Another essential skill in data analysis is data . Visuals are extremely important for both exploratory data analysis, as well the communication of your results. Matplotlib is the most commonly used library for this in Python. Get inspired by viewing some plots and graphs: Matplotlib Gallery Take a look at some sample code: Matplotlib Examples Review the Matplotlib chapter on DataCamp: DataCamp Python for Data Science Come up with some visualizations for your toy dataset.
Many developers (including myself) have included learning machine learning in their new year resolutions for 2018. Even after blocking an hour everyday in the calendar, I am hardly able to make progress. The key reason for this is the confusion on where to start and how to get started. It is overwhelming for an average developer to get started with machine learning.
Guest blog post by Martijn Theuwissen, co-founder at DataCamp.Other Python resources can be found here. Python is widely used for data analysis and you might have considered learning it yourself (if not, or if you're still looking for that bit of extra motivation to get started, see why you should be learning Python below). Of course, learning on your own can be a challenge and some guidance is always helpful. Guidance to learn Python for working with data is exactly what this article will provide you with. We will discuss steps you should take for learning Python accompanied with some essential resources, such as the free Python for Data Analysis courses and tutorials from DataCamp as well as reading and learning materials.