Ready to learn how to use Python for data science? This free course has got you covered! 
It will be no surprise to readers that Python is one of the languages most associated with the practice of data science.
While it could be reasonably argued that Python is the absolute top data science programming language, it would be difficult to argue that, along with R and SQL, Python is not one of the top 3.
Regardless of the exact rank of the language, there is no denying that Python is a useful tool for implementing data science in practice. Its ecosystem provides a rich tapestry of libraries covering the entire spectrum of data science pipelines and related data processing and analysis tasks. Knowing data science, Python, and their intersection is a fantastic way to ensure your usefulness as a data scientist.
freeCodeCamp and Maxwell Armi put are responsible for putting together and making available this 12 hour (that’s right, 12 hour!) course on using Python and its ecosystem to write data science code.
This Python data science course will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib.
The course is hands-on by design, takes learning step by step, and provides a strong foundation in Python language concepts as it goes. Data science concepts are covered along the way, and are focused on more exclusively in the final sections of the course. A final course project looking at COVID trend analysis is undertaken using the Python tools you’ve learned along the way.
The course contents are as follows:
Check out the course video below or head on over to freeCodeCamp’s YouTube channel to see it there.
The 12 hour offering from freeCodeCamp states in its subtitle that it is for beginners, and that it will help you learn Python, Pandas, NumPy, and Matplotlib. As far as I can tell, it seems to hold its promise. If you are looking to learn Python and data science, checking it out in depth would be worth your time.
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master’s degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.
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