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Developing Data Products

Data Science,

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
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Rating 4.4 based on 345 ratings
Length 5 weeks
Starts Oct 5 (3 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

data products

This is a great course if you want to learn how to develop data products that others can use.

The course materials cover presenting data products and the course project requires the student to develop a data product in R/Shiny that is hosted on the internet.

It covers production of data products (as the name suggests), such as web-based data apps using Shiny and HTML-based presentations using Slidify, among other things.

Good overview of tools to use in rstudio to produce data products.

Especially liked shiny: efficient tool to produce useful data products on the web.

Not really sure why making presentations in R would make sense given that we have Powerpoint, Keynote and Canva available to create stunning presentations for our data products.

I was expecting more examples related to the industry, such as data products for data journals, telcos, etc.

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great course

Great course!

Thanks professors for sharing this great course.

Great course, I just missed some material on distributing data products as files or objects.

This is a great course.

Great course.

I didn't learn many data science concepts in this class a great course, I know a lot of new stuff Great experience This course is very hands-on and good for building quick prototypes.

Great Course This course is packed with rich content that draws on all of the data science concepts in the other 8 courses.

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shiny apps

Gives you a good basic introduction to plot_ly and shiny apps.

Good range of topics - updates to content would be useful --> eg http://jupyter.org Great course to consolidate everything that we have learned before good introduction to developing R presentations and shiny apps.

From this course I took away how to make shiny apps.

I particularly enjoyed learning Leaflet and Shiny Apps.

Love this course and enjoyed the shiny apps building exercise.

However, the course project does a good job to get your feet wet with Shiny Apps.

This one's a keeper -- learning to do both Shiny apps and Slidify/RPres will be quite useful in the near future.

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data science specialization

This is the 9th course in teh JHU Data Science Specialization for me and it's also the one I enjoyed the most.

This is the final course before the capstone in the Data Science specialization from Johns Hopkins on Coursera.

The lecture is not so fluent taught than other coursers in the specialization Compared to the other classes in the JHU Data Science specialization, this one is pretty laid back.

This is a very useful course in the Data Science Specialization that teaches us how to present the results of our data analysis using Shiny, Slidify and other R based data presentation tools.

After completing the Data Science Specialization courses with the course of Developing Data Products, I finally understand how important and useful R Programming is as a tool for research, data managing and inference making and for communicating results.

Developing data products is the final course in the 9-part data science specialization offered by John Hopkins on Coursera.

Unlike previous courses in the data specialization, this course is not taught by a single professor: each of the 3 professors involved in the data science specialization leads a few lectures.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Product Data Operations Coordinator $57k

EGames Product & Data Specialist $66k

Product and Data Specialist $66k

Product & Data Coordinator $71k

Data Conversion Product Manager $80k

Product Data Engineer $92k

Product Manager - Data Management $96k

Data Product Developer $104k

Data Center Product Specialist $113k

Data Services Product Manager $119k

Data Quality Product Specialist $138k

Product Manager - Data Infrastructure $187k

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Rating 4.4 based on 345 ratings
Length 5 weeks
Starts Oct 5 (3 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brian Caffo, PhD, Jeff Leek, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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