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Expert Interview (Part 2): James Kobielus on Reasons for Data Scientist Insomnia including Neural Network Development Challenges

In the first half of our two-part conversation with Wikibon lead analyst James Kobielus (@jameskobielus), he discussed the incredible impact of machine learning in helping organizations make better business decisions and be more productive. In today’s Part 2, he addresses what aspects of machine learning should be keeping data scientists up at night. (Hint: neural networks)

Several Challenges Involved with Developing Neural Networks

Developing these algorithms is not without its challenges, Kobielus says.

The first major challenge is finding data.

Algorithms can’t do magic unless they’ve been “trained.” And in order to train them, the algorithms require fresh data. But acquiring this training data set is a big hurdle for developers.

For eCommerce sites, this is less of a problem – they have their own data in the form of transaction histories, site visits and customer information that can be used to train the model and determine how predictive it is.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 2): James Kobielus on Reasons for Data Scientist Insomnia including Neural Network Development Challenges

But the process of amassing those training data sets when you don’t have data is trickier – developers have to rely upon commercial data sets that they’ve purchased or open source data sets.

After getting the training data, which might come from a dozen different sources, the next challenge is aggregating it so the data can be harmonized with a common set of variables. Another challenge is having the ability to cleanse data to make sure it’s free of contradictions and inconsistencies. All this takes time and resources in the form of databases, storage, processing and data engineers. This process is expensive but essential. (For more on this, read Uniting Data Quality and Data Integration)

Third, organizations need data scientists, who are expensive resources. They need to find enough people to manage the whole process – from building to training to evaluating to governing.

“Finding the right people with the right skills, recruiting the right people is absolutely essential,” Kobielus says.

Before jumping into machine learning, organizations should also make sure it makes sense for your business strategies.

Industries like finance and marketing have made a clear case for themselves in implementing Big Data. In the case of finance, it allows them to do high-level analysis to detect things like fraud. And in marketing, for instance, CMOs, found it useful to develop algorithms that allowed them  to conduct sentiment analysis on social media.

There are a lot of uses for it to be sure, Kobielus says, but there are methods for deriving insights from data that don’t involve neural networks. It’s up to the business to determine whether using neural networks is overkill for their purposes.

“It’s not the only way to skin these cats,” he says.

If you already have the tools in place, then it probably makes sense to keep using them. Or, if you find traditional tools can’t address needs like transcription or facial recognition, then it probably makes sense to go to a newer form of machine learning.

What Should Really Be Keeping Data Scientists Up at Night 

While those in the tech industry might be fretting over whether AI will displace the gainfully employed or that there’s a skills deficit in the field, Kobielus has other worries related to data science.

For one, the algorithms used for machine learning and AI are really complex and they drive so many decisions and processes in our lives.

“What if something goes wrong? What if a self-driving vehicle crashes? What if the algorithm does something nefarious in your bank account? How can society mitigate the risks,” Kobielus asks.

When there’s a negative outcome, the question asked is who’s responsible. The person who wrote the algorithm? The data engineer? The business analyst who defined the features?

These are the questions that should keep data scientists, businesses, and lawyers up at night. And the answers aren’t clear-cut.

In order to start answering some of these questions, there needs to be algorithmic transparency, so that there can be algorithmic accountability.

Ultimately, everyone is responsible for the outcome.

There’s a huge legal gray area when it comes to machine learning because the models used are probabilistic and you can’t predict every single execution path for a given probabilistic application built on ML.

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“There’s a limit beyond which you can anticipate the particular action of a particular algorithm at a particular time,” Kobielus says.

For algorithmic accountability, there need to be audit trails. But an audit log for any given application has the potential to be larger than all the databases on Earth. Not just that, but how would you roll it up into a coherent narrative to hand to a jury?

“Algorithmic accountability should keep people up at night,” he says.

Just as he said concerns about automation are overblown, Kobielus says it’s also unnecessary to worry that there aren’t enough skilled data scientists working today.

Data science is getting easier.

Back in the 80s, developers had to know underlying protocols like HTTP, but today nobody needs to worry about the protocol plumbing anymore. It will be the same for machine learning, Kobielus says. Increasingly, the underlying data is being abstracted away by higher-level tools that are more user friendly.

“More and more, these things can be done by average knowledge workers, and it will be executed by underlying structure,” he says.

