Intro to Data Science & Machine Learning

This course will introduce you to the world of data science and cover all the major aspects of deriving insights from data sets. In this course, we will show you how to acquire data, clean it for easier analysis, explore and derive insights, convert it into specific features, and model it using machine learning algorithms.

Finally you'll use insights and predictions from your models to make definitive statements about your data. This course will be presented almost entirely in Jupyter notebook form. Jupyter notebooks are one of the primary tools used by data scientists today. They integrate code data images, and interactive widgets in a seamless presentation format.

We also include interactive notebooks that test and exercise every aspect of the data science. Rather than start with a large amount of theory. This course takes a top down approach towards teaching. We first start with the complete worked example, showing you the full flow of a useful data analysis skill.

As we continue in the course, we'll unpack that example, going deeper and deeper into each component until you understand exactly what each line of code in the example is doing. By the end of the course, you'll be able to write the entire example from scratch on your own. We use this approach to give you a broad understanding about what each data science skill entails.

What you'll learn

• The Jupyter notebook programming environment (used by real-world data scientists).
• Popular Python data science libraries: pandas , numpy , matplotlib , scikit-learn.
• The full data science pipeline:
• Acquiring data
• Cleaning data
• Exploring data for insights
• Converting data to features used in machine learning algorithms
• Create and train machine learning models using your data
• Make predictions and derive insights using your models
• Where to go from here to continue your Data Science & ML journey.