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The emerging age of "value" based medicine has outpaced important discussions on metrics and tools to examine value outside of profit motives and limited variables. The buzz-worthiness of terms like big data and value might leave marketing professionals frothing at the bit--the rest of us are or should be moving forward with business as usual. There is no such thing as big data. Large unstructured datasets didn't simply arrive at the beckon of marketers. And don't get me started on value. Value for whom? If we are indeed in the age of "personalized" anything--why don't we start with refining these general terms to engender meaning and the appropriate granularity. 
I originally wrote the bulk of this article on LinkedIn. I have no idea what drives their algorithm but I will share that where other platforms (including this one) yield hundreds of eyeballs in a single day--their metrics show that from thousands of followers on LI only 7 bothered to read it. Maybe it is a dog I don't know. What I do know is that it is packed with resources you might find interesting and useful.
If I am wrong...woof, woof.
I probably receive anywhere from 20 to 40 messages every week or so from individuals with questions about data literacy, specific data packages, or data visualization with Tableau. There are a lot of questions that I hear that I never thought to ask. I never asked for permission. Many people ask should I learn Python or R?
What is the best book to read to learn Tableau? These are personal and specific to individual interests and deserving of exploration. Mainly because like anything, your mileage may vary. And then, there is the tired question about what is the difference between a data analyst, data scientist, data engineer, statistician, blah, blah, blah...I will leave you to explore this well worn debate elsewhere. There also is a bit of a territorial debate where member of these distinct groups poke fun at the other less worthy participants. Here is what I learned. The brightest and best want to bring you along. They encourage, share resources, and are welcoming.
A recent podcast featuring Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review stopped me in my tracks. A year ago I was reading through his work and wrote a few blogs about his ideas. Curious about a pending part two to the discussion, Statistical Paradises and Paradoxes in Big Data(I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election, I sent him an email.
He promptly responded. Apologized about the delay in the Part II, sent me a copy of the article above and several others he thought might be of interest (along with access instructions). Meet him in the podcast below and learn about his role as Editor-In-Chief of The Harvard Data Science Review.
The Mission and Scope of the HDSR is captured by this quote below:
As an open access platform of the Harvard Data Science Initiative, Harvard Data Science Review (HDSR) features foundational thinking, research milestones, educational innovations, and major applications.
Read the tag-line and I dare you not to be excited if you are even slightly data adjacent.
A microscopic, telescopic, and kaleidoscopic view of data science Here is one of the articles referenced in the journal section brilliantly labeled as CORNUCOPIA: impact, innovation, and knowledge transfer. (A)Data in the Life: Authorship Attribution in Lennon-McCartney Songs Apologies for the interactive experience that will likely derail your tasks at hand as you explore the features below. If the link brings you to 3 vizzes, pick number 3 to activate the visualization below. 
I have been taught that items should be bundled in threes so here is one more. I recently read a great article in Medium, Anaconda is bloated--Set up a lean, robust data science environment with Miniconda and Conda-forge by Ted Petrou. Here is how this was a lifesaver. I work with large datasets in healthcare and medicine. The last thing I needed was a clunky giant installation of valuable but likely not needed programs on my MacBook. Thanks to Ted and his willingness to walk me through a few hiccups I am up and running. If you are a Python person and value just-in-time instruction I suggest you check out his workshops (a few free ones are there for a test drive) over on DUNDER DATA.
Although I completed an executive online course from Columbia School of Engineering in Applied Analytics--once I returned to the real world I wanted expertise at a granular level in the packages and libraries that I actually use daily. Ted is so clear and patient and provides the detailed explanations of why things work a certain way and how to customize your experience specific for your data needs.
Want to connect? Follow along on twitter or catch me live at Women in Tech Summit next month or RStudio conference in San Francisco in January!
Later this month I will be presenting at the Tableau Fringe Festival on October 25th! Announcements about the line-up will be out soon... 
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