Here we go and we all know that the buzz words “big data” and “computer scientist” go together today. They may know more than me on this topic by I’m skeptic since I used to program and that’s the next level down that designs the software that goes with the Math Models produced. The article mentions their big clients asking them for help and it just so happens the Zipfian Academy is also going to run a placement agency for Computer Scientists. So when this gets going “don’t like your computer scientist, not getting the job done, no problem we have a small army in train and will supply a new one soon as they graduate”..ok a little satire there but making a point of sorts.
LinkedIn and Facebook are tied up with them and we all know Facebook really wants to dig deep and get some non linear matching's on millions of users as I’m sure they too want to do more than just sell their data as they are missing out on the analytics boat too. What do you think LinkedIn is hanging around for as well.
Banks are also looking for more computer scientists and I don’t know if this 12 week course would be enough there as there’s a huge difference between bullshit social network analytics and the real world of trading software and black box models and algorithms. Just look at the rogue algorithms and algos out of control on the stock markets and common sense will tell you this is not quite that arena.
Nobody expects Facebook data to be totally accurate out there(except those who are duped like HHS and their Facebook contest a while back thinking there’s an app for everything in healthcare ) and we hear from Facebook quite often when something gets out there that shouldn’t “whoops” and so far although Goldman has been able to pull of “whoops” in the markets as we have a lawyer running the SEC and not a technologist and all that would change if the SEC was not running things ass backwards in this respect.
The course will set on back about $14,000 but you get some of that back if the company places you with a job of $4,000 and they will offer deferred payment plans. If you have not programmed before then you would not qualify as you do have to pass a test to exhibit certain skills to enroll. In other words this is not open to the layman that has a dream to become a “sexy computer scientist”. That is so stupid anyway as there’s really nothing sexy about it other than there’s a demand for those who can model and write code to improve efficiencies and make money for a company or bank. Actually at the end of last year I said the name could use a change as it’s really not “science” to where researchers spend years and years and finally land upon a solution. Most experience died in the wool Quants will agree with that, it’s not the same and the turn around time is fast as demanding CEOs want those models immediately to turn a buck. Look at Jamie Dimon when asked about his models he says “I don’t know ”….he does money and not math but the math makes his money.
“Data Scientists”– An Oxymoron? Is Finding the Value in Data Bases Queried Together in the Business World To Make Money Actually Science?
No matter what the “official name” is, computer scientist, Quant, Actuary or whatever it’s still a model building process. I mention this video for a lot of other reasons, mostly to help people understand what is done with models, but there’s a person in the video you should follow in the Quant Documentary, as he’s a guy wanting to become a Quant..and he’s an IT infrastructure guy who puts all the software in place and write that code and wants to move the next level up..he’s paying $100,000 for classes to be a Quant on Wall Street and he talks about his prior IT work as a programmer, sitting in dismal corners while the Quants are out there where all the action takes place. This video is a couple years old but very relative to where the layman can get it. He wans the power to be a quant.
Listen to what Mike Osinski has to say about the banks messing with the software he modeled, “if you are close to the money you want to make more money”..”you become so isolated from the real world too”. Osinski wrote the software that all the banks abused for sub-prime and now he farms oysters. He had a lot of attention a few years back when he said his software brought down Wall Street, but it was not his design, but rather how the banks manipulated and used it. When I wrote a medical records program oh yes did I experience the isolation and why I made it a point to stay in touch with the real world and so many on the social networks get lost in a lot of this because you don’t know. His best quote is “you can do anything with software” but how it plays out in the real world versus a model is a whole different picture sometimes. As you read more about bitcoin in the news, keep that thought on an intangible currency system were to grow real large, it could spin right out of control.
Mike says with this talent you just know “you are better than anyone else”..and that mind set is dangerous as you do have to relate to people on all levels and if you spend too much time isolated without the people interaction, your perception gets skewed, big time. Can’t tell you the times that my perception saw it one way and then when it got out for the doctors to use, a no go and couple times with that I came right to earth. Those who write code without this are at a loss as your perception can run away with you.
