What she said:
Now I want to keep doing the same things over and over again but maybe in a different key sometimes or with different backup singers or in a different arrangement. I want Nietzschean eternal recurrence except the intra-life version. I’m happy with what I have and if I can redo it again and again, into eternity, I will be satisfied. I am satisfied, even if it is February.
Despite the difficulties I’ve faced since I wrote those words in 2007, I still feel that way–happy and satisfied with the opportunities and challenges life has presented to me; wanting more of the same (but in variation) as long as I can keep spiraling and evolving. I want more time with my family and friends. More chances to engage with smart people and good ideas at work. More laughter and joy. More heartbreak? Sure, that too, because it means I’m still alive and connecting.
I like to think that progress in life happens in a spiral – we return again and again to the same places and lessons, going deeper each time as we evolve into our best and most whole selves.
I cherish the spiral of my life, as long as I find more meaning and human connection each time I come back around to what sometimes seems like the same exact place I was five or ten or twenty years ago.
This afternoon I’ve been browsing the Wayback Machine looking at past blogging I’ve done (such as at The Barely Attentive Mother and Anne 2.1), thinking as I am of dedicating myself in 2015 to renewed blogging and a whole lot more connecting than I’ve done recently. The past five or so years I was focused first on getting my PhD then recovering from the divorce that may have been related in some complex way to my pursuit of the PhD at the same time that I was launching a career in data science. Between those three activities I had little energy and inspiration left to consider any but the most mundane concerns. I was working working working all the time. At least when I wasn’t crying.
So I’ve been busy, not bursty for the past five years. It’s been a whole lot of perspiration not inspiration. But I’m feeling inspired and excited – ready to make connections again – with great people and great ideas – with great people who have great ideas.
In addition to returning to regular writing online other things I’m spiraling back to are these: mountain adventure (skiing, snowboarding, hiking, backpacking), music (playing both guitar and piano, plus helping my middle child find her own musical muse), leading a team at work (have just put together the processes and people I need to accelerate IQN’s data-driven innovation efforts in 2015), plenty of dating of the casual and serious varieties, and Latin American travel (planning a Christmas trip to South America for next year).
I haven’t decided exactly what my blogging and connecting in 2015 will look like but I’m excited to get going, excited to evolve and grow and spiral some more.
Data science is not just overhyped marketing BS, at least not if you are doing it right.
Owning up to the title of data scientist [Sean McClure | Data Science Central]:
To own up to the title of data scientist means practitioners, vendors and organizations must be held accountable to using the term science, just as is expected from every other scientific discipline. What makes science such a powerful approach to discovery and prediction is the fact that its definition is fully independent of human concerns. Yes, we apply science to the areas we are interested in, and are not immune to bias and even falsification of results. But these deviations of the practice do not survive the scientific approach. They are weeded out by the self-consistent and testable mechanisms that underly the scientific method. There is a natural momentum to science that self-corrects and its ability to do this is fully understandable because what survives is the truth. The truth, whether inline with our wishes or not, is simply the way the world works.
Opinions, tools of the trade, programing languages and ‘best’ practices come and go, but what alway survives is the underlying truth that governs how complex systems operate. That ‘thing’ that does work in real world settings. That concept that does explain the behavior with enough predictive accuracy to solve challenges and help organizations compete. This requires discovery; not engineered systems, business acumen, or vendor software. Those toolsets and approaches are only as powerful as the science that drives their execution and provides them their modeled behavior. It is not a product that defines data science, but an intangible ability to conduct quality research that turns raw resources into usable technology.
Why are we doing this? To make our software better – to help it learn about the world and then, based on that learning, improve business outcomes:
The software of tomorrow isn’t programming ‘simple’ logic into machines to produce some automated output. It is using probabilistic approaches and numerical and statistical methods to ‘learn’ the behavior and act accordingly. The software of tomorrow is aware of the market in which it operates and takes actions that are inline with the models sitting under its hood; models that have been built from intense research on some underlying phenomenon that the software interacts with. Science is now being called upon to be a directly-involved piece of real-world products and for that reason, like never before in history, the demand for ushering in science to help enterprise compete is exploding.
Any time someone equates data science with storytelling I get worked up. Science is not storytelling and neither is data science. There is science to figuring out how the world works and how to make things better based on knowing how it works.
RelateIQ and Salesforce: It’s not just about data science | VentureBeat | Big Data | by Andy Byrne, Clari
5 things I wish I knew about Tableau when I started – The Information Lab
We All Hate Each Other, and That’s Good for the Industry | The Staffing Stream
One staffing buyer comes to mind when I think about harnessing the competitive spirit of the supplier community. This buyer discloses the ranking and number of placements for each of the top 15 vendors in the program each month on an all-supplier call, and those stats are also provided via email following the call to the vendors and internal stakeholders. This not only brings transparency, builds credibility and creates trust in the program, but it also generates a level of focus and priority to that client because of the open competition it creates.
Marginally Interesting: What is Scalable Machine Learning?
I’ve just scratched the surface of this, but I hope you got the idea that scalability can mean quite different things. In Big Data (meaning the infrastructure side of it) what you want to compute is pretty well defined, for example some kind of aggregate over your data set, so you’re left with the question of how to parallelize that computation well. In machine learning, you have much more freedom because data is noisy and there’s always some freedom in how you model your data, so you can often get away with computing some variation of what you originally wanted to do and still perform well. Often, this allows you to speed up your computations significantly by decoupling computations. Parallelization is important, too, but alone it won’t get you very far.
Why does data need to have sex? – High Scalability –
Sex is nature’s way of bringing different data sets together, that is our genome, and creating something new that has a chance to survive.