Does Kobielus worry about the job security of data scientists then? Not really. He believes data science automation tools will allow data scientists to do less with more and hopefully to allow them to develop their skills in more challenging and creative realms.

For 5 key trends to watch for in the next 12 months, check out our new report: 2018 Big Data Trends: Liberate, Integrate & Trust

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Data Scientist and Beyond: Jobs in the Big Data World

Big Data is bigger than ever, and so is the job market for people who can help transform, store, analyze or otherwise work with big data. This article outlines the current job market and the types of Big Data jobs.

Hard statistics on the number of big data jobs available in 2017 are elusive. However, a quick look at the data that is available strongly suggests that the Big Data job market is strong and growing.

There were 1.9 million Big Data jobs in the United States, and 4.4 million worldwide, in 2015, according to Statista.

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In spring 2017, IBM predicted that Big Data jobs in the United States would increase to 2.7 million by 2020, a 28 percent increase over current levels.

Big Blue also thinks that there will be 700,000 new positions per year in Big Data by 2020, and that average salaries for typical big data jobs hover around $ 110,000.

Big Data Jobs: 5 Common Positions

What, exactly, do these various jobs entail? And which types of skills should you acquire if you want to work in the Big Data world?

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Following are examples of typical big data positions:

Data Engineer

Data engineers architect and maintain platforms for storing and analyzing data. You could think of data engineer as the most general-purpose type of position in the Big Data field. To be a data engineer, you need to know a little bit about everything related to data analytics, transformation, and storage.

Data Scientist

A data scientist specializes in interpreting data, usually in an automated fashion. Programming skills are a must, as is the ability to work with modern Big Data frameworks like Hadoop.

Machine Learning Engineers

This is a relatively new position that focuses on using data to power artificial intelligence. This job overlaps somewhat with data scientist jobs since data scientists analyze data in order to reveal insights.

However, as machine learning becomes more and more important in applications ranging from “learning” thermostats to self-driving cars, demand is rising for engineers who focus specifically on machine learning.

Storage Engineer

Storage engineers have been around for decades. They are the people who maintain databases and storage infrastructure.

As Big Data has grown into a discipline of its own, however, storage engineers have assumed special importance. Architecting and maintaining storage systems for petabytes of data requires special skills, and the ability to work with platforms like Hadoop.

Data Quality Engineer

Big Data isn’t very helpful if it lacks quality. Data quality engineers specialize in improving and maintaining the quality of data sets.

In many organizations, this work might fall within the role assumed by data engineers, but dedicated data quality engineering positions are now also starting to appear within organizations that recognize the extreme importance of maintaining data quality. Working as a data quality engineer requires expertise with the types of problems that degrade data quality, as well as the ability to use data quality tools.

Check out Syncsort’s free eBook, The New Rules for Your Data Landscape, to discover how today’s new data supply chain impacts how data is moved, manipulated, and cleansed.

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What IBM looks for in a data scientist

 What IBM looks for in a data scientist

Job seekers sometimes ask how IBM defines “data scientist.” It’s an important question since more and more would-be data scientists are fighting for attention in an increasingly lucrative labor market.

The first step is to distinguish between what we see as true data scientists and other professionals working in adjacent roles (for instance, data engineers, business analysts, and AI application developers). To make that distinction, let’s first define what we mean by data science.

At its core, data science is applying the scientific method to solve business problems.

You can further expand on the definition by understanding that we solve those business problems using artificial intelligence to create predictions and prescriptions and to optimize processes.

The definition demonstrates that to achieve the true potential of data science, we need data scientists with very particular experiences and skills — specifically, we need people with the experiences and skills required to run and complete data science projects:

1. Training as a scientist, with an MS or PhD
2. Expertise in machine learning and statistics, with an emphasis on decision optimization
3. Expertise in R, Python, or Scala
4. Ability to transform and manage large data sets
5. Proven ability to apply the skills above to real-world business problems
6. Ability to evaluate model performance and tune it accordingly

Let’s look at those qualifications in the context of our definition of data science.

