DataCamp’s New Introductory Analysis Tutorials

 

DataCamp has been making significant progress with their tutorials in 2017. There are now a number of tutorials available for both R and Python to help you get started. They teach you through hands-on learning of the materials. The Python tutorials are in self-contained modules. This means you don’t need to have Python installed to run through the tutorial. You can do it all in your browser window! I’ve included links to the tutorials that are great for new analysts/scientists to use and get some experience under their belts.

Python NumPy Tutorial: Array Computing – This is a nice introduction into using NumPy package for Python. The biggest feature of this package is that NumPy allows you to convert files/lists into arrays, which tend to function faster and more efficiently than Python’s lists.

Exploring H-1B Data with R – This is a multi-post series that is split into 3 parts. I’ve linked to the first part which helps introduce you to R and collecting data for analysis. By the third part of the series, you’ll be using that scraped data to build out a map to display your analysis

Python Machine Learning: Scikit-Learn Tutorial – Scikit-learn package is a backbone for machine learning in Python. This tutorial will walk you through an exploratory analysis, principal components analysis (PCA), and then begin fitting a model to predict images of numbers.

All-in-all these are some great first steps towards being able to run an analysis on your own. I’m hoping that throughout the rest of the year the DataCamp team continues to put out great content like these tutorials. I’d like to take a moment and appreciate the work that Ted Kwartler and Karlijn Willems did for their tutorials. Until next time, #statheads!

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