Monthly Archives: September 2016

Personal Flow and Plan Carving as Water – pt 2

We’ve established the reasoning and basic premise of ‘plan carving as water’, but how does it work? I think one of the best resources on this thinking is cartoonist Scott Adams (Dilbert – http://blog.dilbert.com/) who raves about the benefits of thinking in systems rather than goals(http://blog.dilbert.com/post/102964992706/goals-vs-systems), but I still have a tendency to think of goals. I like them, but appreciate their complete meaningless-ness.

For example, I have a near OCD-level habit of checking and updating my personal budget. I don’t strictly stick to it, but I’m constantly updating it and leveraging it to find ways to move finances, shift goals, better plan for myself, and think of things in different ways. Having this really benefits me since I’ve started as I’ve become more aware of my ridiculous spending habits and understand where my savings are actually used the most effectively; I’ve managed to crawl out of a debt that metaphorically crippled me into a literal depression, afforded a new (relatively) expensive apartment at a good price, started a savings plan, started a second master’s program and am paying it all with cash, bought a car and figured out the most effective way to pay for it and what I needed from it in return, and many, many, many more examples. A budget wasn’t necessary for me to do any of things, but I was comforted with its insights.

Yet I agree with Scott and recommend his book. Despite it’s simplicity it’s a fun read and you may learn something from it–if you don’t then you at least have an extra book to fill your shelf, one you’ve read and not let gather dust. The system works, the goals rarely ever do. The problem with goals is that they are simply targets, or ideal pictures of a landscape you hope to find; when the supports move and the targets change, the landscape is unreachable and your goal is unwinnable.

In terms of water: you’re in a stream flowing to the ocean with the goal being the image you have of ocean itself. When there’s a shift in the landscape, say a riverbed or an unexpected dam, that image of the ocean must change to remain realistic. The reason it must change is that the landscape itself has changed. A dam would give the entry way a much lower point of entry or a riverbed would case the entryway to be much faster in speed. It’s a slight shift in the picture but it’s still a shift. Planning is difficult and time only makes it more so. The longer the time between plan and fruition the harder it is to predict the result.

Plans must move like water and adjust and tweak itself to perfection. Broadly speaking: time is the flow of water and you only get to where you’re going by following where it moves. Unless it pushes you further away, in which case you need to put your foot down and go against the tide. Part 3 coming soon.

Farewell Margaret Martin, Rest in Peace

Rest in Peace
You will be loved,
Sorely missed,
And forever remembered

Rest in Peace
A wonderful woman,
Incredible mother,
Incredible grandmother,
And a true friend

Rest in Peace
Rest in Peace
Rest in Peace

My heart goes out to you, and I truly cannot say I have ever or will ever meet a better person in my life. A woman who never said a bad thing in her life. She more than anyone deserved and will be rightly remembered as everything anyone could hope to be.

Personal Flow and Plan Carving as Water – pt 1

Sometimes I feel stupid for writing on a blog. Sometimes I feel like what I write is interesting and (not very often) important–if not important to other people. It could be for one of the same reasons that I don’t post many pictures of myself (except the one on the landing page): embarrassment.

I’m not embarrassed about myself: I’m awesome, I know I’m awesome, people think I’m awesome, and that makes me even more awesome. No, I’m embarrassed by the fear of embarrassment. If I don’t put myself out there no one else will. At the same time, luck is created and earned; it’s not just for the lucky–you have to put yourself out there and take a risk in order to see the risk pay off. Risks don’t pay off if there is no risk.

The result after luck is a mystery. Because as we plan, it’s easy to set goals and say "I was this to happen by this time, but then luck (or unluck) comes in and says: "Well it turned out that something else happened before". Luck (or unluck) comes in and changes things for better or for worse! And while the results can sometimes be either: a) you achieved your goal/plan, or b) you didn’t achieve your goal/plan, more often it’s: a) things changed and your goal/plan needs to be adjusted to correct for this change, or b) things didn’t really change much but there is new information so you should re-evaluate your goal/plan.

