Engenheiros não devem fazer ETL

Uma pedrada de artigo.

But the role sounds really nice, and it’s easy to recruit for. Thus was born the traditional, modern day data science department: data scientists (Report developers aka “thinkers”), data engineers (ETL engineers aka “doers”), and infrastructure engineers (DBAs aka “plumbers”).

Whoops. It would seem that the business intelligence department never really changed, we just added a Hadoop cluster and started calling it by a new name.

Engenheiros não devem fazer ETL

Aplicações de Deep Learning e desafios e Big Data Analytics

Uma coisa interessante nesse artigo, foi que é um dos poucos que tem uma estratégia para Deep Learning não baseada em algoritmos, mas sim em indexação semântica. 

Abstract

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.

s40537-014-0007-7

Aplicações de Deep Learning e desafios e Big Data Analytics

MVN – Ferramenta web para verificar se os dados seguem uma distribuição normal

Tá certo que quem está acreditando na bolha do big data nem sabe o que é isso; mas para quem usa a estatística como ferramenta esse portal pode ajudar e muito.

MVN – Ferramenta web para verificar se os dados seguem uma distribuição normal

10 coisas que a estatística pode nos ensinar sobre a análise de Big Data

Por mais que o ruído sobre o Big Data seja maior do que o sinal, posts como esse mostram que há uma luz no fim do túnel.

  1. If the goal is prediction accuracy, average many prediction models together. In general, the prediction algorithms that most frequently win Kaggle competitions or the Netflix prize blend multiple models together. The idea is that by averaging (or majority voting) multiple good prediction algorithms you can reduce variability without giving up bias. One of the earliest descriptions of this idea was of a much simplified version based onbootstrapping samples and building multiple prediction functions – a process called bagging (short for bootstrap aggregating). Random forests, another incredibly successful prediction algorithm, is based on a similar idea with classification trees.
  2. Know what your real sample size is.  It can be easy to be tricked by the size of a data set. Imagine you have an image of a simple black circle on a white background stored as pixels. As the resolution increases the size of the data increases, but the amount of information may not (hence vector graphics). Similarly in genomics, the number of reads you measure (which is a main determinant of data size) is not the sample size, it is the number of individuals. In social networks, the number of people in the network may not be the sample size. If the network is very dense, the sample size might be much less. In general the bigger the sample size the better and sample size and data size aren’t always tightly correlated.

10 coisas que a estatística pode nos ensinar sobre a análise de Big Data

Um post demolidor do Stephen Few sobre o Big Data

Contrariando os departamentos de marketing dos grandes vendedores de software, o Stephen Few vem travando uma guerra quase que pessoal contra a indústria do Big Data.

Como esse termo que é mais comentado nas redes sociais e no marketing do que é praticado em campo (como eu chamo esses verdadeiros soldados da ciência de dados como o Luti, Erickson Ricci, Big Leka, Fabiano Amorim, Fabrício Lima, Marcos Freccia, entre outros) há uma entropia de opiniões e conceitos. Com essa entropia quem perde são somente os desinformados que não conseguem separar o sinal do ruído que acabam virando presas fáceis de produtos com qualidade duvidosa.

A vítima da vez foi o livro Dataclysm do Christian Rudder.

Em um dado momento do livro, o autor realiza um tipo de criticismo ao processo científico em que alguns pesquisadores das ciências do comportamento aplicadas utilizam seus alunos como amostra, e o autor de forma quase que pedante chama essas pesquisas de WEIRD (White, Educated, Industrialized, Rich and Democratic). Em tradução livre uma brincadeira com o acrônimo da palavra “Esquisita” em inglês como uma espécie de conotação pejorativa.

I understand how it happens: in person, getting a real representative data set is often more difficult than the actual experiment you’d like to perform. You’re a professor or postdoc who wants to push forward, so you take what’s called a “convenience sample”—and that means the students at your university. But it’s a big problem, especially when you’re researching belief and behavior. It even has a name: It’s called WEIRD research: white, educated, industrialized, rich, and democratic. And most published social research papers are WEIRD.

O que poderia ser um criticismo de um autor que tem como background os méritos em ser um dos co-fundadores do OKCupid, vira em uma leitura mais cuidadosa da exposição de uma lacuna em relação à análise de dados e pior: expõe um erro de entendimento em relação à teoria da amostragem (nada que uma leitura atenciosa do livro dos professores Bolfarine e Bussab não solucionasse).

