29 January 2021
Data Science with Pythononline course
This course will focus on using Python for Data Science. You will learn to use the tools needed to analyze, understand and gain new insights from data in Python. We will begin with the very basics of Python and end with utilizing powerful libraries such This course will focus on using Python for Data Science. You will learn to use the tools needed to analyze, understand and gain new insights from data in Python. We will begin with the very basics of Python and end with utilizing powerful libraries such as Numpy, Pandas and Matplotlib. The course will also give compact insights to Machine Learning by realizing simple algorithms with Python.
Reading week: January 4th - January 8th, 2021. Flexible, 5- 10 hours preparatory work to be done on-demand.
Online course: January 11th - January 29th, 2021. Estimated meeting times: Mondays through Fridays. Exact session times will be confirmed once registrations have closed (sessions will be scheduled according to the time zones of the registered course participants). Should you have any questions regarding the course timetable, please contact us at firstname.lastname@example.org
Please note this is a full-time, intensive course. Weeks 1-3 will involve approximately 30 hours of workload.
Jason Harris is a Research Assistant in the Robotics and Biology Laboratory (RBO) of the TU Berlin and a graduate (final year) student in Computer Engineering, specializing in robotic technologies.
This course is designed for current university students, working professionals and any individuals with an interest in furthering their knowledge and skills in programming with Python for Data Science and Machine Learning.
Participants from all fields and disciplines are welcome.
Basic programming knowledge is also required for this course. Students should be able to write and run small programs in the language of their choice. Students should also have basic knowledge in linear algebra and statistics/probability theory and know what loops, conditionals, methods/functions, libraries, vectors, matrices, gradient and probability distributions are.
Code in Python with Jupyter Notebooks
● Use popular libraries such as Numpy, Pandas, Matplotlib etc. in Python
● Manipulate and visualize data in Python
● Do object orientated coding in Python
● Run exploratory analysis on data and gain new insights
● Implement simple Machine Learning algorithms
EUR 920: Student
EUR 1320: Working professional/Non-student