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Data science and machine learning among the most popular fields today, in which Python is one of the most popular languages. As you might expect, there are several libraries and tools available to you. As you begin your journey into this field, it will help to be familiar with the most common frameworks and techniques. This is what we’re here to help you with!
We’re going to introduce Jupyter notebooks, a common tool for data scientists. We’re also going to show off pandas which is used to help manage and explore data, and scikit-learn for incorporating machine learning. You’ll see how to bring everything together and walk through a common scenario of loading data and running it through a particular algorithm.
Our goal is to help show you the tools you’ll be using as you dig deeper into data science and machine learning. While we won’t highlight the decision points of algorithms or collecting the data (there are other courses available for those topics), you will explore the techniques and libraries.
What you’ll learn¶
- Jupyter notebooks
- pandas DataFrame for managing data
- NumPy for arrays
- scikit-learn for machine learning
What we don’t cover¶
- Theory behind machine learning
- Algorithm selection
- Managing big data
As the goal of this course is to help get you up to speed on Python so you can work through a quick start, the next step after completing the videos is to follow a tutorial! Here’s a few of our favorites: