This Week in Data Science (May 16, 2017)
Posted on May 16, 2017 by Janice Darling
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:
- 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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- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.
- Predictive Modeling Fundamentals I – Take this free course and learn the different mathematical algorithms used to detect patterns hidden in data.
- Using R with Databases – Learn how to unleash the power of R when working with relational databases in our newest free course.
- Deep Learning with TensorFlow – Take this free TensorFlow course and learn how to use Google’s library to apply deep learning to different data types in order to solve real world problems.