After you watch this you too might wonder if 12 weeks is really enough? Watch the guy in class with the #1 quant in the world, Paul Wilmott and the faces, music fits perfectly. So if you are a partner or significant other of a quant, mathematician this will give you an idea why they need some space and extreme brain power modeling takes. Again this documentary makes a nice complement to the course being talked about here. You do forget what day it is when you are that absorbed as well and you have a ton of others breathing down your neck to meet deadlines they promised and you know there’s no way you will be able to do it. I’m guessing the Quants at Allscripts really felt this when the pressure was on to merge with the Eclipsys EHR , they had too as the CEO was out lying his fanny off to share holders.
“Yes indeed our trained quants will be able to use the “pick six” methodology as described by Paul Wilmott” in the documentary, which is no science but can easily bail out even the best computer scientist when they just can’t create a model for it, pick the number 6”…ok second bit of satire here, but again in the video Paul Wilmott tells you this how it gets done sometimes…”no science to that he states”…and not to say students will learn that here but in the real world it happens and those algos come and take your money, again the theme of the entire video is good for anyone to watch. You can hear Wilmott and Derman, the very early day quants and experts talk about the code of ethics as well as apologizing to the world as they wrote some of those that turned sour or where others abused the models. Both are now professors teaching mathematics. In doing this type of work you will need code of ethics as well.
Hiding, Falsifying, And Accelerating Risk Has Become the Achilles Heel of the US Economy As the “Real” World” Clashes With the Values Created From a World of “Fictional Values” Of Formulas and Math
So will these so called “data scientists” be able to draw the line between utility and menace, as Christopher Steiner describes (video at the Algo Duping Page) as we already know it has failed on Wall Street as there are far more “rich” menaces” than there used to be and again is this really science? I still think it’s an oxymoron but you can beg to differ with me if you like.
Last but not least will the people who take on these jobs ever be able to live up to what the general public “thinks” they can do with a horrifically high level of expectation and accuracy…hmmm….I’m chuckling on that one as the present level of Algo Duping and people’s expectations and what is really there is already causing some big awakenings for many:) Data scientists are hybrids one with more than one focus. I’m a hybrid and that’s why you get what you may consider weird posts over at this blog that don’t tell stories but get to the rock bottom as I see both programming and marketing with past experience in both, so this is what the computer scientist is going to do as well, but instead of programming they have to wrestle the models. Here’s a clip from Wall Street Technology on that topic..
So that is one other thing to think about, make sure you are or can become a hybrid and Bill Gates told the graduating class of Berkeley the same thing a few years ago, be a hybrid . Banks sink money into such as that means money for them with formulas and models to run their money making IT infrastructures with investments in particular. Insurance companies, same thing and why it’s a very close line for them and you see reports with insurance carriers exaggerated and it’s the result of computer scientist drumming it up something to do what the boss asks for marketing. United Healthcare is the king of that with some of their studies that spin into space with claiming way too much in the savings departments, but they have shareholders and the CEO says they are number one. (Is he by chance Leona's lost son, grin).
Remember not too long ago the story about Wall Street and the spreadsheets on teaching college graduates to use them…comes back to this as the computer scientists will be creating the spread sheet formats the analysts use They don’t get code at all, and I have had an analyst tell me that. I hear from him when he gets stuck with an answer on stocks once in a while that relate to technology and code. So there’s the other side of the coin make sure those analysts can use spreadsheets based on the models the computer scientists create.
Wall Street and Other Financial Institutions Spending Big Bucks Teaching New College Graduates How to Use Spreadsheets–Is This An Emergence of “Mini Modelers”Without Borders?
Now here’s something else of interest..anyone can model using software as a service and sell your model too and this format required little or no coding so a pitch to the spreadsheet folks.
BigData Modeling and Analytics Goes Into Mass Production Based on SaaS Machine Learning Platform to Create Predictive Models in Minutes–Accountability, Accuracy Still Needed By All Means – “Models Without Borders”
Word to the wise on this as far as financial trading models, be careful as you don’t want your model to the break the exchanges, leave that exposure to the big guys and don’t your head get too big here. You can read around on the web and even big companies have computer scientists at work that have not figured out or hit a target and they meet with management all the time to give them updates and I can see of these meetings creating a little sweat as management doesn’t get the time elements needed sometimes. The code of ethics for computer scientists is something to be aware as as you could have a boss wanting the “pick six” model as it makes money and you say not, and then you are out the door to get another quant in your place that will entertain the pick six as all will stand to gain a lot of money with cheating.
