What Is This Course About?
Let's go through how this course can help you learn data analytics and the prerequisites.
We'll cover the following
About this course
This course’s curriculum is organized by eight data “stories,” each of which is interesting on its own and revolves around Python data analytics. Each story/chapter contains all the information you’d need to get the raw data and then write the Python analytics code necessary to solve a specific problem.
If you’re motivated and have a bit of available time, there’s no reason you can’t use those stories to teach yourself data analytics in 30 days. You’ll find everything you need to build your own basic data analytics skills, including:
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Getting Python up and running on Jupyter Notebooks (or JupyterLab, if you prefer)
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Finding and cleaning data sources
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Plotting your data
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Understanding results through domain knowledge and tools like regression lines
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Python functionalities that show the graphs of the data, as shown in the figure.
Expectations from this course
While not enough to give Ph. D. in statistics, this course will teach you how to use Python and Jupyter Notebooks to find, manage, interpret and process a wide range of data sources.
Whether you dream of becoming a data scientist or just want to know more about data analytics, this crash course for analytics tools can help you across the developer world. And have much more fun while you’re at it.
Let’s get started!
Prerequisites of this course
To get the most out of this course, it’s important to have a fundamental understanding of general programming concepts and some experience with Python programming. However, if you feel you’re not quite there yet, catching up shouldn’t take you too long. For a Python refresher, try Educative’s free course, Python 3 From Scratch.