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Comments to How 5 Data Dynamos Do Their Jobs
I’d like someone to go through the tax data and find out what happened to all the accountants before and after Wang Spreadsheet, Lotus123, and Excel were released. What happened to their earnings, ...
data_scientists  letters_to_the_editor  organizing_data  storytelling  from notes
june 2019 by jerryking
How 5 Data Dynamos Do Their Jobs
June 12, 2019 | The New York Times | By Lindsey Rogers Cook.
[Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.]
Reporters from across the newsroom describe the many ways in which they increasingly rely on datasets and spreadsheets to create groundbreaking work.

Data journalism is not new. It predates our biggest investigations of the last few decades. It predates computers. Indeed, reporters have used data to hold power to account for centuries, as a data-driven investigation that uncovered overspending by politicians, including then-congressman Abraham Lincoln, attests.

But the vast amount of data available now is new. The federal government’s data repository contains nearly 250,000 public datasets. New York City’s data portal contains more than 2,500. Millions more are collected by companies, tracked by think tanks and academics, and obtained by reporters through Freedom of Information Act requests (though not always without a battle). No matter where they come from, these datasets are largely more organized than ever before and more easily analyzed by our reporters.

(1) Karen Zraick, Express reporter.
NYC's Buildings Department said it was merely responding to a sudden spike in 311 complaints about store signs. But who complains about store signs?....it was hard to get a sense of the scale of the problem just by collecting anecdotes. So I turned to NYC Open Data, a vast trove of information that includes records about 311 complaints. By sorting and calculating the data, we learned that many of the calls were targeting stores in just a few Brooklyn neighborhoods.
(2) John Ismay, At War reporter
He has multiple spreadsheets for almost every article he works on......Spreadsheets helped him organize all the characters involved and the timeline of what happened as the situation went out of control 50 years ago......saves all the relevant location data he later used in Google Earth to analyze the terrain, which allowed him to ask more informed questions.
(3) Eliza Shapiro, education reporter for Metro
After she found out in March that only seven black students won seats at Stuyvesant, New York City’s most elite public high school, she kept coming back to one big question: How did this happen? I had a vague sense that the city’s so-called specialized schools once looked more like the rest of the city school system, which is mostly black and Hispanic.

With my colleague K.K. Rebecca Lai from The Times’s graphics department, I started to dig into a huge spreadsheet that listed the racial breakdown of each of the specialized schools dating to the mid-1970s.
analyzed changes in the city’s immigration patterns to better understand why some immigrant groups were overrepresented at the schools and others were underrepresented. We mapped out where the city’s accelerated academic programs are, and found that mostly black and Hispanic neighborhoods have lost them. And we tracked the rise of the local test preparation industry, which has exploded in part to meet the demand of parents eager to prepare their children for the specialized schools’ entrance exam.

To put a human face to the data points we gathered, I collected yearbooks from black and Hispanic alumni and spent hours on the phone with them, listening to their recollections of the schools in the 1970s through the 1990s. The final result was a data-driven article that combined Rebecca’s remarkable graphics, yearbook photos, and alumni reflections.

(4) Reed Abelson, Health and Science reporter
the most compelling stories take powerful anecdotes about patients and pair them with eye-opening data.....Being comfortable with data and spreadsheets allows me to ask better questions about researchers’ studies. Spreadsheets also provide a way of organizing sources, articles and research, as well as creating a timeline of events. By putting information in a spreadsheet, you can quickly access it, and share it with other reporters.

(5) Maggie Astor, Politics reporter
a political reporter dealing with more than 20 presidential candidates, she uses spreadsheets to track polling, fund-raising, policy positions and so much more. Without them, there’s just no way she could stay on top of such a huge field......The climate reporter Lisa Friedman and she used another spreadsheet to track the candidates’ positions on several climate policies.
311  5_W’s  behind-the-scenes  Communicating_&_Connecting  data  datasets  data_journalism  data_scientists  FOIA  groundbreaking  hidden  information_overload  information_sources  journalism  mapping  massive_data_sets  New_York_City  NYT  open_data  organizing_data  reporters  self-organization  systematic_approaches  spreadsheets  storytelling  timelines  tools 
june 2019 by jerryking
The Art of Statistics by David Spiegelhalter
May 6, 2019 | Financial Times | Review by Alan Smith.