1. Training as a scientist, with a Masters of Science or Doctorate

This is less about the degree itself and more about what you learn when you get an advanced degree. In short, you learn the scientific method, which starts with the ability to take a complex yet abstract problem and break it down into a set of testable hypotheses. This continues with how well you design experiments to test your hypotheses, and how you analyze the results to see whether the hypotheses are confirmed or contradicted. A determined person can learn these skills outside of academia or via the right mix of online training and practice — so there’s some flexibility around having the actual degree — but direct experience applying the scientific method is a must.

Another advantage of an advanced degree is the rigor of the peer review process and publishing requirements that the degree programs impart. To get published, candidates have to present their work in a way that allows others to review and reproduce it. You must also provide evidence that the results are valid and the methods are sound. Doing so requires a deep understanding of the difference between probabilistic and deterministic factors as well as the value and curse of the correlation. It’s possible to get an abstract sense of those values, but there’s no substitute for the negative and positive reinforcement from mentors or the rejection or acceptance of journals and reviews.

2. Expertise in machine learning and statistics, with an emphasis on decision optimization

Applying the scientific method to business problems lets us make better decisions by predicting what will happen next. Those predictions are the product of artificial intelligence and more specifically machine learning. For a true data scientist, the core technical skillsets of machine learning and statistics are simply non-negotiable.

In addition, decision optimization (aka operations research) is a fast-growing aspect of data science. Indeed, the goal of data science is to help make better decisions by probabilistically estimating what’s likely to occur in the future. Carefully applying decision optimization lets data scientists prescribe or determine the next best action for the best business outcome.

3. Expertise in R, Python, or Scala

Being a data scientist doesn’t require you to be as good at programming as professional developers, but the ability to create and run code that supports the data science process is mandatory — and that includes the ability to use statistical and machine learning packages in one of the popular data science languages.

Python, R, and Scala are the fastest-growing languages for data science, along with Julia, another upcoming language in the space, though Julia isn’t yet fully mature. Like Python, R, and Scala, the core of Julia is open source. But it’s important to note that the reason to use these languages isn’t that they’re free, but for the innovation and the freedom to take them where you want to go.

4. Ability to transform and manage large data sets

The fourth skill is sometimes called big data. Here, the ability to use distributed data processing frameworks like Apache Spark is key. The true data scientist will know how to pull data sets together from multiple sources and multiple data types with the help of his or her data science team. The data itself might be a combination of structured, semi-structured, and unstructured data living on multiple clouds.

The data management process consists of finding and collecting the data, exploring the data, transforming the data, identifying features (data elements important in the prediction), engineering the features, and making the data accessible to the model for training. A priority for any data scientist will be streamlining this process, which can easily eat up 80 percent of their time.

5. Proven ability to apply the skills above to real-world business problems

Fifth on the list is a soft skill set. It’s the ability to communicate with non-data scientists in order to make sure that data science teams have the data resources they need and that they’re applying data science to the right business problems. Mastering this skill also means ensuring that the results of data science projects — for instance, predictions about the probable evolution of the business — are fully understood and actionable by business people. This requires good storytelling skills, and in particular, the ability to map mathematical concepts to common sense.

6. Ability to evaluate model performance and tune it accordingly

To some, this sixth skillset is an aspect of the second skillset: expertise in machine learning in general. We wanted to call it out separately because, all too often, it’s what distinguishes a good data scientist from a dangerous one. Data scientists who lack this skill can easily believe that they’ve created and deployed effective models when in fact their models are badly over-fit to the available training data.

Be a true data scientist

If you want to be a true data scientist — as opposed to an aspiring data scientist or a data scientist in title only — we encourage you to master each of these six competencies. A data scientist is fundamentally different from a business analyst or data analyst, who often serve as product owners on data science teams, with the important role of providing subject matter expertise to the data scientists themselves.

That’s not to say business analysts, data analysts, and others can’t transition to become true data scientists — but understand that it takes time, commitment, mentoring, and applying yourself again and again to real and difficult problems.

Seth Dobrin is vice president and chief data officer at IBM Analytics.

Jean-François Puget is an IBM distinguished engineer in machine learning and optimization.

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The numbers don’t lie: Why women must fill the data scientist demand

 The numbers don’t lie: Why women must fill the data scientist demand

We hear every day that more and more jobs are disappearing, yet the data science community cannot keep up with unprecedented demand. When you consider the growth of the industry, it’s not surprising to hear there will be a shortage of 1.5 million analysts capable of analyzing big data in the U.S. alone, by 2018, according to McKinsey. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.

