.MP4, AVC, 400 kbps, 1280×720 | English, AAC, 128 kbps, 2 Ch | 2h 49m | 723 MBInstructor: Chris Dalla VillaPerform reproducible data analyses with these data exploration toolsGetting started with data science doesnt have to be an uphill battle.

This step-by-step video course is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course.

Youll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world.

We’ll start with understanding the basics of Jupyter and its standard features.

You’ll be analyzing an example of a data analytics report.

After analyzing a data analytics report, next step is to implement multiple classification algorithms.

Well then show you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.

Finish up by learning to visualize these data interactively.

What You Will LearnIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findings