The Art of Statistics, by Sir David Spiegelhalter, former president of the UK’s Royal Statistical Society and current Winton professor of the public understanding of risk at the University of Cambridge.

The comparison with Rosling is easy to make, not least because Spiegelhalter is humorously critical of his own field which, by his reckoning, has spent too much time arguing with itself over “the mechanical application of a bag of statistical tools, many named after eccentric and argumentative statisticians”.

His latest book, its title,
books  book_reviews  charts  Communicating_&_Connecting  data  data_journalism  data_scientists  Hans_Rosling  listening  massive_data_sets  mathematics  statistics  visualization 
may 2019 by jerryking
Tyson Made Its Fortune Packing Meat. Now It Wants to Sell You Frittatas.
Feb. 13, 2019 | WSJ | By Jacob Bunge

Tyson’s strategy is to transform the 84-year-old meatpacking giant into a modern food company selling branded consumer goods on par with Kraft Heinz Co. or Coca-Cola Co.
.....Tyson wants to be big in more-profitable prepared and packaged foods to distance itself from the traditional meat business’s boom-and-bust cycles. America’s biggest supplier of meat wants to also be known for selling packaged foods........How’s the transformation going? Amid an historic meat glut, the company’s shares are worth $4.9 billion less than they were a year ago—and are still valued like those of a meatpacker pumping out shrink-wrapped packs of pork chops and chicken breasts....Investors say the initiatives aren’t yet enough to counteract the steep challenges facing the poultry and livestock slaughtering and processing operations that have been the company’s core since....1935.....Record red meat and poultry production nationwide is pushing down prices and eroding Tyson’s meat-processing profit margins. Tariffs and trade barriers to U.S. meat have further dented prices and built up backlogs, while transport and labor costs have climbed. .......The packaged-foods business is itself struggling with consumers gravitating toward nimbler upstart brands and demanding natural ingredients and healthier recipes........Tyson's acquisition of Hillside triggered changes, including the onboarding of executives attuned to consumer trends. Tyson added managers from Fortune 100 companies, including Boeing Co. and HP Inc., who replaced some meat-processing officials who led Tyson for decades. The newcomers brought experience managing brands, understanding consumers, developing new products and building new technology tools, areas Tyson deemed central to its future......A chief sustainability officer, a newly created position, began working to shift Tyson’s image among environmental groups, .....Shifting consumer tastes have created hurdles for other packaged-food giants, such as Campbell Soup Co. and Kellogg Co. .... the meat business remains Tyson’s biggest challenge. In 2018 a flood of cheap beef, fueled by enlarged cattle herds, spurred a summer of “burger wars,” meat industry officials said. .......investment in brands and packaged foods hasn’t insulated Tyson’s business from these commodity-market swings. ........The company is also trying to improve its ability for forecast meat demand..........developing artificial intelligence to help Tyson better predict the future.........Scott Spradley, who left HP in 2017 to become Tyson’s CTO, said company data scientists are crunching numbers on major U.S. metropolitan areas. By analyzing historic meat consumption alongside demographic shifts, the number of residents moving in and out, and the frequency of birthdays and baseball games, Mr. Spradley said Tyson is building computer models that will help plan production and sales for its meat business. The effort aims to find patterns in data that Tyson’s human economists and current projections might not see. ......Deep data dives helped steer Tyson toward what executives say will be one of its biggest new product launches: plant-based replacements for traditional meat,
Big_Food  brands  Coca-Cola  CPG  cured_and_smoked  data_scientists  Kraft_Heinz  meat  new_products  plant-based  prepared_meals  reinvention  shifting_tastes  stockpiles  strategy  sustainability  tariffs  Tyson  predictive_modeling 
february 2019 by jerryking
Hedge funds fight back against tech in the war for talent
August 3, 2018 | | Financial Times | by Lindsay Fortado in London.

Like other industries competing for the top computer science talent, hedge funds are projecting an image that appeals to a new generation. The development is forcing a traditionally secretive industry into an unusual position: having to promote itself, and become cool.

The office revamp is all part of that plan, as hedge funds vie with technology companies for recruits who have expertise in machine learning, artificial intelligence and big data analytics, many of whom are garnering salaries of $150,000 or more straight out of university.

