Most Influential Tableau-Related Blog Posts For Tableau Blogging Month, I wanted to focus on the back-catalogue of amazing blog posts written, perhaps, before you'd heard of Tableau. You should go get a cup of tea, sit down, and read all of these posts. If these posts are new to you, these are seminal posts that will inspire your Tableau journey. And maybe they will inspire you to start up your own blog? How long have there been Tableau blogs? At Tableau HQ, we’re not sure when the first ever Tableau-related blog post was. Since then, which posts have been the most influential? I gathered this list by asking Tableau Zen Masters, and a bunch of Tableau employees for contributions. This list is ordered by publish date, oldest to newest. The Ring of Fire… by Robert Morton, June 2009 Tableau’s Community Manager, Dustin Smith, says, “It was the first time it dawned on me that data was literally lying all around us and that all I had to do was point Tableau at it and the story within would come tumbling out.” “Charts inside a tooltip?
Bio & Health Tech SBIR Impact NYC Small Business Innovation Research Through the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs, 11 federal agencies award over $2.3 billion in non-dilutive grants and contracts annually. These programs provide critical early-stage funding for R&D in a small business setting. What is SBIR Impact NYC? An initiative of NYCEDC and the City of New York, SBIR Impact NYC is designed to enhance the competitiveness of SBIR/STTR proposals among NYC-based life sciences and healthcare technology companies. Introductory workshops and webinars open to the public.20 hrs. of one-on-one assistance for each of 20 select companies actively preparing SBIR/STTR proposals. Overview of Application Process: Interested participants should submit an online application by one of three deadlines: January 10, April 11 and Sept 3. Learn more at www.sbirnyc.com
VirtuHall Concept - Littérature et Infographie 3D - Accueil Rank of Rank {This is the fourth blog post in the #TableauStatsMonth series, and is a guest post by Tableau Zen Master Jonathan Drummey. Jonathan gives us a basic introduction to Rank, and then dives into a special use case of sorting a list based on average rank across a number of categories.} I’ve got several favorite new features in Tableau version 8.1: performance improvements, full-screen presentation mode, R integration, and the new built-in rank functions. This post will cover the new rank functions, some ways they can be used and differ from existing functions, and finish up with how they’ve let me replace a whole bunch of SQL, a temporary table, and several different worksheets with a single one-line calculation, and maybe help save some feet, too. The (blinded) data I’ll be using is based on a real-world use case from the integrated healthcare system I work for. Here are some sample rows: And the download the data in .xls And here’s the view: [Moment of Silence] Ok, now onto more ranking fun!
Tracking the Internet of Things: Postscapes The Data Science Equation I present here the results of a data science study about data science. Based on LinkedIn data (top people listed when you do a people search for data science, from a LinkedIn account with 8,000+ data science connections), we identified the fields most frequently associated with data science, as well as top data scientists on LinkedIn. The statistical validity of data science related fields is strong, while validity is weak for top data scientists. The reason being that you need to have at least 10 endorsements for your LinkedIn data science in the skills section, to be listed as a top data scientist in the following list. Skill Set Found on Profile of Most Popular Data Scientist on LinkedIn The following lists were created by searching for data scientists with 10+ data science skill endorsements on LinkedIn (see above image as an illustration), and analyzing the top 5 skills that they list, as in the above picture. In short, you could write the Data science formula Parameters in the formula
Taxonomy of Data Scientists This is a first attempt at classifying data scientists. I invite you to produce a more comprehensive, better solution. The 10 pioneering data scientists listed here were identified as top data scientists in our previous article entitled data science equation, based on their LinkedIn profile. Here we computed, for each pioneer, the number of endorsements for each of the top 4 data science related skills: analytics, big data, data mining and machine learning; these skills were identified in our previous article as most strongly linked to data science. Note that the correlation between machine learning and analytics is very negative (-0.82). Notes: (1) Kirk Borne skill sets is highly fragmented. Big Data (x-axis) / Machine Learning (y-axis) scatter-plot The big data / machine learning combo exhibits the strongest cluster structure among the 6 potential scatter-plots. Comments Is this a "big data" analysis? Who is the purest data scientist? Related article The data science equation
Six categories of Data Scientists We are now at 9 categories after a few updates. Just like there are a few categories of statisticians (biostatisticians, statisticians, econometricians, operations research specialists, actuaries) or business analysts (marketing-oriented, product-oriented, finance-oriented, etc.) we have different categories of data scientists. First, many data scientists have a job title different from data scientist, mine for instance is co-founder. Check the "related articles" section below to discover 400 potential job titles for data scientists. Categories of data scientists Those strong in statistics: they sometimes develop new statistical theories for big data, that even traditional statisticians are not aware of. Most of them are familiar or expert in big data. There are other ways to categorize data scientists, see for instance our article on Taxonomy of data scientists. Implications for other IT professionals Are data scientists a threat to your job/career? Related articles
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Millennials, Civic Engagement and Civic Tech This post is written by Tamir Novotny, Senior Policy Associate at Living Cities, and is cross-posted from livingcities.org/blog. We’re (Living Cities) working with the City of Louisville, Ken. to develop technology to engage low-income young adults in the city’s long-term planning. Here’s what we’re learning. We recently kicked off in earnest a project in Louisville to develop a piece of technology aimed at engaging low-income Millennials (young adults ages 18-30) in city planning processes. We came into this process with a few questions in our minds: Who exactly are Millennials and what does it take to engage them in civic process? Here are some things we are learning. Millennials and Traditional Civic Participation: Out of syncThe relationship between Millennials, technology and civic participation is not as straightforward as one might think The verdict on Millennials (a group to which I belong) is mixed. Questions?