Beginning Data Science with Python and Jupyter, Perform reproducible data analyses with these data exploration tools
- Created by Packt Publishing
- 3 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Description
Getting started with data science doesn’t 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. You’ll 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. We’ll 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.
About The Author
Chris DallaVilla is the founder and CEO of VALID., an independent marketing consulting practice specializing in providing data-driven solutions that help chief marketing officers and their teams strengthen their planning and execution, and drive results. Chris has expertise in digital and social media marketing, as well as certifications in Agile, Google AdWords, and Google Analytics. He studied computer science at Harvard University, design technology at Massachusetts College of Art and Design, and advertising and marketing communications at the Questrom School of Business at Boston University.
Who is the target audience?
- This course is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science.
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