So why are women not entering the field? It appears women are deterred from jobs in data science for the same reason they are deterred from other STEM fields. But interestingly, some stereotypically female traits are exactly the qualities that make for a successful data scientist. In fact, we’re seeing some women channel these stereotypes to move ahead of their male counterparts in the industry.

Consider the following three examples.

Collaboration. Women are skilled collaborators, able to work with all different people. This is an important quality for data science professionals, as cross-departmental collaboration is key. Data science touches every function in a modern business, and those most successful will be able to collaborate with all different teams and individuals.

Communication. For many of the same reasons, data scientists must also be strong communicators. Communication is an area where many women traditionally excel and it’s an important quality to have. Not only do data scientists lead dialogue and share insights among various departments, but they also need to be able to listen well and speak the language of different teams. For example, communicating with the sales department may be different from communicating with the IT department. Good data scientists will be able to speak to everyone.

Perspective. Being able to inspire a team and see the big picture are both important. A data scientist must be able to not only collect and analyze data but draw meaningful insights and understand what it means for the company. The ability to holistically view a situation is a competitive differentiator for organizations as well as a positive attribute that many women possess.

Once we begin associating a variety of skills with data science, the perceptions of our industry can change. According to the Washington Post, women now make up 40 percent of graduates with degrees in statistics – a popular starting point for a career in data science.

While a degree in mathematics is a great place to start, it’s important not to categorize the position as being completely scientific and technical, only suited for individuals who excel at math and science. A career in data science is transferrable across all industries. Whether you have a passion for healthcare or retail, there is likely a data science opportunity for you.

Personally, I was drawn to data science because of my love for innovation and my passion for wanting to make the world a better place through technology. Specifically, the Internet of Things (IoT) is a technology I believe can change the world. I was taken by the idea that through data science we can accelerate the impact of IoT, as data scientists are key in helping organizations get a true ROI. Additionally, big data can only be managed through intelligent systems and processes, which you need data scientists to administer.

So how can you get started? In an effort to educate people in other fields about data science, we are seeing boot camps pop up around the world. These 10-12 week courses are a great way for people to get their feet wet, to see whether a career in data science is right for them, and to simply gain a better understanding of what the job entails. Further, an increasing number of universities are adding full-time data science majors to their roster of undergraduate offerings. The University of Cambridge has even created the Cambridge Big Data Strategic Research Initiative to bring together researchers from across the University to address challenges presented by our access to unprecedented volumes of data.

In the field of data science, the numbers don’t lie. While the number of female data scientists is currently disproportionate to men, the employment demands and signs from academia are encouraging. We may have to fight a little harder to break down the stereotypes that have prevented women from entering STEM fields for many years, but it’s worth the fight. Setting the stage now will inspire future generations to see that they too can be a data scientist.

Tanja Rueckert is President of IoT and Digital Supply Chain at SAP.

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U.S. chief data scientist: Entrepreneurs should do a ‘tour of duty’ in government

There’s no question that the U.S. government has incredible amount of data. Whether it’s for things like the census, housing, agriculture, transportation, or health care, federal agencies have accumulated a lot of data around what’s going on in the country, but what use is talking about governmental innovation if everything remains siloed?

In the past seven years, the White House has taken up efforts to leverage more technology at the federal level. It has tapped Aneesh Chopra, Todd Park, and most recently former Googler’s Megan Smith to the post of U.S. chief technology officer, a position first created by President Obama, brought on board Twitter veteran Jason Goldman to assist the administration with digital outreach, and recruited renowned data scientist DJ Patil as the country’s inaugural chief data scientist.

“President Obama has been unique,” Patil told VentureBeat in an interview during visit to the San Francisco Bay Area where he still maintains a residence. “He’s recognized the sea change with data and made it a cornerstone of his administration. With a data-driven government, you take the data that we use in services like weather forecasting, data submitted by citizens like with the census….and use it to make better and faster decisions.”

In the role of the chief data scientist, Patil has been tasked with looking at the policies, rules, and laws that are in place in our government to evaluate whether they’re hindering or enabling U.S. innovation. Smith once wrote that “across our great nation, we’ve begun to see an acceleration of the power of data to deliver value,” and on the one-year anniversary of Patil’s appointment, we spoke with him about how the Obama Administration views the tech industry and how it’s working to make our data more transparent to not only spur innovation, but also move the country forward.

