Python and Data Science
Python is a general-purpose programming language with one of the fastest growing communities. Its increasing popularity is due to the fact that it is quite easy to learn. Even with just a few basic concepts of programming, one can expect first results very early. Especially in the field of research, Python has become one of the most common programming languages. It offers a variety of useful scientific libraries such as for numerical calculations, data visualisation or machine learning and deep learning.
Our focus of this course will be the application of some of those libraries in order for you to be enabled to earn insightful results from data for recent and future projects of yours. Starting with getting used to the syntax and some basic concepts of programming in general we will learn how to work with a straightforward but yet powerful programming ecosystem which will provide most of the necessary tools in order to handle your data.
Besides theoretical input within the sessions we will focus on practical applications. With hands on some test data sets you will learn how to clean, visualize, and understand your data with short tasks within and between the sessions. In case you are working on your own data currently, you are encouraged to bring your own data sets as assets and try out what you have learned. The programming trainer will be available for your questions within the sessions.
- Python Basics: A quick overview of Python for those who are completely new to this topic or to those who need a brief recapitulation.
- Data Structure with numpy and pandas: Learn how to structure, clean and aggregate your data and earn first insights.
- Statistical Analysis: Find common patterns and trends in your data sets to convert them into meaningful information.
- Data Visualisation with matplotlib and seaborn: Learn how to present your data expressive manner.
Mixture of theoretical input, exercises, self-learning and collegial exchange/Q&A.
To participate you need an internet-enabled computer, a good microphone & speaker or headset and a webcam. In addition, a second monitor is recommended.
For Programming wie use Anaconda (https://www.anaconda.com/)
Weitere Informationen zu dieser Veranstaltung
Koordination der überfachlichen Promovierendenausbildung | Kasseler Graduiertenprogramm
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M. Sc. Lars Grygosch
Lars Grygosch has been part of the Codingschule team for 3 1/2 years. Codingschule is a non-profit company that promotes equal opportunities and diversity in the tech industry. In this way, we give people access to digital education, regardless of their background. Lars taught himself programming as part of his astrophysics studies in order to generate simulations of populations of black holes in our galaxy and to determine the chances of success of the detectability of isolated black holes with the eROSITA space telescope. At Codingschule, Lars is responsible for concepts and trainings on various topics, such as the basics of programming, data science and machine learning.