This course is intended to be interactive (you will learn by doing things in groups and by watching me do it live and talking about it). I will use Jupyter, an environment that allows us to do everything in real time on the browser, so you will work locally on your computer and my working is visible on the projected screen. It basically allows you to take your own notes on the lecture notes, and run/modify the programs on your lecture notes. You will receive the course material before the course starts.
This is a short course that introduces the participants to using Python in the broad fields of molecular biology and biochemistry. It was designed to be done in less than a week, four days at an optimum pace or three for those that are already introduced to Python programming. It starts with a tutorial like introduction to programming with Python, thus even people with extremely limited programming skills can participate. It then goes to present how Python can be used to make simple to advanced plots, how one can use Python for scientific computation and in the final part it takes the participant through every 'omics level with small scale projects that are discussed and modified in the classroom. Before the end of it, participants also learn how to use Python to interogate remote data and databases, use Python in the Cloud, and wrap calls to other programming languages.
This course will not guarantee you will be a good programmer in three days. Today learning to program is as important as learning to write. But it takes years to become a good programmer. What is important is to persevere! There is an intent to also offer similarly short courses but with a more targeted scope, like sequencing, metabolic modeling and systems biology.
The creator and organizer of this course is Sergiu Netotea, researcher in bioinformatics at Chalmers, Gothenburg. Sergiu is member of NBIS (National Bioinformatics Infrastructure for Sweden), working in the long term support branch. NBIS is a national infrastructure project for Sweden, offering various Bioinformatics services. This is his staff page:
https://nbis.se/about/staff/sergiu-netotea/
This course is held for a fourth time now.
Brief course history:
For this next installment there was a change in course structure, with an additional full day being added. This was due to various reasons: there was not enough time for exercising, the course seemed a bit too packed, there were important chapters such as cloud computing and deep learning being left out.
We expect you to work in fields related to molecular biology and biochemistry, but more distant studies of ecology and clinical medicine are of course welcome. If you have a computer science or mathematics background, this course is still useful because it introduces to you data science for biology.
This course has the secondary aim to establish working connections between NBIS and the participants. So if you have a special interest to bioinformatics and scientific programming in your work we would like to hear it and perhaps make suggestions.
This is handled now on SciLifeLab anouncement page: https://www.scilifelab.se/events/quick-and-clean-advanced-python-for-data-science/
We do not ask you to learn or read anything before the course starts or after it ends. But for those who wish to improve we recommend the following books:
Python for Biologists: A complete programming course for beginners This book will take you slowly through programming with Python reinforcing good programming skills.
Learning Python: Interactive computing and Visualization: also contains lots of free notebooks
The Elements of Statistical Learning Math and statistics heavy book of ML (Bayesian methods in particular)
Tom Mitchell's Machine Learning Another famous book of ML.
http://www.deeplearningbook.org/ The DL Bible
As Python is surrounded by a whole computing galaxy, this book is an introduction to it having Python in its center.
We reccommend you to follow other NBIS and SciLifeLab courses as well:
When you get stuck, I recommend you to use this venue for getting help:
And I recommend this forum for biology related Python dillemas:
If you like blogs, this is a famous blog on scientific computing with Python:
Here is a nice collection of online IPython notebooks teaching various subjects in data science:
And not in the least, we also recommend you to learn other languages, if for no other reason but to understand just how awesome Python in comparison. My magic stack is Js, C, Python, Closure and Julia. People around me also like R, C++, Java, Matlab, Perl and I also use them, but I would not jump in the water to rescue them.
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