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Above: President Barack Obama holds a precision medicine meeting in the Oval Office, Oct. 3, 2014. (Official White House Photo by Pete Souza)

Opening data of the people to the people

“How unique is it, as a professor of constitutional law, to grasp what it means to have data and understand how transformative data is in this day of age,” Patil remarked, referencing Obama’s comprehension on the enormous stockpile of information his government has on the people.

As part of this effort, the president has removed an obstacle that prevented not only the sharing of data between agencies, but also with the public. In 2013, Obama signed an executive order that mandated government information must now be “open and machine-readable.” For decades, it was the default that data was shared in a PDF, making it difficult for someone to take action on that data.

“How do we ensure that we are staying at the forefront as a country riding this wave [of data]?” Patil asked. “This world is about to change and the government needs to change.”

He cited a study by Harvard professors Raj Chetty, Nathaniel Hendren, and Lawrence Katz which used data from the Internal Revenue Service (IRS) to explore the effects on children in high poverty areas. Among the findings is that when a child is moved to a low poverty area while young, they saw a 40 percent lift in their median income over life.

Patil also referenced the work that his team is doing with the Precision Medicine initiative, a research effort to change how the country improves health and treats disease separate from the Affordable Care Act. He thinks that the use of data and the human genome can be used to ascertain cures for diseases like cancer and this White House program is “pushing the whole ecosystem” forward into the “genetic era.”

And for all the examples he provided during our conversation, his message was quite clear: The U.S. has data and needs the public and even Silicon Valley’s help.

Bringing Silicon Valley together with Washington, D.C.

While many might know Patil as being a member of Obama’s administration, he’s an accomplished entrepreneur and iconic data scientist within the technology industry. In fact, he and Jeff Hammerbacher coined the term “data scientist.” He led the data products and security teams at LinkedIn, was a data scientist in residence at Greylock, and served as vice president of product at RelateIQ, which was acquired by Salesforce in 2014 for $ 392 million.

But he believes there’s a myth that “Silicon Valley is coming to save [Washington] D.C.” when in fact data scientists in the tech industry are coming from the federal government. In fact, Patil started out as an academic at the University of Maryland before working with the Department of Defense (DOD) doing threat anticipation and hunting down bioweapons. “I am forever grateful for that experience and when I had the opportunity to jump into Silicon Valley, those lessons were critical,” he said.

And as the White House pushes to make data more transparent, Patil thinks that more entrepreneurs and tech companies should seize the opportunity to use this new-found data proliferation.

“What I would love to see is a model where people move back and forth more seamlessly, where people are able to do a tour of duty like we’re seeing here in Silicon Valley, spending a couple of years here and then decide that they want to do something for the government, for your local city, for a community outside the industry,” he stated although Patil understands the allure of Silicon Valley can be too much for entrepreneurs to pass up.

Patil believes that the government work isn’t looked at being sexy enough because the government hasn’t done a good enough job to explain its mission: “Over time, it’s gotten harder and harder for a technologist, product manager, designer to get into government.” This is one of the main reasons why Obama created 18F, a digital services consultancy within the government to deploy tools companies can use to build products using public data.

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“These are the ways for people to come in and have the direct impact,” Patil explained.

Secretary of Defense Ashton Carter once told Patil that there was something about waking up every morning and thinking that we’re part of something bigger. And this is what keeps the chief data scientist going, understanding that it’s not about creating the next photo-sharing app or luxury valet service or even an Uber for X, Y, and Z.

It’s about “what’s important for your kids and your kids’ kids,” he said. “There is a quest for happiness out there….And when you’re worked on solving those problems…and come to Silicon Valley, you’re hurting because it’s hard to find a company with a strong mission. We’re going to see a shift where there’s a notion that mission and happiness are more valuable.”

Trust your government

And while it’s easy to say that the government wants more transparency, it’s also understandable that there may be some skepticism, especially from the technology industry. One need only look at the revelations coming from Edward Snowden, companies releasing periodic transparency reports, and even the recent legal battle between the FBI and Apple over access to the iPhone belonging to one of the San Bernardino terrorists.