“A lot have gone down the Google route to offer more perks,” said Mr Roussanov, who works for the recruitment firm Selby Jennings in New York. “They’re trying to rebrand themselves as tech firms.”...While quantitative investing funds, which trade using computer algorithms, have been on the forefront of hiring these types of candidates, other hedge funds that rely on humans to make trading decisions are increasingly upping their quantitative capabilities in order to analyze reams of data faster.

The casual work atmosphere and flexible hours at tech firms such as Google have long been a strong draw, and hedge funds are making an effort to 'rebrand themselves' Besides the increasing amount of perks funds are trying to offer, like revamping their workplace and offering services such as free dry-cleaning, they are emphasizing the amount of money they are willing to spend on technology and the complexity of the problems in financial markets to entice recruits.

“The pitch is . . . this is a very data-rich environment, and it’s a phenomenally well-resourced environment,” said Matthew Granade, the chief market intelligence officer at Point72, Steve Cohen’s $13bn hedge fund.

For the people Mr Granade calls “data learning, quant types”, the harder the problem, the better. “The benefit for us is that the markets are one of the hardest problems in the world. You think you’ve found a solution and then everyone else catches up. The markets are always adapting. So you are constantly being presented with new challenges, and the problem is constantly getting harder.”
hedge_funds  recruiting  uWaterloo  war_for_talent  millennials  finance  perks  quantitative  hard_questions  new_graduates  data_scientists 
august 2018 by jerryking
Commodity trading enters the age of digitisation
July 9, 2018 | Financial Times | by Emiko Terazono.

Commodity houses are on the hunt for data experts to help them gain an edge after seeing their margins squeezed by rivals......commodity traders are seeking ways of exploiting their information to help them profit from price swings.

“It is really a combination of knowing what to look for and using the right mathematical tools for it,” ........“We want to be able to extract data and put it into algorithms,” .......“We then plan to move on to machine learning in order to improve decision-making in trading and, as a result, our profitability.” The French trading arm is investing in people, processes and systems to centralize its data — and it is not alone.

“Everybody [in the commodity world] is waking up to the fact that the age of digitisation is upon us,” said Damian Stewart at headhunters Human Capital.

In an industry where traders with proprietary knowledge, from outages at west African oilfields to crop conditions in Russia, vied to gain an upper hand over rivals, the democratisation of information over the past two decades has been a challenge......the ABCDs — Archer Daniels Midland, Bunge, Cargill and Louis Dreyfus Company — all recording single-digit ROE in their latest results. As a consequence, an increasing number of traders are hoping to increase their competitiveness by feeding computer programs with mountains of information they have accumulated from years of trading physical raw materials to try and detect patterns that could form the basis for trading ideas.......Despite this new enthusiasm, the road to electronification may not come easily for some traders. Compared to other financial and industrial sectors, “they are coming from way behind,” said one consultant.

One issue is that some of the larger commodities traders face internal resistance in centralising information on one platform.

With each desk in a trading house in charge of its profit-and-loss account, data are closely guarded even from colleagues, said Antti Belt, head of digital commodity trading at Boston Consulting Group. “The move to ‘share all our data with each other’ is a very, very big cultural shift,” he added.

Another problem is that in some trading houses, staff operate on multiple technology platforms, with different units using separate systems.

Rather than focusing on analytics, some data scientists and engineers are having to focus on harmonising the platforms before bringing on the data from different parts of the company.
ADM  agribusiness  agriculture  algorithms  artificial_intelligence  Bunge  Cargill  commodities  data_scientists  digitalization  machine_learning  traders  food_crops  Louis_Dreyfus  grains  informational_advantages 
july 2018 by jerryking
The quant factories producing the fund managers of tomorrow
Jennifer Thompson in London JUNE 2, 2018

The wealth of nations and individuals is ever more likely to be influenced by computer algorithms as investors look to computer-powered quantitative trading strategies to generate returns. But underpinning those machines and algorithms are real people, namely the world’s sharpest mathematicians and data scientists.

Though not hard to identify, virtually every industry — and especially Big Tech — is competing with the financial world for their skills....Competition for talent means the campuses of elite universities have become a favoured hunting ground for many groups, and that the very best students and early career academics can command staggering starting salaries should they join the investment world......The links asset managers foster with universities vary. In the UK, Oxford and Cambridge are home to dedicated institutes established and funded by investment managers. Although these were set up with a genuine desire to foster research in the field, with a nod to philanthropy, they are also proving to be an effective way to spotting future talent.

Connections between hedge funds and investment managers are less formalised on US campuses but are treated with no less importance.

