MOOCs have become increasingly popular in recent years, offering free on their online platforms some of the best courses from the best universities and experts from around the world. You will find Python courses on Coursera (https://www.coursera.org/), Edx (https://www.edx.org/), and Udacity (https://www.udacity.com). Another great source is the MIT open course ware, which is easily accessible (https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/). When you consult each of these sites, you may find different active courses on Python. We recommend a free, always available, and do it at your own pace course by Peter Norvig, the Director of Research at Google Inc. This course aims to take your knowledge of Python to a higher level of proficiency.

The Python Conference (PyCon) is an annual convention organized at various locations around the world with the purpose of promoting the usage and diffusion of the Python language. During such conventions, tutorials, hands-on demonstrations, and training sessions are commonly held. You can check out http://www.pycon.org/ to find out where and when the next PyCon will be held near you. If you cannot attend, you can still perform a search on https://www.youtube.com/ because most of the interesting sessions are recorded and uploaded there. Attending and watching the real demonstration is a different thing anyway, so we warmly suggest you attend such conventions because they are really worth it. Similarly, PyData, a community of Python developers and users devoted to data analysis, hold many events around the world. You can check out http://pydata.org/events.html for upcoming events (to go to and attend) and check whether any past event may have interested you. As with PyCon, presentations are often available on YouTube, on dedicated channels such as PyDataTV.

Sometimes, you need some written explanations and the opportunity to test some sample code by yourself. Jupyter, an open tool like Python itself, offers you all of this via its notebooks—interactive web pages where you will find both explanations and example code that can be tested directly. We devote explanations about Jupyter and its kernels throughout the book because it is a real data science workhorse. It allows you easily to run Python scripts and evaluate their effects on the data that you are working on.

The GitHub location of the IPython kernel (the Python kernel of Jupyter, since Jupyter can run many different programming languages) offers a complete list of example notebooks. You can check it out at: https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks. In particular, a section of the list is about General Python Programming, whereas another one is about Statistics, Machine Learning, and Data Science, where you will find quite a lot of examples of Python scripts that you can take inspiration from in your learning.

If you want to do something that can take your Python coding ability to a different level, we suggest you go and take a challenge on Kaggle. Kaggle (http://www.kaggle.com/) is a platform for predictive modeling and analytic competitions, which applies the idea of competitive programming (participants try to program according to the provided specifications) in data science by proposing challenging data problems to participants and asking them to provide possible solutions that are evaluated on a test set. The results of the test set are partly public, partly private. The most interesting part for a Python learner is the opportunity to take part in a real problem with no obvious solution, which requires you to code something to propose possible solutions to the problem, even something simple or naive (which we warmly suggest you start with first before getting involved in complex solutions). By doing so, the learner will come across interesting tutorials, beat-the-benchmark codes, helpful communities of data scientists, and some very smart solutions proposed by other data scientists or Kaggle itself in its blog, no free hunch (http://blog.kaggle.com/).

You may wonder how to find the right challenge for yourself. Just have a look at the present and past competitions at https://www.kaggle.com/competitions and look for every competition that has knowledge as a reward. You will be surprised to find an ideal stage for learning about how other data scientists code in Python, and you can immediately apply what you learn from this book.