E a resposta do Stephen Few é demolidora:

Rudder is a co-founder of the online dating service OKCupid. As such, he has access to an enormous amount of data that is generated by the choices that customers make while seeking romantic connections. Add to this the additional data that he’s collected from other social media sites, such as Facebook and Twitter, and he has a huge data set. Even though the people who use these social media sites are more demographically diverse than WEIRD college students, they don’t represent society as a whole. Derek Ruths of McGill University and Jürgen Pfeffer of Carnegie Mellon University recently expressed this concern in an article titled “Social Medial for Large Studies of Behavior,” published in the November 28, 2014 issue of Science. Also, the conditions under which the data was collected exercise a great deal of influence, but Rudder has “stripped away” most of this context.

Lição #1: Demografia não é sinal de diversidade em análise de dados.

Após esse trecho vem uma fala do Stephen Few que mostra de maneira bem sutil o arsenal retórico dos departamentos de marketing para convencer pessoas inteligentes em investir em algo que elas não entendem que é a poesia do entendimento; e uma outra situação mais grave: acreditar que os dados online em que somos perfis falam de maneira exata quem somos.

Contrary to his disclaimers about Big Data hype, Rudder expresses some hype of his own. Social media Big Data opens the door to a “poetry…of understanding. We are at the cusp of momentous change in the study of human communication.” He believes that the words people write on these sites provide the best source of information to date about the state and nature of human communication. I believe, however, that this data source reveals less than Rudder’s optimistic assessment. I suspect that it mostly reveals what people tend to say and how they tend to communicate on these particular social media sites, which support specific purposes and tend to be influenced by technological limitations—some imposed (e.g., Twitter’s 140 character limit) and others a by-product of the input device (e.g., the tiny keyboard of a smartphone). We can certainly study the effects that these technological limitations have on language, or the way in which anonymity invites offensive behavior, but are we really on the “cusp of momentous change in the study of human communication”? To derive useful insights from social media data, we’ll need to apply the rigor of science to our analyses just as we do with other data sources.

Lição #2: Entender o viés amostral, sempre irá reduzir a chance de más generalizações.

Lição #3: Contextos específicos não são generalizáveis (i.e. indução não é a mesma coisa que dedução).

E por último o autor fala uma pérola que merece estar em um panteão de bullshits (como esse da Bastter.com que é o maior combatente do bullshit midiático e de marketing do Brasil). É necessário que os leitores mais sensíveis a ausência de raciocínio lógico-cientifico segurem-se com o que vem aí. Segurem-se porque essa afirmação é forte:

“With Big Data we no longer need to adhere to the basic principles of science.”

 “Com Big Data não precisaremos aderir os princípios básicos da ciência”

A resposta, mais uma demolição:

Sourcing data from the wild rather than from controlled experiments in the lab has always been an important avenue of scientific study. These studies are observational rather than experimental. When we do this, we must carefully consider the many conditions that might affect the behavior that we’re observing. From these observations, we carefully form hypotheses, and then we test them, if possible, in controlled experiments. Large social media data sets don’t alleviate the need for this careful approach. I’m not saying that large stores of social media data are useless. Rather, I’m saying that if we’re going to call what we do with it data science, let’s make sure that we adhere to the principles and practices of science. How many of the people who call themselves “data scientists” on resumes today have actually been trained in science? I don’t know the answer, but I suspect that it’s relatively few, just as most of those who call themselves “data analysts” of some type or other have not been trained in data analysis. No matter how large the data source, scientific study requires rigor. This need is not diminished in the least by data volume. Social media data may be able to reveal aspects of human behavior that would be difficult to observe in any other way. We should take advantage of this. However, we mustn’t treat social media data as magical, nor analyze it with less rigor than other sources of data. It is just data. It is abundantly available, but it’s still just data.

Utilizando a mesma lógica contida na argumentação, não precisamos de ensaios randomizados para saber se um determinado remédio ou mesmo tipo de paradigma de alimentação está errado; podemos esquecer questões como determinação amostral, a questão das hipóteses, ou mesmo conceitos básicos de randomização amostral, ou mesmo verificar especificidades da população para generalizar conclusões, ou sequer considerar erros aleatórios ou flutuações estatísticas.

Apenas pegue dados de redes sociais e generalize.

Lição #4: Volume não significa nada sem significância amostral.

