This Week in Data Science (May 16, 2017)

Here’s this week’s news in Data Science and Big Data.

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Interesting Data Science Articles and News

• General Tips for Web Scraping with Python – Tips on scrapping and saving data from the web.
• Top 10 Skills in Data Science – The results of a study on the skills possessed by Data Science.
• Data Mining for Social Intelligence – Opinion Data as a Monetizable Resource – A look at how opinion data is quickly becoming a monetary resource.
• Sparking change: How analytics is helping global communities improve water security – How Water Mission turned to IBM to use analytics to improve access to safe water.
• How to go about interpreting regression coefficients – A brief look at coefficients and how to interpret them.
• Three Mistakes that set Data Scientists up for Failure – Mistakes that Data Scientists may make in their line of work and how to avoid them.
• Analytics and the cloud: The rise of open source – Open Source and IBM’s involvement in Open Source software.
• Top 15 Python Libraries for Data Science in 2017 – A look at 15 of the most popular Python Data Science libraries.
• IBM updates PowerAI to make deep learning more accessible – How IBM updates to PowerAI will make it easier for Data Scientists and developers to integrate and deploy models.
• Big Data for Humans: The Importance of Data Visualization – The importance of the most crucial and oft overlooked step in Analytics:
  Data Visualization.
• Top 3 ways to measure the success of your analytics investment – Three factors to consider when evaluating technologies that aid in business decisions.
• Pretty histograms with ggplot2 – Learn to create visually stimulating histograms by example with ggplot2 for R.
• IBM pushes for NVMe adoption to boost storage speeds – Why the adoption of NVMe is necessary for today’s vast amounts of data.
• 
  In case you missed it: April 2017 roundup – A look back at all the stories from Revolutions R blog.
• Machine Learning Pipelines for R – How the R package pipeliner helps to streamline the process of building machine learning and statistical models.
• Machine Learning. Linear Regression Full Example (Boston Housing). – Short tutorial on performing linear regression on a data set.

 

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