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Laxtonto

Burnt Ends
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Posts posted by Laxtonto

  1. 4 minutes ago, jebusshuttlecock said:

    rumor on the ohio state reddit is that meyer is stepping down at his press conference this afternoon

    Now that would be a shock to me...

    To me he may get canned over this, but to make the decision this quickly would amaze me

  2. 13 minutes ago, BabaYaga said:

    This is why the left has a case of the vapors.  It's got nothing to do with labor  - check out the top contributors to the DNC

    Top Organization Contributors

    I have little doubt the media will spin this as a huge loss of revenue to the unions and democrats without saying that the revenue comes as an expense to the taxpayer.  Private sector unions are an anachronism who's main skill is inflating the cost of doing business without any regard to market forces or the economics of their industry.

    3 Unions in the top 10, all with more than 90+% of their donations (approximately 35M in total) to Democrats/Liberals causes.

    To me, th is an extremely telling list and makes me curious who determines in these organizations which donations are made.

    If we were expecting this to actually reflect the population, wouldn't these values be closer to 60/40 or 50/50 instead of so heavily one sided?

    The other thing that stands out to me is that in the top 10 only one is hovering around 50/50.

    Yeah... I'm a data whore. This is an absolutely fascinating list to dig into. Thanks for sharing this.

     

  3. Fun game. A little buggy at launch, but for the most part. Finished the campaign and will wait on a second play through until they release of the free quality of life updates references in their most recent AMA on reddit.

     

    If you are an old school BT player, this will scratch the itch a bit, but I would probably wait a few months if you have not purchased it yet until the modding community and HBS provides a few tweaks. 

  4. It is OK to scrape in the US

    For those of you who don’t know, Amazon (and multiple other online retailers) had made it against their TOS to scrape customers comments in an attempt to control the availability of that type of unstructured data. Thus allowing themselves to provide services to those who use their site as a third party retail solution. This also meant that by doing so it keeps academia from using them to find the same solutions and publishing their findings in the open public domain.

     

    This is a major boon to consumer behavior research and I am very thankful that this ruling came to pass. 

  5. Went from DFW to small town South Dakotka. Even if this is a "college town" I just can't do small town living anymore. We are moving an hour away for  more/greater population density, just for my wife and I 's sanity.

    And no, this is not the city boy not being able to stand the fragrances of nature. 

    I grew up in a town of 400, but damn I can't take the nosey bullshit any longer.

     

     

     

  6. 22 hours ago, futureman said:

    first of all, to understand what happened to shaggy, you gotta understand who shaggybevo was.  now shaggy was born to a three-legged bitch of a mother, and he was always ashamed of this, man.  and then right after that he's adopted by this man, blacklab.  he's a small-time gun-runner and a fight promoter.  so he puts shaggy into training.  they see shaggy’s good.  he is damn good.  but then he had the fight of his life.  they pit him against his old friend kevin morgan.  and shaggy said "no man that's my brother, I can't fight kevin" but blacklab made him fight anyway, and shaggy, he killed kevin.  shaggy said "that's it!" and he called off all his fights, and he started doing crack, and he freaked out.  then in a rage, he collapsed, and his heart no longer beat. wow.

    Well your screen name checks out, so I guess that is how it ends then 

  7. 22 hours ago, futureman said:

    first of all, to understand what happened to shaggy, you gotta understand who shaggybevo was.  now shaggy was born to a three-legged bitch of a mother, and he was always ashamed of this, man.  and then right after that he's adopted by this man, blacklab.  he's a small-time gun-runner and a fight promoter.  so he puts shaggy into training.  they see shaggy’s good.  he is damn good.  but then he had the fight of his life.  they pit him against his old friend kevin morgan.  and shaggy said "no man that's my brother, I can't fight kevin" but blacklab made him fight anyway, and shaggy, he killed kevin.  shaggy said "that's it!" and he called off all his fights, and he started doing crack, and he freaked out.  then in a rage, he collapsed, and his heart no longer beat. wow.

    Well your screen name checks out, so I guess that is how it ends then 

  8. Needless to say, the thread title should be self explanatory.

    After reading the CR thread regarding Facebook and Cambridge Analytics it has occurred to me that maybe this type of thread is needed on the Shag somewhere.

    Lets start with the basics. The world is constantly generating structured and unstructured data. We are just now beginning to create both the technology and the techniques to really start using this voluminous amount of data to find real actionable information.  

    This goes by many names. It has been called Management Science, or Decisions Support Systems, or Business Intelligence, or Data Analytics or Data Mining or Big Data or [Insert buzzword here]. 

    This is an area I have been working and researching in for a while now, and I would be happy to discuss what I can. I am sure there are others on the Shag that can fill in the spots where I lack expertise. 

    My area of specialty is in unstructured data, text to be more precise, and how that can then be utilized in predictive and prescriptive modeling. This means I play with everything from twitter, amazon reviews, to structured psychology interviews, to journal abstracts, to SEC fillings, as long as there are meaningful text involved, I'm there.