For Patil, Silicon Valley thinks about protecting the American public from a one-dimensional point of view. He agrees with something Carter once said: “Security is like air: You only realize it when you don’t have it. There are a lot of countries in the world that don’t have security.”

“There is a really important dialogue happening around encryption, security, and cyber,” Patil explained. “The place and the way to make the best progress on this is through that model where people are coming in and out of government more easily, making government more porous. That’s how we make the best decision. The ability for companies that are out here and how they think about cybersecurity is because we’re dealing with an adversary that’s beating us up and we get to see that. The government also sees a different side of the adversary and the more that we share of that, the better we get and the smarter we become.”

As it relates to encryption, he said “the president is very much for strong encryption. The policy is for strong encryption because it’s the most important path forward for cybersecurity. What he has also called for is saying that we are living in a world where we have to work collectively together…Technologists offer a very unique way to have the conversation.”

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Above: President Barack Obama views science exhibits during the 2015 White House Science Fair celebrating student winners of a broad range of science, technology, engineering, and math (STEM) competitions, in the Red Room, March 23, 2015. (Official White House Photo by Chuck Kennedy)

Patil said not to worry about the data policies being undone with the next administration they have seeped into the DNA of agencies, meaning they can’t just be undone. “The long arc of the government has shifted as a result of this president. Because of this, it doesn’t change easily. That only happens with presidential powers of focus,” he said.

He hasn’t thought much about what he’s going to do after he leaves the White House. However, he remains fascinated by all that has been accomplished over the course of Obama’s presidency, citing the launch of the Opportunity project, which uses open data to improve economic mobility for Americans, the White House science fair, a hackathon where New Orleans police chief Michael Harrison worked with a student to write his first line of code so he could access data about his own police department, the creation of a working group around the benefits and risks of artificial intelligence, and more.

He’s convinced that a data-driven government will not only improve the services it offers, but that it can keep the country’s competitive edge, enhance national security, and develop the next generation of technology. And because these policy shifts take time, we won’t see the effects immediately. In the end, Patil believes that as the government moves forward, so too does the nation.

The White House is recognized as the symbol of the President, of the President’s administration, and of the U.S….. All The White House news »

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DJ Patil is the VP of Product at RelateIQ. He coined the term “Data Scientist”. Previously he was the Chief Data Scientist at Greylock Partners and before that he was the Chief Product O… All DJ Patil news »

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Bitcoin creator unmasked: Australian computer scientist Craig Wright says he is Satoshi Nakamoto

After years of speculation, the search for the elusive creator behind the Bitcoin cryptocurrency may have reached a conclusion.

Revealing his identity to the BBC, among a handful of other publications, Australian entrepreneur and computer scientist Dr. Craig Wright today confirmed rumors that first surfaced in December, stating that he is, in fact, Satoshi Nakamoto, the pseudonymous individual who introduced Bitcoin to the world way back in 2009.

Dr Craig Wright 300x223 Bitcoin creator unmasked: Australian computer scientist Craig Wright says he is Satoshi Nakamoto

Above: Dr. Craig Wright

On December 9 2015, Australian police raided the home of Wright, a Sydney-based man fingered by both Gizmodo and Wired magazine as the likely creator of Bitcoin, though the raid itself was reportedly related to tax issues rather than any direct involvement with Bitcoin. Shortly before the police raid, both Gizmodo and Wired received “leaked” documents that strongly suggested Wright had created the leading cryptography-derived payment mechanism, and it looked as though Wright may have deliberately unmasked himself.

It was a curious move for sure, given that the real identity of Satoshi Nakamoto had been deliberately kept under wraps for so long, a mystery that had led to a 64-year-old Californian being wrongly identified as the Bitcoin creator. However, Wright has now directly claimed to be Satoshi Nakamoto and has provided proof to support his claim, with key members of the Bitcoin community also confirming Wright’s claim.

During a meeting with the BBC, Wright “digitally signed” messages using cryptographic keys created during the early days of Bitcoin’s development. “These are the blocks used to send 10 B to Hal Finney in January [2009] as the first Bitcoin transaction,” said Wright during the meeting. Finney, for the record, was a renowned cryptographer and key engineer who helped Wright develop the Bitcoin protocol.

