python

Python and data science

Step 1: Learn the basics of Python language

You should start by understanding the basics of the language, libraries and data structure. The free interactive Python tutorial by DataCamp is one of the best places to start your journey. This 4 hour coding course focuses on how to get started with Python for data science and by the end you should be comfortable with the basic concepts of the language.

Specifically learn: Lists, Tuples, Dictionaries, List comprehensions, Dictionary comprehensions

Assignment: Take the interactive Python tutorial by DataCamp

Alternate resources: If interactive coding is not your style of learning, you can also look at The Google Class for Python. It is a 2 day class series and also covers some of the parts discussed later.

Step 2: Learn Regular Expressions in Python

You will need to use them a lot for data cleansing, especially if you are working on text data. The best way to learn Regular expressions is to go through the Google class and keep this cheat sheet handy.

Assignment: Do the baby names exercise

Step 3: Learn Scientific libraries in Python – NumPy, SciPy, Matplotlib and Pandas

This is where fun begins! Here is a brief introduction to various libraries. Let’s start practicing some common operations.

Practice the NumPy tutorial thoroughly, especially NumPy arrays. This will form a good foundation for things to come.

Next, look at the SciPy tutorials. Go through the introduction and the basics and do the remaining ones basis your needs.

If you guessed Matplotlib tutorials next, you are wrong! They are too comprehensive for our need here. Instead look at this ipython notebook till Line 68 (i.e. till animations)

Finally, let us look at Pandas. Pandas provide DataFrame functionality (like R) for Python. This is also where you should spend good time practicing. Pandas would become the most effective tool for all mid-size data analysis. Start with a short introduction, 10 minutes to pandas. Then move on to a more detailed tutorial on pandas. Check out DataCamp’s course on Pandas Foundations You can also look at Exploratory Data Analysis with Pandas and Data munging with Pandas

Additional Resources:

If you need a book on Pandas and NumPy, “Python for Data Analysis by Wes McKinney”

Step 4: Effective Data Visualization

Go through this lecture form CS109. You can ignore the initial 2 minutes, but what follows after that is awesome! Follow this lecture up with this assignment

Check out Bokeh Data Visualization Tutorial from DataCamp

Step 5: Learn Scikit-learn and Machine Learning

Now, we come to the meat of this entire process. Scikit-learn is the most useful library on python for machine learning. Here is a brief overview of the library. Go through lecture 10 to lecture 18 from CS109 course from Harvard. You will go through an overview of machine learning, Supervised learning algorithms like regressions, decision trees, ensemble modeling and non-supervised learning algorithms like clustering. Follow individual lectures with the assignments from those lectures.

Additional Resources:

If there is one book, you must read, it is Programming Collective Intelligence – a classic, but still one of the best books on the subject.

Additionally, you can also follow one of the best courses on Machine Learning course from Yaser Abu-Mostafa. If you need more lucid explanation for the techniques, you can opt for the Machine learning course from Andrew Ng and follow the exercises on Python.

Step 6: Practice, practice and Practice

Congratulations, you made it!

You now have all what you need in technical skills. It is a matter of practice and what better place to practice than compete with fellow Data Scientists on Kaggle. Go, dive into one of the live competitions currently running on Kaggle and give all what you have learnt a try!

Step 7: Deep Learning

Now that you have learnt most of machine learning techniques, it is time to give Deep Learning a shot. There is a good chance that you already know what is Deep Learning, but if you still need a brief intro, here it is.

I am myself new to deep learning, so please take these suggestions with a pinch of salt. The most comprehensive resource is deeplearning.net. You will find everything here – lectures, datasets, challenges, tutorials. You can also try the course from Geoff Hinton a try in a bid to understand the basics of Neural Networks.

what to learn in python data science