Personal relationships are important,
mathematics  data_scientists  quants  quantitative  hedge_funds  algorithms  war_for_talent  asset_management  PhDs  WorldQuant  Big_Tech 
june 2018 by jerryking
A new boss for McKinsey - Firm direction
Mar 1st 2018

On February 25th the result of a long election process was made public. Kevin Sneader, the Scottish chairman of McKinsey’s Asia unit, will replace Dominic Barton as managing partner—the top job. He inherits a thriving business. The firm remains by far the biggest of the premium consultancies (see table). Over the past decade, annual revenues have doubled to $10bn; so too has the size of the partnership, to more than 2,000......Mr Barton claims that half of what it does today falls within capabilities that did not exist five years ago. It is working to ensure that customers turn to McKinseyites for help with all things digital. It has had to make acquisitions in some areas: recent purchases include QuantumBlack, an advanced-analytics firm in London, and LUNAR, a Silicon-Valley design company. It is increasingly recruiting outside the usual business schools to bring in seasoned data scientists and software developers.....McKinsey has kept plenty of older ones as clients, such as Hewlett Packard, but it has a lot more to do to crack new tech giants and unicorns (private startups worth more than $1bn). ....McKinsey’s response is to try to gain a foothold earlier on in tech firms’ life-cycles. It is targeting medium-sized companies, which would not have been able to afford its fees, by offering shorter projects with smaller “startup-sized” teams
appointments  CEOs  data_scientists  management_consulting  McKinsey  mergers_&_acquisitions  SMEs  software_developers 
march 2018 by jerryking
America’s intelligence agencies find creative ways to compete for talent - Spooks for hire
March 1, 2018 | Economist |

AMERICA’S intelligence agencies are struggling to attract and retain talent. Leon Panetta, a former Pentagon and CIA boss, says this is “a developing crisis”......The squeeze is tightest in cyber-security, programming, engineering and data science.....Until the agencies solve this problem, he says, they will fall short in their mission or end up paying more for expertise from contractors. By one estimate, contractors provide a third of the intelligence community’s workforce.....Part of the problem is the demand in the private sector for skills that used to be needed almost exclusively by government agencies, says Robert Cardillo, head of the National Geospatial-Intelligence Agency (NGA). To hire people for geospatial data analysis, he must now compete with firms like Fitbit, a maker of activity-measurement gadgets. .....The NGA now encourages certain staff to work temporarily for private firms while continuing to draw a government salary. After six months or a year, they return, bringing “invaluable” skills to the NGA, Mr Cardillo says. Firms return the favour by quietly lending the NGA experts in app development and database security. .....
war_for_talent  talent  data_scientists  CIA  security_&_intelligence  cyber_security  Leon_Panetta  SecDef  Pentagon  geospatial 
march 2018 by jerryking
Novartis’s new chief sets sights on ‘productivity revolution’
SEPTEMBER 25, 2017 | Financial Times | Sarah Neville and Ralph Atkins.

The incoming chief executive of Novartis, Vas Narasimhan, has vowed to slash drug development costs, eyeing savings of up to 25 per cent on multibillion-dollar clinical trials as part of a “productivity revolution” at the Swiss drugmaker.

The time and cost of taking a medicine from discovery to market has long been seen as the biggest drag on the pharmaceutical industry’s performance, with the process typically taking up to 14 years and costing at least $2.5bn.

In his first interview as CEO-designate, Dr Narasimhan says analysts have estimated between 10 and 25 per cent could be cut from the cost of trials if digital technology were used to carry them out more efficiently. The company has 200 drug development projects under way and is running 500 trials, so “that will have a big effect if we can do it at scale”.......Dr Narasimhan plans to partner with, or acquire, artificial intelligence and data analytics companies, to supplement Novartis’s strong but “scattered” data science capability.....“I really think of our future as a medicines and data science company, centred on innovation and access.”

He must now decide where Novartis has the capability “to really create unique value . . . and where is the adjacency too far?”.....Does he need the cash pile that would be generated by selling off these parts of the business to realise his big data vision? He says: “Right now, on data science, I feel like it’s much more about building a culture and a talent base . . . ...Novartis has “a huge database of prior clinical trials and we know exactly where we have been successful in terms of centres around the world recruiting certain types of patients, and we’re able to now use advanced analytics to help us better predict where to go . . . to find specific types of patients.