Though there is skepticism over Wright’s claims, experts who have had the opportunity to review the data include Jon Matonis, an economist and one of the founders of the Bitcoin Foundation, who says that he believes Wright’s claims stack up. “During the London proof sessions, I had the opportunity to review the relevant data along three distinct lines: cryptographic, social, and technical,” he said. “It is my firm belief that Craig Wright satisfies all three categories.” The Bitcoin Foundation’s Gavin Andresen has also published a blog post supporting this assertion.

Since Wright was first implicated as the man behind Bitcoin, he has been subjected to scrutiny from the media, and this, according to Wright, is partly why he has decided to come out now. “There are lots of stories out there that have been made up and I don’t like it hurting those people I care about,” he said. “I don’t want any of them to be impacted by this. I have not done this because it is what I wanted. It’s not because of my choice. I really do not want to be the public face of anything.” Wright also said he wasn’t seeking money or fame and that he just wants to work.

Wright has also published this blog post with some admissions and thoughts, as well as further cryptographic “proof” that he is who he says he is. Wright says:

I have been staring at my screen for hours, but I cannot summon the words to express the depth of my gratitude to those that have supported the bitcoin project from its inception – too many names to list. You have dedicated vast swathes of your time, committed your gifts, sacrificed relationships and REM sleep for years to an open source project that could have come to nothing. And yet still you fought. This incredible community’s passion and intellect and perseverance has taken my small contribution and nurtured it, enhanced it, breathed life into it. You have given the world a great gift. Thank you.

Be assured, just as you have worked, I have not been idle during these many years. Since those early days, after distancing myself from the public persona that was Satoshi, I have poured every measure of myself into research. I have been silent, but I have not been absent. I have been engaged with an exceptional group and look forward to sharing our remarkable work when they are ready.

Satoshi is dead.

But this is only the beginning.

According to Wright’s website, he is a “computer scientist, businessman and inventor” born in Brisbane, Australia, in October 1970. He says he studied engineering at the University of Queensland before switching to computer science, and later studied nuclear physics and organic chemistry, as well as gaining a Masters in Statistics, a Masters in Law, and a Doctorate in theology. He has held a number of positions in the IT realm and also claims to have worked at the Australian Stock Exchange, “dealing with security and firewalls.”


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Data science handbook: 3 tips for becoming a data scientist

The impact of data science continues to reverberate through industry, government, and nonprofits. Employers are hunting data science talent, and a surplus of masters programs have sprung up to serve aspiring data acolytes.

But how do you actually enter the field?

To get a clearer picture of the state of data science, how employers and employees alike can take advantage of it, and how you can enter the field, we spoke with some of the field’s most prominent voices: DJ Patil, co-coiner of the term “data scientist”; Michelangelo D’Agostino, formerly of Obama 2012’s data team; and Clare Corthell, creator of The Open Source Data Science Masters.

We’ve distilled their insights into three main pieces of insight that we will share below.

(Their full interviews are available for free online as a part of the Data Science Handbook Pre-Release.)

1. Seek fast, collaborative environments

In all of the unicorn-chasing that goes on when companies try to hire the perfect data scientist, it’s easy to forget the importance of collaboration.

Ultimately, however, Patil said that it is a common mistake for both employers and data scientists to forget that data science is a collaborative endeavor:

People make a mistake by forgetting that Data Science is a team sport. People might point to people like me or (Jeff) Hammerbacher or Hilary (Mason) or Peter Norvig and they say, oh look at these people! It’s false, it’s totally false, there’s not one single data scientist that does it all on their own.

Data science is a team sport. Somebody has to bring the data together, somebody has to move it, someone needs to analyze it, someone needs to be there to bounce ideas around.


The authors have also published 120 Data Science Interview Questions
on VB Insight


Given this, it is far more important for employers to keep in mind that they are hiring to build a team of data scientists who can all work together, and not a cadre of lone wolves.

Aspiring data scientists should seek out such collaborative cultures to maximize their ability to learn, grow, and steep themselves in the culture of teamwork that makes for successful data science.

As Patil concluded:

“In academia, the first thing you do is sit at your desk and then close the door. There’s no door anywhere in Silicon Valley; you’re out on the open floor. These people are very much culture shocked when people tell them, ‘No, you must be working, collaborating, engaging, fighting, debating, rather than hiding behind the desk and the door.’”

