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LVx: Intro to Data Science & Machine Learning

A first introduction to data science and machine learning. Use Python to acquire, clean, and analyze data using powerful machine leanring models and popular data science libraries.

Intro to Data Science & Machine Learning
8 weeks
1–5 hours per week
Self-paced
Progress at your own speed
This course is archived

About this course

Skip About this course

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.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills:Jupyter Notebook, Teaching, Machine Learning, Data Analysis, Python (Programming Language), Data Science, Jupyter, Presentations, Forecasting

What you'll learn

Skip 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.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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