And that’s just the thing, isn’t it? You have an objective (your goal/plan) and you know how you want to get there (with the information you have now), but then luck (or unluck) steps in and things change so you receive new information and need to change your goal/plan. Planning and forecasting is all about finding and adjusting for the risks ahead of time, and that’s why it’s important to take everything like water: adjusting to the new flow and stepping in when you’re against the tide. Part 2 will take this a next step, and I’ll try to include some images which support my thinking. Sounds like yet another "can’t-miss" blog post!

Weekly Reading Roundup – 9/22/16

To celebrate post-mid-September (a made up thing I just made up), here’s a week’s worth of reading, with a lot of stuff around Elon Musk coming through:

idalab – 7 Ways How Data Science Fuels the FinTech Revolution: http://idalab.de/blog/data-science/fintech-revolution

Mashable – Elon Musk says the future of AI is in linking it to our brains: http://mashable.com/2016/09/17/elon-musk-ai-brain-interface (not very new in terms of future concept, but this is apparently becoming more mainstream)

Washington Post – The battle between Tesla and your neighborhood car dealership: https://www.washingtonpost.com/business/economy/the-battle-between-tesla-and-your-neighborhood-car-dealership/2016/09/09/55fb1878-6864-11e6-99bf-f0cf3a6449a6_story.html

Y-Combinator – How to Build the Future Series: Elon Musk Interview: http://www.ycombinator.com/future/elon/

NY Times – What San Francisco Says About America: http://www.nytimes.com/2016/09/18/opinion/sunday/what-san-francisco-says-about-america.html?_r=0 (honestly, didn’t understand the point of this article. I mean, there was a point yeah… but there is no Walmart in or around the San Francisco Bay Area so the writer was obviously talking about a San Francisco in another world. Or simply used the city as click-bait. Like I’ve been saying though, the United States is not a first-world country. There’s no way it can be.)

R-Bloggers/Flavio Azevedo – Learning Statistics on YouTube: https://www.r-bloggers.com/learning-statistics-on-youtube/ or http://flavioazevedo.com/stats-and-r-blog/2016/9/13/learning-r-on-youtube (better format IMO)

yhat – Analyzing the Conditions for Studying Stars: http://blog.yhat.com/posts/astronomical-conditions.html

Harvard Business Review – Price Gouging and the Dangerous New Breed of Pharma Companies: https://hbr.org/2016/07/price-gouging-and-the-dangerous-new-breed-of-pharma-companies

Bloomberg – Amazon Eats the Department Store: https://www.bloomberg.com/gadfly/articles/2016-09-20/amazon-clothing-sales-could-soon-top-macy-s

Bloomberg – Blame Headhunters for Increasing Wage Gap: http://www.bloomberg.com/news/articles/2016-08-28/blame-headhunters-for-increasing-wage-gap

Stratechery – Oracle’s Cloudy Future: https://stratechery.com/2016/oracles-cloudy-future/ (extremely interesting, essentially gives the history of Oracle and database structures in the process!)

Corporate Technologies (via OpenDataScience) – Next Generation Analytics: The Collision of Classic and Big Data Analytics: https://www.opendatascience.com/blog/next-generation-analytics-the-collision-of-classic-big-data-analytics

Data Science 101 – Data Science and the Perfect Team: http://101.datascience.community/2016/09/22/data-science-and-the-perfect-team/

KDNuggets – Data Science Basics: 3 Insights for Beginners: http://www.kdnuggets.com/2016/09/data-science-basics-3-insights-beginners.html

Email to My Brother and Sister on the Future

Sent this email to my brother and sister a few weeks ago about the future. We were discussing income inequality, what the future will bring, and other random thoughts. The conversation came up after I forwarded her this article from Time previewing a book I want to read called The Weapons of Math Destruction by Cathy O’Neil( http://time.com/4471451/cathy-oneil-math-destruction/):

"The way our system works is capitalism, where there are winners and losers. If we want to continue to evolve and continue to be ‘fair’, the system we live in will need to change in line with our technology. Guaranteed income is one idea of doing this (everyone earns $XX.XX a year, regardless of their ‘job’) but as our technology evolves, we need to think of our place (as a human society) in this world. We either develop the technology (software, hardware, space exploration and development), assist in the planning of the future (urban development, electronic banking, urban farming, driverless cars, power generation), or assist in the cleanup (waste disposal, water cleaning, resource management, elderly care, death disposal, species preservation and reduction, earth management). There are jobs to be had, just different ones from the ones we currently have. The poor will need to step up, or be left behind, and that’s the only option."

