You know Python. You know Excel. You may even know how to crunch numbers in R using the Tidyverse if you have a statistics background.
But when it comes to applying all this knowledge to the world of data science, you know you need more than these tools to be successful. What makes matters worse is that you are not exactly sure of what order you should be learning which data science tools. It can be a challenge to know exactly where to focus, and how to apply what you do know.
At Mass Street University, we guide statisticians and developers interested in exploring how to process and analyze data—efficiently. In Python for Data Analysis, we focus you on precisely what you need to know, and teach you how best to utilize what you already do know.
In the course, we will teach you how to combine your existing knowledge of Python with tools like Pandas and Numpy. If you have only worked with the basic Python data types, approaching some of the higher order data types can be intimidating. The structure of our course takes you from the simplest tools to the more complex to ensure you stay focused on what you need while you build on your font of data science knowledge.
JupyterLab is one tool you may not be familiar with, and it is a popular data analysis notebook that supports many languages, including Python. Notebook technology is relatively new to the world of data science, and we will go over how JupyterLab will allow you to write much smaller amounts of code efficiently.
There are a ton of data science tools that interact very well with Python to make data science a breeze when explored and taught properly. And at Mass Street University, we make sure that this dynamic is managed as efficiently as possible. Enroll today in Python for Data Analysis to stay focused on what you need to excel in data analysis.
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