Educators:
Wasiu Akanni, Renato Alves, Toby Hodges, Kimberly Meechan, Nikolaos Papadopoulos, Jelle Scholtalbers (HD-HuB)

Date:
06/07/2020 - 10/07/2020
09:00 - 12:30

Location:
Online

Contents:
Have you learned the basics of Python programming, and want to take your skills to the next level? Do you find yourself mystified by some of the syntax and wizardry you see in other people’s code? Do you feel that you can achieve most things you want to do with Python, but it takes you much too long and you’re never satisfied with the resulting program?
As with all Bio-IT courses, these sessions will provide plenty of opportunity for hands-on practice and challenges to tackle, guided by a team of instructors and helpers.

Planned Course Content
- An overview of elements of Python syntax
- Working with data using numpy and pandas
- Creating figures with matplotlib
- Good practices for better programs and better collaborations

Learning goals:
Taking place over five consecutive mornings, this course is aimed at researchers who are familiar with the fundamentals of the Python programming language – strings, lists, dictionaries, for loops, function definitions, module imports, etc – but want to improve their skills to analyse and visualise data, write better code, and take advantage of some of the more advanced features of the language.

Prerequisites:
To follow the course material, participants are expected to already be familiar with the following concepts in Python:

• basic data types, e.g. integers, floats, strings, lists, dictionaries
• control flow i.e. for loops and if elif else conditionals
• function definitions and variable scope
• import statements
• working in an interactive development environment e.g. Jupyter

In addition, participants will need access to a computer with Python version ≥3.7 installed, preferably via Anaconda or miniconda. Your environment must include the following modules and tools (all included in Anaconda by default):

• numpy
• pandas
• matplotlib
• jupyter
• pycodestyle
• pyflakes
• pylint

Keywords:
Python, numpy, pandas, matplotlib, jupyter, pycodestyle, pyflakes, pylint

Tools:
Python