     

     

    And 

    This is actually an extremely loaded question, and probably not for the reasons you think. 

    First I will provide a classic baseline and from there I will provide you my own personal answer.

    The analytics discussion has fragmented into really 3 separate areas (and more than likely 4 if things continue at current pace). Visualizations, so descriptive statistics applications, simple dashboards and the like is the first group. This is the level of analytics most likely "seem" by the large majority of the business environment. This is many ways is also the hardest one to teach. Not because the skills are necessarily complex, but the visualization (and the implied ability to present the information in a functional manner at a level that the entire audience really understands) process requires both an intuitive grasp of the "problem" as well as a true understanding of both the strengths and limits of the data. 

    From there you have pure predictive. This is exactly what it sounds like. Everything from simple regression and heuristic driven applications to much more complicated concepts like Neural Networks. The biggest issue here is that many people fail the basic issue in all statistics, that the data drives the method and analysis, not the other way. Depending on the data characteristics and the questions you are asking a wide variety of techniques are available. Never forget that prediction is just that, attempting to predict the future based upon some set of data and therefore is subject to both the assumptions of the modeling technique used and the error apparent with the data itself.

    Finally is prescriptive. This is taking the raw data and even sometimes the predictive models you have already been built, and then use those in a way provide a what if level of interpretations. What f I change this input, what does it do. What if we change the production level to this, what does it flow into? This sounds extremely simple, and in some case it can be. Simple techniques like basic Linear and Nonlinear Optimization techniques can be used to provide best case scenarios and answers for when you change a limited number of input parameters or constraints. Then you have a wide variety of simulation techniques, which are much more interesting and complicated. This allows much crazier applications like changing the underlying distributions of input data, or manipulating full scale plant layouts or changing a wide variety of parameters and constraints simultaneously and then running those probabilistic models or "simulations" a multitude of times to determine the new distribution of potential results and best case worst scenarios and even simple expected values . This is one of the more complicated areas to work in because it requires an amazing grasp of what ever process you are simulating, a intuitive grasp of what levers you can manipulate and a very detailed intensive approach to not miss anything that is integral to the process. 

    There is now a new "pillar" or area of analytics that is becoming an expected standalone category. This is data preparation, cleaning, manipulation, storage/acquisition. This is by far the most labor intensive, tedious, frustrating part of analytics. As we have more people in analytics, there are now people that specialize in this instead of having everyone do their own. The birth of economies of scale in analytics I guess. 

    Now my personal view is the same answer I have always had, analytics does not really exist. It is just the extension of what we in management science have been doing since the 1940s, just the computing power and scale of the data has changed. It is just another consulting word, just like Business intelligence, or DSS or machine learning or AI. It really doesn't describe anything, it just tries to encapsulate a series of techniques under a new umbrella. 

    This is actually driven at the academic levels over who will control "analytics" and who gets the resources tied to the program. If you learn analytics at a computer science or data science driven institution, it is going to be very method heavy and lack context. If you learn in a IS school, it is going to be data manipulation, data storage and the like. If you get it in a business school it will be outcome based tied to problems with context. The problem with this approach is that all require a large investment time and skills and so each emphasis comes at the cost of something else.

    My personal philosophy is to teach the business approach and the logic skills and allow the students to spin up their techniques on their own. Evey industry is different, so if you understand the concepts, you can then figure out what you need to do or what additional skills you need to acquire to solve the problem. If you lack the business problem skills, the rest is really meaningless.

    If you know analytics and can explain your answers at both the technical side and to the layman, I can always get you a job. Analytics is an area that seems to be treated as a special subset in many companies so you end up with more interaction with senior management teams than most traditional new employees. If you cant figure out what they actually want done, then what data they have and then a solution that you can explain that provides them a answer, you are useless in their eyes. The only way that happens is that you must also learn the problem definition and presentation skills to go with the analytic techniques.
     
     

     

    • Like 2
  9. 12 hours ago, tjhooker said:

    He has 2900 posts and I've never seen him before. He hates the old shaggy and thinks he's the guy to clean it up. He's for real. 

     

    It is almost like everyone that still has a log on there needs to go back to shag, log on, neg him till he is crowd sourced banned as a parting gift and then log off forever again.

    • Like 2
  10. This is one that fits none of the profiles..

    Pissed off Vegan that had issues with Youtube decides the best way to deal with the issue is shooting up their campus and then killing herself..

    Just wow. That is just bizarre

  11. Quote

    Another eyewitness told KTVU that a second victim, another woman, was shot “10 times” and was almost certainly dead. Then there were four shots, and then nothing. He says the shooting began in the courtyard of the YouTube building where there was a party going on.

    (As a caveat, the man refused to give his name to reporters, saying, “Everybody knows me.”)

    This MO screens exlover or workplace problem and everyone just got in the way.

    10 shot from a handgun is overkill and generally implies that the this was the intended target.

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