Lição #5: Independente da fonte dos dados, ainda continuam sendo dados. E sempre devem ser tratados com rigor.

Haverá alguns posts sobre essa questão amostral, mas o mais importante são as lições que podemos tirar desses que eu considero inocentes a serviço da desinformação.

Um post demolidor do Stephen Few sobre o Big Data

Michael Jordan (Não o do basquete) fala sobre alguns tópicos em Aprendizado de Máquina e sobre Big Data

Abaixo está o depoimento mais sensato sobre alguns assuntos relativos à análise de dados, Data Mining, e principalmente Big Data.

UPDATE: O próprio MJordan deu uma entrevista dizendo que em alguns pontos foi mal interpretado. No entanto, cabe ressaltar que muito do que é importante na fala ele não falou nada a respeito; então tirem as suas conclusões.

Para quem não sabe, o Michael Jordan (IEEE) é uma das maiores autoridades no que diz respeito em aprendizado de máquina no mundo acadêmico.

Esta entrevista (que foi sonegada por este espaço por puro desleixo) ele apresenta argumentos extremamente sóbrios e lúcidos sobre Deep Learning (que terá um tópico aqui em breve) e principalmente sobre o Big Data.

Sobre a parte de Big Data em especial, esses comentários convidam à uma reflexão, e acima de tudo colocam pontos que merecem ser discutidos sobre esse fenômeno.

Obviamente empresas do calibre da Google, Amazon, Yahoo, e alguns projetos como Genoma podem ter benefício de grandes volumes de dados. O problema principal é que todo essa hipsterização em torno do Big Data parece muito mais algo orientado ao marketing do que a resolução de questões de negócio pertinentes.

Seguem alguns trechos importantes:

Sobre Deep Learning, simplificações e afins…

IEEE Spectrum: I infer from your writing that you believe there’s a lot of misinformation out there about deep learning, big data, computer vision, and the like.

Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are. Specifically on the topic of deep learning, it’s largely a rebranding of neural networks, which go back to the 1980s. They actually go back to the 1960s; it seems like every 20 years there is a new wave that involves them. In the current wave, the main success story is the convolutional neural network, but that idea was already present in the previous wave. And one of the problems with both the previous wave, that has unfortunately persisted in the current wave, is that people continue to infer that something involving neuroscience is behind it, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.

Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists. You called these “cartoon models” of the brain.

Michael Jordan: I wouldn’t want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

Sobre Big Data

Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.

Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.

Spectrum: How so?

Michael Jordan: In a classical database, you have maybe a few thousand people in them. You can think of those as the rows of the database. And the columns would be the features of those people: their age, height, weight, income, et cetera.

Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns—and we do in modern databases—you’ll get up into millions and millions of attributes for each person.

Now, if I start allowing myself to look at all of the combinations of these features—if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age—what’s the probability you will have a certain disease or you will like my advertisement? Now I’m getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe.

Those are the hypotheses that I’m willing to consider. And for any particular database, I will find some combination of columns that will predict perfectly any outcome, just by chance alone. If I just look at all the people who have a heart attack and compare them to all the people that don’t have a heart attack, and I’m looking for combinations of the columns that predict heart attacks, I will find all kinds of spurious combinations of columns, because there are huge numbers of them.

So it’s like having billions of monkeys typing. One of them will write Shakespeare.

Spectrum:Do you think this aspect of big data is currently underappreciated?

Michael Jordan: Definitely.

Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?

Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.

Spectrum: What will happen if people working with data don’t heed your advice?

Michael Jordan: I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.

Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.

And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up. So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.

Spectrum: Do we currently have the tools to provide those error bars?

Michael Jordan: We are just getting this engineering science assembled. We have many ideas that come from hundreds of years of statistics and computer science. And we’re working on putting them together, making them scalable. A lot of the ideas for controlling what are called familywise errors, where I have many hypotheses and want to know my error rate, have emerged over the last 30 years. But many of them haven’t been studied computationally. It’s hard mathematics and engineering to work all this out, and it will take time.

It’s not a year or two. It will take decades to get right. We are still learning how to do big data well.

Spectrum: When you read about big data and health care, every third story seems to be about all the amazing clinical insights we’ll get almost automatically, merely by collecting data from everyone, especially in the cloud.

