Python Programming for Data Analysis


24-28 January 2022


Due to the COVID-19 outbreak, this course will be held online


Course overview

Python is a general-purpose programming language used in disparate fields and is becoming more and more popular for data science. In our course, the student will learn about the powerful tools to perform “data wrangling”, i.e. to clean, unify and transform “raw data” into an accessible dataset to make it more appropriate for a variety of downstream analyses. This course will introduce the learner to the basics of the Python programming language and its data science libraries such as NumPy and Pandas as well as data visualization libraries such as Matplotlib, Altair, and Plotly. By the end of this course, students will be able to take “raw data”, clean it, manipulate it, and run basic descriptive statistical analyses. Lessons consist of lectures followed by practical exercises where students will put into practice what they just learned during the course by solving problems and exercises of increasing difficulty.



●       The course is aimed at complete beginners and assumes no prior programming experience.
●       As programming tools we are going to use Jupyter Notebook, a web application provided by the organizers, thus users are required to only have an updated web browser



Monday– Classes from 2-8 PM Berlin time

○        Course overviews

○        Introduction to computer programming, Python and Jupyter notebook

○        Data types, data structures and variables

○        Operators

○        Strings and Lists

○        Control structures (Loop and Conditional)

Tuesday– Classes from 2-8 PM Berlin time

○        I/O

○        Functions (and lambda functions)

○        Exception handling

○        Iterables, Iterators and Generators

○        Objects and Object Oriented Programming

Wednesday– Classes from 2-8 PM Berlin time

○        Packages: extend Python functionalities

○        Overview of Python most used packages for Data Analysis

○        NumPy: vectorization, array operations, slicing, masking and broadcasting


Thursday– Classes from 2-8 PM Berlin time

○        Pandas Series and DataFrame

○        Data I/O with Pandas

○        Data cleaning and preparation

○        Data aggregation and data wrangling

Friday– Classes from 2-8 PM Berlin time

○        Data visualization with Matplotlib, Seaborn, Altair and Plotly






Cost overview

Should you have any further questions, please send an email to

Cancellation Policy:


> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.


Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.