I still stand by this. I also wrote this having just finished reading the second book of the Red Rising trilogy (Golden Son, https://www.amazon.com/Golden-Son-Book-Rising-Trilogy/dp/0345539834)–about a space based future chock-full of class society prose–and was finishing up the second book of the Rememberance of Earth’s Past trilogy (Death’s End, https://www.amazon.com/Deaths-End-Remembrance-Earths-Past/dp/0765377101)–I won’t tell you what it’s about but it’s absolutely my favorite book, ever, period. So a lot of my thinking involved space (which is undoubtedly the long-term future) and technological advancements. Elon Musk and Jeff Bezos, beware. I see what you’re doing, and I’m coming for you. (As a friend, or a foe?)

Data In, Who Know What Out

There’s no shortage of news pieces from prominent media outlets reporting on false information. Either the articles/pieces themselves are completely made up, the data they use is skewed and unreported, or the data is just totally made up completely. Well, how do we really know what is true and what is made up? There’s probably a model in psychology and/or economics based on this pointing out how circularly destructive false information is on the reported and the reporter (and outlet that reported); not to mention how social media exacerbates false information!

I’ve read an interesting use case scenario written by data.world promoting their data set hosting platform (http://www.kdnuggets.com/2016/09/behind-dream-data-work.html). The case itself was decent, BUT it reminded me of the saying: "Garbage In, Garbage Out", wherein the quality of findings is only as good as the quality of the underlying data. Makes sense. But it really made me think about how the data we use is actually collected.

I appreciate websites like data.world; I hope they continue really because practicing data analysis with data is important especially for beginners. One specific feature on data.world is interesting: verified sources, where the source of a data set (the person that posted your brand new .csv file full of information) shows up as verified if they are a verified member (i.e. Professor at the University of Washington, or Researcher at the Center of Disease Control). I guess we can think about it like Twitter’s verified user tag, but useful. These users also document the data collection methodology, engage in helpful conversation, and answer questions other users may have on its collection. I just hope that other practitioners don’t rely on this information to make decisions.

Prediction alert: I predict that other users will rely on the information to make decisions. And furthermore, these users will increasingly be making the wrong decisions based on poor data.

This isn’t surprising. Unless you capture the data yourself, how can you really know if it’s any good? The Catch-22 is that there’s no possible way to capture all of the data yourself and so you rely on third-party information to make decisions. See where this cycle gets out of hand, creating an exponential number of problems?

Perhaps going forward we should all just use the disclaimer: "This report/decision was based on the findings that was created for this specific set of data, and may not apply with new sets of data" with requisite links and sources.

Personally, I prefer to think in broad strokes. If the data (generally) matches the consensus (generally), then the result/report/decision should be made in a general sense. Disclaimers would help here, and they are very important.

Weekly Reading Roundup – 9/16/16

Here’s a week’s worth of reading:

Kaggle Blog – Building a Team From the Inside Out: Alok Gupta on the Evolution of Data Science at Airbnb http://blog.kaggle.com/2016/09/06/building-a-team-from-the-inside-out-alok-gupta-on-the-evolution-of-data-science-at-airbnb

Stratechery – Facebook vs the Media https://stratechery.com/2016/facebook-versus-the-media/

Standford Engineering – How will driverless cars and other applications of AI affect society? https://engineering.stanford.edu/news/how-will-driverless-cars-and-other-applications-ai-affect-society

Stitch – The State of Data Engineering https://www.stitchdata.com/resources/reports/the-state-of-data-engineering/

NY Times – How to Become a C.E.O.? The Quickest Path is a Winding One http://www.nytimes.com/2016/09/11/upshot/how-to-become-a-ceo-the-quickest-path-is-a-winding-one.html

Stitch – We’re in the Middle of a Data Engineering Talent Shortage https://blog.stitchdata.com/new-research-were-in-the-middle-of-a-data-engineering-talent-shortage-bdd59673608c#.7tnk3wec5