Michael Jordan: You can’t be completely a skeptic or completely an optimist about this. It is somewhere in the middle. But if you list all the hypotheses that come out of some analysis of data, some fraction of them will be useful. You just won’t know which fraction. So if you just grab a few of them—say, if you eat oat bran you won’t have stomach cancer or something, because the data seem to suggest that—there’s some chance you will get lucky. The data will provide some support.

But unless you’re actually doing the full-scale engineering statistical analysis to provide some error bars and quantify the errors, it’s gambling. It’s better than just gambling without data. That’s pure roulette. This is kind of partial roulette.

Spectrum: What adverse consequences might await the big-data field if we remain on the trajectory you’re describing?

Michael Jordan: The main one will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress. And then there will be a period during which it will be very hard to get resources to do data analysis. The field will continue to go forward, because it’s real, and it’s needed. But the backlash will hurt a large number of important projects.

Michael Jordan (Não o do basquete) fala sobre alguns tópicos em Aprendizado de Máquina e sobre Big Data

Muitas peças mas nenhum carro

Neste post do Mikio Braun ele lança uma importante reflexão na estruturação de uma solução de infraestrutura em Big Data no qual muito do que está sendo vendido não está em linha com o objetivo final que é a resolução de problemas de negócio.

The bottom line is that all those pieces of Big Data infrastructure which exists today provide you with a lot of pretty impressive functionality, distributed storage, scalable computing, resilience, and so on, but not in a way which solves your data analysis problems out of the box. The analogy I like is that Big Data is a lot like providing you with an engine, a transmission, some tires, a gearbox, and so on, but no car.

Muitas peças mas nenhum carro

10 coisas que a estatística pode nos ensinar sobre Big Data

De tempos em tempos vemos vendedores de software tentando empurrar ‘novidades’ como Big Data, Map Reduce, Processamento Distribuído, etc. Isso é muito bom no sentido de marketing e propaganda, mas dentro do aspecto técnico todos que trabalham com análise de dados devem no mínimo conhecer o básico, e este básico se chama estatística.

Entendam uma coisa, Big Data hoje nada mais é do que um jargão de marketing utilizado por todos os players do mercado para causar frisson em gerentes de tecnologia da informação, diretores, coordenadores, gerentes entre outros.

Análise de dados sempre houve desde quando Edgar Frank Codd começou os seus postulados sobre modelagem de bases de dados baseado no paradigma da álgebra relacional.

O que mudou foi que a Lei de Moore que se aplicava à capacidade de processamento (transistores nos chips) e que muitos acreditavam se também aplicava-se ao armazenamento simplesmente provou-se errada. Em outras palavras, descobrimos que podemos armazenar muito mais informação, a um custo extremamente baixo do que fazíamos a 40 anos atrás.

Veja no gráfico abaixo o que o mesmo Jeff Leek considera como a ‘revolução do big data’.

Big Data Revolution

Se isso aumentou a disponibilidade dos dados para a análise, por outro lado muito por culpa da ciência da computação que (na minha visão pessoal de momento) prostituiu a estatística com o advento dos algoritmos muitos cientistas da computação, bacharéis em Sistemas de Informação, entre outros que por ventura passaram a realizar análise de dados acharam que poderiam subestimar a estatística que está a muito tempo ajudando cientistas do mundo inteiro.

Um pequeno aforismo que eu tenho sobre essa questão é “não dá para pensar em Big Data, quando ainda não aprendemos os postulados sobre amostragem que a estatística nos oferece”.** Simples assim.

Com isso, seguem as 10 coisas que a estatística pode ajudar o Big Data elencadas pelo Jeff Leek:

1) If the goal is prediction accuracy, average many prediction models together
2) When testing many hypotheses, correct for multiple testing
3) When you have data measured over space, distance, or time, you should smooth
4) Before you analyze your data with computers, be sure to plot it
5) Interactive analysis is the best way to really figure out what is going on in a data set
6) Know what your real sample size is
7) Unless you ran a randomized trial, potential confounders should keep you up at night
8) Define a metric for success up front
9) Make your code and data available and have smart people check it
10) Problem first not solution backward

**Assim que eu finalizar algumas leituras importantes sobre o assunto vou falar mais um pouco dessa besteira de big data que estão vendendo, e algumas alternativas a respeito disso.

10 coisas que a estatística pode nos ensinar sobre Big Data

Porque o fenômeno do Big Data está envolvido em Problemas? Eles esqueceram estatística aplicada

O Jeff Leek neste post coloca um ponto de vista bem relevante no que tange a análise de dados.