2. Delve deeply into hard, dirty problems

The experience of working on difficult problems and the strategies that you use to approach them is one of the most valuable skills that D’Agostino picked up during his astrophysics PhD at UC Berkeley. To get the experience that will ultimately become relevant to data science, D’Agostino suggested:

“Work on a hard problem for a long time and figure out how to push through and not be frustrated when something doesn’t work, because things just don’t work most of the time. You just have to keep trying and keep having faith that you can get a project to work in the end. Even if you try many, many things that don’t work, you can find all the bugs, all the mistakes in your reasoning and logic and push through to a working solution in the end.”

For students, this means you should be always looking for applications of your classwork or research on real, live datasets. This gives you the wisdom of all the nuances when dealing with large, messy datasets and allows you to understand much more than just the theory of your textbook.

D’Agostino explained to us:

“You can read about it, and people can teach you techniques, but until you’ve actually dealt with a nasty data set that has a formatting issue or other problems, you don’t really appreciate what it’s like when you have to merge a bunch of data sets together or make a bunch of graphs to sanity check something and all of a sudden nothing makes sense in your distributions and you have to figure out what’s going on.”

For current practitioners, it means wrangling with the formidable mathematical and engineering challenges that naturally arise in data science. Instead of trusting the standard tools, open the mysterious black boxes of machine learning and figure out for yourself what is going on.

Summarizing this view, D’Agostino concluded:

“For aspiring data scientists, take your time to get your hands dirty by digging deeply into hard data, and mess around with demanding intellectual and engineering problems. The difficulty will stretch your mind and abilities, ultimately growing and improving you.”

3. Bootstrap yourself with projects to demonstrate mastery

There is no single path to becoming a data scientist. Although holding a PhD might be common among data science aspirants, Corthell’s path illustrates it is not the only way.

Corthell was designing for an early-stage startup when she realized that many design decisions could be augmented by data about user behavior. Eventually she left the startup, and she used her newfound free time to think about what she really wanted to do.

“On a long layover in Barcelona, I ordered an espresso and wrote down the technical skills I would need to dissect meta-trends and understand user data. That list laid out 6 months of full-time work, after which I’d really be able to do some damage. This became the Open Source Data Science Masters.”

She decided that she wanted to acquire data science skills to understand the meta-trends among users and designed a curriculum for herself to do so. Corthell bravely embarked on a six-month journey of self-education, and turned her lack of formal work experience as a data scientist into an opportunity to demonstrate her raw determination to become one. She even created visibility for herself by publishing the resources and courses she used as The Open Source Data Science Masters (OSDSM).

In constructing the OSDSM, Corthell sought out and synthesized a wide variety of publicly available online courses, tutorials, and websites. Using these resources, she steeped herself in the nuts and bolts of data science. She set up intermediate projects to test her understanding. Her tenacity won her the attention of many, including the admiration of Mattermark, a startup where Corthell now works as a data scientist.

“As Patient Zero of a new type of Internet-based institution-free education, I didn’t know what to expect. It was impossible to know how I would be judged and whether I would benefit from my experiment. This type of ambiguity usually makes people extremely uncomfortable. It’s like leaving a six-year-old in the library by herself instead of putting her in class with a teacher. What is she going to do?

I knew that it would be a risk, but I took a leap of faith and left myself alone in the library. In the end, the greatest reward didn’t come from the curriculum, it came from what taking a risk demonstrated about me. It led me to a tribe that respected the risk I had taken, and valued the grit that it required to follow through.”

The amount of information that is freely available on the Internet is staggering but surmountable.

Don’t be disheartened if you don’t fit the typical mold of a data scientist; use it as a chance to demonstrate your independence and self-discipline. Take a look at Corthell’s OSDSM and use it as inspiration for navigating your own growth vector.


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IBMer John Cohn. He’s super smart, with 65+ patents to his name.  He’s a reality TV star.  A backyard science experimenter. A regular “Burning Man” attendee. And was on the team that created video gaming chips. All in all, he has more nerd cred than you can shake a Tesla coil at.

And today, at 12.30 EST, he’ll also become a Reddit AMAer, so you can ask John anything!

Just sign in to Reddit and join in on the geek-kingdom conversation.   (You need to have an account to ask John anything, so if you don’t have one, create one here.)

Oh, and don’t forget to ask him about Innovation through Play! See you there!

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