https://github.com/stared/python-neuroaspects-2016
First steps with data analysis in Python - Aspects of Neuroscience 2016
https://github.com/stared/python-neuroaspects-2016
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First steps with data analysis in Python - Aspects of Neuroscience 2016
- Host: GitHub
- URL: https://github.com/stared/python-neuroaspects-2016
- Owner: stared
- Created: 2016-11-22T17:49:19.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2016-11-28T12:10:21.000Z (over 8 years ago)
- Last Synced: 2025-01-13T19:55:25.840Z (6 months ago)
- Language: Jupyter Notebook
- Size: 855 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# First steps with data analysis in Python
[First steps with data analysis in Python](First steps with data analysis in Python) - a 3h hands-on workshop at [Aspects of Neuroscience 2016](http://neuroaspects.org/), 25-27th November 2016 (Warsaw, Poland), by [Piotr Migdał, PhD](http://p.migdal.pl/).
## Abstract
Do you want to learn how to write scripts, and use programming to solve practical data analysis problems? Python is the way to go! It starts in a simple way, but is one of the best environments for data science (including machine learning and artificial neural networks).
You will learn how to load data, process it and make plots.It is a basic introduction, starting from basic structures (like dictionaries and lists) and control flow (if, else, loops). If you are clicking your way to get results, or not yet comfortable it any programming language (but would love to change that!), it is for you!
We will be using [Jupyter Notebook](http://jupyter.org/) - an environment, which makes interaction with data convenient and beautiful. Notebooks are also an easy ways to share data analysis and results with others.
## Installation
Before attending workshops, install [Python 3.5 from Anaconda](ttps://www.continuum.io/downloads). Downloading and installation takes some time, so please do it before (otherwise we won’t wait for you during the workshop). If you have problem with it, drop me an email.
Additionally, I recommend opening the command line (or for Windows: Anaconda console) and typing:
```bash
$ conda install seaborn
```
to get much nicer plots.
## Outline
(notebook are in progress)
* [0. What is programming? Why Python?](0_intro.ipynb)
* [1. Programming in Python](1_basic_programming.ipynb)
* [2. More of Python](2_more_of_python.ipynb)
* [3. Data analysis in Python](3_data_analysis.ipynb)
## References
Some notebooks contain links. Here are a few additional ones:
Just after the workshop I've spotted a book with an introduction to Python for science:
http://www.southampton.ac.uk/~fangohr/teaching/python/book.html
There were questions how to move from MATLAB to Python. This once covers it:
http://scottsievert.com/blog/2015/09/01/matlab-to-python/
My general science to data science introduction:
http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html
The state of current recurrent neural networks
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
And if you actually want to play with it:
https://gist.github.com/stared/70daf8e0334abf6e7527259e7221f568
(Be sure to look at the references)