Em tempos em que vendedores de software de Business Intelligence, ou mesmo vendedores deSistemas Gerenciadores de Banco de Dados tentam seduzir gerentes, diretores, e tomadores de decisão de que precisamos de mais dados; este post simplesmente diz: “Não, aprendam estatística antes!

One reason is that when you actually take the time to do an analysis right, with careful attention to all the sources of variation in the data, it is almost a law that you will have to make smaller claims than you could if you just shoved your data in a machine learning algorithm and reported whatever came out the other side.

The prime example in the press is Google Flu trends. Google Flu trends was originally developed as a machine learning algorithm for predicting the number of flu cases based on Google Search Terms. While the underlying data management and machine learning algorithms were correct, a misunderstanding about the uncertainties in the data collection and modeling process have led to highly inaccurate estimates over time. A statistician would have thought carefully about the sampling process, identified time series components to the spatial trend, investigated why the search terms were predictive and tried to understand what the likely reason that Google Flu trends was working.

As we have seen, lack of expertise in statistics has led to fundamental errors in both genomic science and economics. In the first case a team of scientists led by Anil Potti created an algorithm for predicting the response to chemotherapy. This solution was widely praised in both the scientific and popular press. Unfortunately the researchers did not correctly account for all the sources of variation in the data set and had misapplied statistical methods and ignored major data integrity problems. The lead author and the editors who handled this paper didn’t have the necessary statistical expertise, which led to major consequences and cancelled clinical trials.

Similarly, two economists Reinhart and Rogoff, published a paper claiming that GDP growth was slowed by high governmental debt. Later it was discovered that there was an error in an Excel spreadsheet they used to perform the analysis. But more importantly, the choice of weights they used in their regression model were questioned as being unrealistic and leading to dramatically different conclusions than the authors espoused publicly. The primary failing was a lack of sensitivity analysis to data analytic assumptions that any well-trained applied statisticians would have performed.

No final o autor faz uma pergunta que eu acho extremamente relevante: ” When thinking about the big data era, what are some statistical ideas we’ve already figured out?”

Eu tenho algumas:

1) Determinação de tamanho de amostra para criação de modelos usando tamanho de população conhecida ou desconhecida;

2) Design de Experimentos

3) Análise Exploratória de Dados

Porque o fenômeno do Big Data está envolvido em Problemas? Eles esqueceram estatística aplicada

O Estouro da Bolha do Big Data

Provavelmente esse é um dos melhores posts da blogosfera a respeito do assunto. A Cathy O’Neil toca na ferida de muitos dos Vendedores Engenheiros de Vendas no que tange o alto volume de publicações, posts, e demais White Advertised Papers lançados sobre o Big Data.

A questão como um todo merece reflexões em doses homeopáticas, mas seguem abaixo alguns dos interessantes pontos do post:

[…] Unfortunately, this process rarely actually happens the right way, often because the business people ask their data people the wrong questions to being with, and since they think of their data people as little more than pieces of software – data in, magic out – they don’t get their data people sufficiently involved with working on something that data can address.[…] 

[…] Also, since there are absolutely no standards for what constitutes a data scientist, and anyone who’s taken a machine learning class at college can claim to be one, the data scientists walking around often have no clue how to actually form the right questions to ask anyway. They are lopsided data people, and only know how to answer already well-defined questions like the ones that Kaggle comes up with. That’s less than half of what a good data scientist does, but people have no idea what a good data scientist does.[…] 

[…] Here’s what I see happening. People have invested some real money in data, and they’ve gotten burned with a lack of medium-term results. Now they’re getting impatient for proof that data is an appropriate place to invest what little money their VC’s have offered them. That means they want really short-term results, which means they’re lowballing data science expertise, which means they only attract people who’ve taken one machine learning class and fancy themselves experts.[…] 

[…] In other words, data science expertise has been commodified, and it’s a race to the bottom. Who will solve my business-critical data problem on a short-term consulting basis for less than $5000? Less than $4000?[…] 

[…] My forecast is that, once the hype wave of big data is dead and gone, there will emerge reasonable standards of what a data scientist should actually be able to do, and moreover a standard of when and how to hire a good one. It’ll be a rubrik, and possibly some tests, of both problem solving and communication.[…] 

O Estouro da Bolha do Big Data