Big Data–The Data Science Code of Ethics-Designed By Those Who Create Models - Don’t Fall Victim To Write Fictitious Code and Models Just to Make Money With Clients Demanding Such
Here’s a little preview of what the computer scientist needs to keep in mind as the world has all kinds of temptations and big money for a model that makes money but lies at the same time. The code of ethics say toss your boss into jail if need be in so many words.
“Rule 8 - Data Science Evidence, Quality of Data and Quality of Evidence
(a) A data scientist shall inform the client of all data science results and material facts known to the data scientist that will enable the client to make informed decisions, whether or not the data science evidence are adverse.
(b) A data scientist shall rate the quality of data and disclose such rating to client to enable client to make informed decisions. The data scientist understands that bad or uncertain data quality may compromise data science professional practice and may communicate a false reality or promote an illusion of understanding. The data scientist shall take reasonable measures to protect the client from relying and making decisions based on bad or uncertain data quality.
(c ) A data scientist shall rate the quality of evidence and disclose such rating to client to enable client to make informed decisions. The data scientist understands that evidence may be weak or strong or uncertain and shall take reasonable measures to protect the client from relying and making decisions based on weak or uncertain evidence.
(d) If a data scientist reasonably believes a client is misusing data science to communicate a false reality or promote an illusion of understanding, the data scientist shall take reasonable remedial measures, including disclosure to the client, and including, if necessary, disclosure to the proper authorities. The data scientist shall take reasonable measures to persuade the client to use data science appropriately.”
And a few more words here….
“(g) A data scientist shall use reasonable diligence when designing, creating and implementing algorithms to avoid harm. The data scientist shall disclose to the client any real, perceived or hidden risks from using the algorithm. After full disclosure, the client is responsible for making the decision to use or not use the algorithm. If a data scientist reasonably believes an algorithm will cause harm, the data scientist shall take reasonable remedial measures, including disclosure to the client, and including, if necessary, disclosure to the proper authorities. The data scientist shall take reasonable measures to persuade the client to use the algorithm appropriately.”
Ok well this a wrap up here on this topic and I hope all just cool their heels a bit on “big data” as yes it is there and it will help us find answers but there’s also the other side. Due to all the activity you read above, this is why I’m right in the same belief as the Australian bankers, half of what is created and sold in analytics will be a waste and telling the difference is hard as models and algorithms get slung to the walls sometimes to see what sticks. Be a skeptic when you need to be if something doesn’t seem right. Its just like other data out there, like credit agencies for one as the more data that gets distributed and sold the more errors possible and same holds true with models and analytics, it gets harder to perform and do the same outstanding results as the field get crowded. BD
Half of Analytics Investments By Companies and Banks Will Be a Waste–What Do We Analyze with Big Data and Does It Have Value–Some Algo Fairies Would Do Better at Disneyland…
What's the best way to become a data scientist? Well, you could earn an advanced degree from an accredited university, a process that may take several years and cost tens of thousands of dollars. Or you could go the express route: A 12-week boot camp that teaches the pragmatic skills needed to land a data science gig at a reputable business.
One such fast-track school is Zipfian Academy , a San Francisco-based facility that this week is welcoming its first class of data science students. Its goal is to teach its students what they need to know to be proficient data scientists in just 12 weeks.
Certainly the Zipfian Academy isn't alone in this field, as a quick Web search readily demonstrates. Major universities are in on the action too. In July, for instance, the University of California at Berkeley launched its Master of Information and Data Science program, which school officials called the nation's first online master's degree program for data scientists.
About 220 applicants vied for just 13 spots in Zipfian's inaugural class, which started its intensive 12-week program this week. "[The selected students] range from various quantitative backgrounds -- business analysts, statisticians, PhDs, master's degrees -- and all have a general programming knowledge," said Orban.
A data scientist needs good communications skills , and the Zipfian course addresses this requirement. "At the end of this project, not only do you have a full data science project under your belt, but we also require you to write up and present your results, because your insight is only as good as how effectively you can communicate it," Orban said. "If you're talking about complicated predictive models, you need to not only understand how they work, but also be able to explain how they work in layman's terms so that people can understand them and get on board."