired skills will immediately be brought into practice to handle real-world data sets. Applications include time series handling, categorical data, merging data, geospatial data,...

The course closes with a discussion on the scientific Python ecosystem and the visualisation landscape learning participants to create interactive charts.

The course does not cover statistics, data mining, machine learning, or predictive modelling. It aims to provide participants the means to effectively tackle commonly encountered data handling tasks in order to increase the overall efficiency. These skills are both useful for data cleaning as well as feature engineering.

All sessions are hands-on in Jupyter notebooks.

Schrijf je hier in voor lessen uit deze cursus

Upgrade your Python Skills: Data Wrangling & Plotting

  • Type of course: This is an on campus course.
  • Dates & times: March 7, 10, 14, 17 and 24, 2022, from 5.30 pm to 9 pm
  • Venue: UGent, Faculty of Sciences, Campus Sterre, Krijgslaan 281, building S9, 9000 Gent
  • Target audience: The course is intended for professionals who wish to enhance their general data manipulation and visualization skills in Python, with a specific focus on tabular data.
  • Exam/certificate: Participants who attend all classes receive a certificate of attendance via e-mail at the end of the course. There is no exam connected to this course.
  • Course prerequisites: Basic programming skills are required equivalent to Module 4 - Getting Started with Python for Data Scientists of this year's program. A basic (scientific) programming course should suffice. For those who have experience in another programming language (e.g. Matlab, R, ...), following a Python tutorial prior to the course is strongly recommended. A good introduction is the ‘Python language introduction’ section of the Scipy lecture notes: https://scipy-lectures.org/intro/language/python_language.html..
  • Funding: => Our academy is recognised as a service provider for the 'KMO-portefeuille'. In this way small and middle sized businesses located in the Flanders region can save up to 30% on the registration fee for our courses. You can request this subsidy via www.kmo-portefeuille.be up until 14 calender days after the course has started. => UGent PhD students can apply for a full refund from their Doctoral School.
  • Reduction: => If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment => Reduced prices apply to coworkers in governmental institutions, non-profit organisations and higher eduction as well as for students and the unemployed.
  • Enrolling for this course is possible via the IPVW-ICES website.