Summer SchoolPython for Data Analysis and Visualization- fully booked, waitinglist only
| Studienort | Deutschland, Berlin |
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| Art | On Campus, Vollzeit |
| Nominale Dauer | 2 Wochen |
| Studiensprache | Englisch |
| Auszeichnungen | Summer School |
| Akkreditierung | 3 ECTS |
| Studiengebühren | 1.050 € pro Programm The program price consists of the course/tuition fee (student or working professional, see details below) plus the registration fee (€60). Student course/tuition fee: €990 This course/tuition fee covers the course, course materials and a cultural program. |
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| Anmeldegebühr | 60 € einmalig The registration fee is in addition to the course/tuition fee and covers the processing of your application. It is payable upon registration. Please note that the registration fee is non-refundable. |
| Einstiegsqualifikation | Die Zulassungsunterlagen werden in folgenden Sprachen akzeptiert: Englisch / Deutsch. Please upload one of the following documents:
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| Sprachanforderungen | Englisch All applicants are required to upload a document or certificate to demonstrate their proficiency in English language. If you are a non-native English speaker, you must prove you have a score equivalent to the level B2 or above in the European system (the Common European Framework of Reference for Languages, or CEFR), or provide evidence that you’ve undertaken an equivalent degree/studies in English. CEFR: B2 More details: www.tu.berlin/international/summer-school/requirements If you are a native English speaker, please select this during registration. You will then be exempt from having to upload proof of English level. |
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| Sonstige Voraussetzungen | Besondere Anforderungen für Nicht-EU Bewerber: Please upload your insurance policy in English (all pages). |
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| Weitere Informationen |
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Übersicht
According to the 2022 annual IEEE Spectrum survey of the top programming languages, Python remains the most popular programming language in job listings. In this
course, the fundamentals of Python are covered, with a special focus on the skills necessary for
in-depth data analyses and data visualization. These two skills are fundamental in a wide range
of disciplines, including but not limited to STEM (Sciences, Technology, Engineering and
Mathematics) and Humanities fields of study.
In this course, we will cover the following:
1. Data types and compound data structures
2. Conditional statements and loops
3. Python functions
4. Importing, exporting and analyze different types of data using pandas
5. Visualizing data using Matplotlib and Seaborn
6. Bonus: developing dashboards using Metabase
At the end of the two weeks course, students will work and present a final personal data
analytics and visualization project.
Learning goals
In this course, the fundamentals of Python are covered, with a special focus on the skills
necessary for in-depth data analyses and data visualization. These skills are fundamental in a
wide range of disciplines, including but not limited to STEM (Sciences, Technology, Engineering
and Mathematics) and Humanities fields of study.
The learning goals of the course can be summarised in the following points:
1. install and run Python and all other needed external packages
2. write basic python code, including conditional statements, loops and functions
3. import and export data in python
4. analyze different types of data in python using the pandas package
5. create meaningful visualizations in python to summarize different types of data usingMatplotlib and seaboarn packages
6. Effectively and clearly present analytical results, i.e. data storytelling
Main course components
In this course, we will cover the following regarding Python as a tool for data analysis and
visualization:
1. Data types and compound data structures
2. Conditional statements and loops
3. Python functions
4. Visualizing data using Matplotlib and Seaborn
5. Importing, exporting and analyze different types of data using pandas
6. Bonus: developing dashboards using Metabase
The main learning tools will be:
1. Python jupyter notebooks and .py files shared in class by the instructor
2. Recommended educational material including online ebooks, blog posts, web-based tutorials and videos. All such materials are free for educational use and will be shared with the students via email and in class
3. Recommended books and online courses (not free and these will be optional)
Students are assumed to have their own personal laptops with a Python installation as the main hardware tool required for this course.