“We’re finding that we’re able to significantly reduce the amount of time that it takes to execute a clinical trial and that’s huge . . . You could take huge cost out.”...Dr Narasimhan cites one inspiration as a visit to Disney World with his young children where he saw how efficiently people were moved around the park, constantly monitored by “an army of [Massachusetts Institute of Technology-]trained data scientists”.
He has now harnessed similar technology to overhaul the way Novartis conducts its global drug trials. His clinical operations teams no longer rely on Excel spreadsheets and PowerPoint slides, but instead “bring up a screen that has a predictive algorithm that in real time is recalculating what is the likelihood our trials enrol, what is the quality of our clinical trials”.

“For our industry I think this is pretty far ahead,” he adds.

More broadly, he is realistic about the likely attrition rate. “We will fail at many of these experiments, but if we hit on a couple of big ones that are transformative, I think you can see a step change in productivity.”
adjacencies  algorithms  analytics  artificial_intelligence  attrition_rates  CEOs  data_driven  data_scientists  drug_development  failure  Indian-Americans  kill_rates  massive_data_sets  multiple_targets  Novartis  pharmaceutical_industry  predictive_analytics  productivity  productivity_payoffs  product_development  real-time  scaling  spreadsheets  Vas_Narasimhan 
november 2017 by jerryking
The Ivory Tower Can’t Keep Ignoring Tech
NOV. 14, 2017 | The New York Times | By Cathy O’Neil is a data scientist and author of the book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Follow her on Twitter at @mathbabedotorg.

We urgently need an academic institute focused on algorithmic accountability.

First, it should provide a comprehensive ethical training for future engineers and data scientists at the undergraduate and graduate levels, with case studies taken from real-world algorithms that are choosing the winners from the losers. Lecturers from humanities, social sciences and philosophy departments should weigh in.

Second, this academic institute should offer a series of workshops, conferences and clinics focused on the intersection of different industries with the world of A.I. and algorithms. These should include experts in the content areas, lawyers, policymakers, ethicists, journalists and data scientists, and they should be tasked with poking holes in our current regulatory framework — and imagine a more relevant one.

Third, the institute should convene a committee charged with reimagining the standards and ethics of human experimentation in the age of big data, in ways that can be adopted by the tech industry.

There’s a lot at stake when it comes to the growing role of algorithms in our lives. The good news is that a lot could be explained and clarified by professional and uncompromised thinkers who are protected within the walls of academia with freedom of academic inquiry and expression. If only they would scrutinize the big tech firms rather than stand by waiting to be hired.
algorithms  accountability  Cathy_O’Neil  Colleges_&_Universities  data_scientists  ethics  inequality  think_tanks  Big_Tech 
november 2017 by jerryking
Art market ripe for disruption by algorithms
MAY 26, 2017 | Financial Times | by John Dizard.

Art consultants and dealers are convinced that theirs is a high-touch, rather than a high-tech business, and they have arcane skills that are difficult, if not impossible, to replicate..... better-informed collectors [are musing about] how to compress those transaction costs and get that price discovery done more efficiently.....The art world already has transaction databases and competing price indices. The databases tend to be incomplete, since a high proportion of fine art objects are sold privately rather than at public auctions. The price indices also have their issues, given the (arguably) unique nature of the objects being traded. Sotheby’s Mei Moses index attempts to get around that by compiling repeat-sales data, which, given the slow turnover of particular works of art, is challenging.....Other indices, or value estimations, are based on hedonic regression, which is less amusing than it sounds. It is a form of linear regression used, in this case, to determine the weight of different components in the pricing of a work of art, such as the artist’s name, the work’s size, the year of creation and so on. Those weights in turn are used to create time-series data to describe “the art market”. It is better than nothing, but not quite enough to replace the auctioneers and dealers.....the algos are already on the hunt....people are watching the auctions and art fairs and doing empirics....gathering data at a very micro level, looking for patterns, just to gather information on the process.....the art world and its auction markets are increasingly intriguing to applied mathematicians and computer scientists. Recognising, let alone analysing, a work of art is a conceptually and computationally challenging problem. But computing power is very cheap now, which makes it easier to try new methods.....Computer scientists have been scanning, or “crawling”, published art catalogues and art reviews to create semantic data for art works based on natural-language descriptions. As one 2015 Polish paper says, “well-structured data may pave the way towards usage of methods from graph theory, topic labelling, or even employment of machine learning”.

