Programming with Python for researchers

Introduction

Key terms: Quantitative research, digital skills, Python programming language, introductory course, relevant for PhD students in all phases of their project

ECTS: 2
Number of session: 3
Hours per session: 6

Ever done hundreds of repetitive actions on rows in tables? Ever wondered how computer programs work, or how programming can help your research?

Then, this is the course for you! You will be learning the basic components of programming with Python, write your own programs, trace bugs and inspect variables in Jupyter Notebooks.

The course consists of 3 contact sessions of 6 hours each. The course will consist of lecture and hands-on programming. You will directly learn how to program, while the teacher and teaching assistants, all professional programmers, will be available for any questions that might pop up.

There are small assignments in JupyterHub. Since this hub can be reached from all over the world, participants can continue to work on the assignments from their office or from home outside of the contact sessions.

During the course and 3 weeks thereafter, a progress update will be sent to each participant. The final deadline for the assignments will be 3 weeks after the last contact session.

Who is this for?

Relations with other courses

There is no overlap and/or relation to any other course provided by EGSH.

Sessions and preparations

Day 1: Variables, collection data types and control flow
In this session, we will introduce variable and data types (i.e., Integer, Float, String, Boolean), collection data types (i.e., lists, dictionaries, sets, tuples) and talk about ways to control the flow of the program (i.e., if, else, loops).

Day 2: Functions, namespace and scoping and file manipulation
In this session, we will get to know what functions are. We will introduce namespace, scoping, errors & exceptions and learn how to read and write files.

Day 3: Data analysis with numpy
In this session, we will get to know the numpy package and learn how to use it to analyse data and visualise tabular data.