Machine-learning techniques, such as software programs for deep recurrent neural networks, have already been used to analyse and predict other auction processes.
algorithms  disruption  art  art_finance  auctions  collectors  linear_regression  data_scientists  machine_learning  Sotheby’s  high-touch  pricing  quantitative  analytics  arcane_knowledge  art_market 
june 2017 by jerryking
Seven Tips for Hiring Great Data-Analytics People - The Experts - WSJ
By TOM GIMBEL
May 16, 2017

1. Check references. References may sound basic, but they are crucial.
2. Actual examples. Regardless of their previous role, have them share an example of how they’ve analyzed data in the past. Ask for both the written and oral presentation. You want the person who actually did the heavy lifting, versus the person who only interpreted the information.
3. Take-home projects. Give your candidates a case study to take home and analyze.
4. On-the-spot tests. The best way to tell in real time whether or not a candidate is good at analyzing data is to present them with a data set during the interview and have them share how they would go about drawing conclusions.
5.Challenge the status quo. Talk to the candidate about a flawed process, or something you did that went wrong. Do they challenge or push back on why you went about it a certain way, or suggest a different way?
6. Storytelling. If when explaining a project they worked on, candidates claim to have reduced or increased key metrics, ask why they thought it was successful and what downstream impact it had on the business.
7. Insightfulness. Regardless of the project, whether it was an in-person analysis or report from a take-home assignment, have them walk through how they got to each step. What was their thought process, and are they able to expand on how it would impact business?
data_scientists  hiring  howto  tips  reference-checking  references  storytelling  insights 
may 2017 by jerryking
Building an Empire on Event Data – The Event Log
Michelle WetzlerFollow
Chief Data Scientist @keen_io
Mar 31

Facebook, Google, Amazon, and Netflix have built their businesses on event data. They’ve invested hundreds of millions behind data scientists and engineers, all to help them get to a deep understanding and analysis of the actions their users or customers take, to inform decisions all across their businesses.
Other companies hoping to compete in a space where event data is crucial to their success must find a way to mirror the capabilities of the market leaders with far fewer resources. They’re starting to do that with event data platforms like Keen IO.
What does “Event Data” mean?
Event data isn’t like its older counterpart, entity data, which describes objects and is stored in tables. Event data describes actions, and its structure allows many rich attributes to be recorded about the state of something at a particular point in time.
Every time someone loads a webpage, clicks an ad, pauses a song, updates a profile, or even takes a step into a retail location, their actions can be tracked and analyzed. These events span so many channels and so many types of interactions that they paint an extremely detailed picture of what captivates customers.
data  data_driven  massive_data_sets  data_scientists  event-driven  events  strategy  engineering  Facebook  Google  Amazon  Netflix 
april 2017 by jerryking
With 125 Ph.D.s in 15 Countries, a Quant ‘Alpha Factory’ Hunts for Investing Edge - WSJ
By BRADLEY HOPE
Updated April 6, 2017

The firm is part of the forefront of a new quantitative renaissance in investing, where the ability to make sense of billions of bits of data in real time is more sought after than old-school financial analysis.

“Brilliance is very equally distributed across the world, but opportunity is not,” said Mr. Tulchinsky, a 50-year-old Belarusian. “We provide the opportunity.”

To do this, WorldQuant developed a model where it employs hundreds of scientists, including 125 Ph.D.s, around the world and hundreds more part-time workers to scour the noise of the economy and markets for hidden patterns. This is the heart of the firm. Mr. Tulchinsky calls it the “Alpha Factory.”....Quantitative hedge funds have been around for decades but they are becoming dominant players in the markets for their ability to parse massive data sets and trade rapidly. Amid huge outflows, traditional hedge funds are bringing aboard chief data scientists and trying to mimic quant techniques to keep up, fund executives say.

Some critics of quants believe their strategies are overhyped and are highly susceptible to finding false patterns in the noise of data. David Leinweber, a data scientist, famously found that the data set with the highest correlation with the S&P 500 over a 10-year period in the 1990s was butter production in Bangladesh.
quantitative  Wall_Street  PhDs  alpha  investors  slight_edge  massive_data_sets  signals  noise  data_scientists  real-time  algorithms  patterns  sense-making  quants  unevenly_distributed  WorldQuant 
april 2017 by jerryking

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