## Archive for the ‘Data Science’ Category

### Data Science Toolbox

Saturday, October 1st, 2016

Data Science Toolbox

From the webpage:

Start doing data science in minutes

As a data scientist, you don’t want to waste your time installing software. Our goal is to provide a virtual environment that will enable you to start doing data science in a matter of minutes.

As a teacher, author, or organization, making sure that your students, readers, or members have the same software installed is not straightforward. This open source project will enable you to easily create custom software and data bundles for the Data Science Toolbox.

A virtual environment for data science

The Data Science Toolbox is a virtual environment based on Ubuntu Linux that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run the Data Science Toolbox either locally (using VirtualBox and Vagrant) or in the cloud (using Amazon Web Services).

We aim to offer a virtual environment that contains the software that is most commonly used for data science while keeping it as lean as possible. After a fresh install, the Data Science Toolbox contains the following software:

• Python, with the following packages: IPython Notebook, NumPy, SciPy, matplotlib, pandas, scikit-learn, and SymPy.
• R, with the following packages: ggplot2, plyr, dplyr, lubridate, zoo, forecast, and sqldf.
• dst, a command-line tool for installing additional bundles on the Data Science Toolbox (see next section).

Let us know if you want to see something added to the Data Science Toolbox.

Great resource for doing or teaching data science!

And an example of using a VM to distribute software in a learning environment.

### Data Science Series [Starts 9 September 2016 but not for *nix users]

Sunday, September 4th, 2016

From the webpage:

Every Friday beginning September 9, 2016
9am – 10am Pacific Time

Working jointly with the BD2K Centers-Coordination Center (BD2KCCC) and the NIH Office of Data Science, the BD2K Training Coordinating Center (TCC) is spearheading this virtual lecture series on the data science underlying modern biomedical research. Beginning in September 2016, the seminar series will consist of regularly scheduled weekly webinar presentations covering the basics of data management, representation, computation, statistical inference, data modeling, and other topics relevant to “big data” biomedicine. The seminar series will provide essential training suitable for individuals at all levels of the biomedical community. All video presentations from the seminar series will be streamed for live viewing, recorded, and posted online for future viewing and reference. These videos will also be indexed as part of TCC’s Educational Resource Discovery Index (ERuDIte), shared/mirrored with the BD2KCCC, and with other BD2K resources.

View all archived videos on our YouTube channel:
https://www.youtube.com/channel/UCKIDQOa0JcUd3K9C1TS7FLQ

Please join our weekly meetings from your computer, tablet or smartphone.
https://global.gotomeeting.com/join/786506213
You can also dial in using your phone.
United States +1 (872) 240-3311
Access Code: 786-506-213
First GoToMeeting? Try a test session: http://help.citrix.com/getready

Of course, running Ubuntu, when I follow the “First GoToMeeting? Try a test session,” I get this result:

OS not supported

Long-Term Fix: Upgrade your computer.

You or your IT Admin will need to upgrade your computer’s operating system in order to install our desktop software at a later date.

Since this is most likely a lecture format, could just stream the video and use WebConf as a Q/A channel.

Of course, that would mean losing the various technical difficulties, licensing fees, etc., all of which are distractions from the primary goal of the project.

But who wants that?

PS: Most *nix users won’t be interested except to refer others but still, over engineered solutions to simple issues should not be encouraged.

### DataScience+ (R Tutorials)

Monday, August 29th, 2016

DataScience+

From the webpage:

We share R tutorials from scientists at academic and scientific institutions with a goal to give everyone in the world access to a free knowledge. Our tutorials cover different topics including statistics, data manipulation and visualization!

I encountered DataScience+ while running down David Kun’s RDBL post.

As of today, there are 120 tutorials with 451,129 reads.

That’s impressive! Whether you are looking for tutorials or you are looking to post your R tutorial where it will be appreciated.

Enjoy!

### The Ethics of Data Analytics

Sunday, August 21st, 2016

The Ethics of Data Analytics by Kaiser Fung.

Twenty-one slides on ethics by Kaiser Fung, author of: Junk Charts (data visualization blog), and Big Data, Plainly Spoken (comments on media use of statistics).

Fung challenges you to reach your own ethical decisions and acknowledges there are a number of guides to such decision making.

Unfortunately, Fung does not include professional responsibility requirements, such as the now out-dated Canon 7 of the ABA Model Code Of Professional Responsibility:

A Lawyer Should Represent a Client Zealously Within the Bounds of the Law

That canon has a much storied history, which is capably summarized in Whatever Happened To ‘Zealous Advocacy’? by Paul C. Sanders.

In what became known as Queen Caroline’s Case, the House of Lords sought to dissolve the marriage of King George the IV

on the grounds of her adultery. Effectively removing her as queen of England.

Queen Caroline was represented by Lord Brougham, who had evidence of a secret prior marriage by King George the IV to Catholic (which was illegal), Mrs Fitzherbert.

Brougham’s speech is worth your reading in full but the portion most often cited for zealous defense reads as follows:

I once before took leave to remind your lordships — which was unnecessary, but there are many whom it may be needful to remind — that an advocate, by the sacred duty of his connection with his client, knows, in the discharge of that office, but one person in the world, that client and none other. To save that client by all expedient means — to protect that client at all hazards and costs to all others, and among others to himself — is the highest and most unquestioned of his duties; and he must not regard the alarm, the suffering, the torment, the destruction, which he may bring upon any other; nay, separating even the duties of a patriot from those of an advocate, he must go on reckless of the consequences, if his fate it should unhappily be, to involve his country in confusion for his client.

The name Mrs. Fitzherbert never slips Lord Brougham’s lips but the House of Lords has been warned that may not remain to be the case, should it choose to proceed. The House of Lords did grant the divorce but didn’t enforce it. Saving fact one supposes. Queen Caroline died less than a month after the coronation of George IV.

For data analysis, cybersecurity, or any of the other topics I touch on in this blog, I take the last line of Lord Brougham’s speech:

To save that client by all expedient means — to protect that client at all hazards and costs to all others, and among others to himself — is the highest and most unquestioned of his duties; and he must not regard the alarm, the suffering, the torment, the destruction, which he may bring upon any other; nay, separating even the duties of a patriot from those of an advocate, he must go on reckless of the consequences, if his fate it should unhappily be, to involve his country in confusion for his client.

as the height of professionalism.

Post-engagement of course.

If ethics are your concern, have that discussion with your prospective client before you are hired.

Otherwise, clients have goals and the task of a professional is how to achieve them. Nothing more.

### Contributing to StackOverflow: How Not to be Intimidated

Friday, August 19th, 2016

From the post:

StackOverflow is an essential resource for programmers. Whether you run into a bizarre and scary error message or you’re blanking on something you should know, StackOverflow comes to the rescue. Its popularity with coders spurred many jokes and memes. (Programming to be Officially Renamed “Googling Stackoverflow,” a satirical headline reads).

(image omitted)

While all of us are users of StackOverflow, contributing to this knowledge base can be very intimidating, especially to beginners or to non-traditional coders who many already feel like they don’t belong. The fact that an invisible barrier exists is a bummer because being an active contributor not only can help with your job search and raise your profile, but also make you a better programmer. Explaining technical concepts in an accessible way is difficult. It is also well-established that teaching something solidifies your knowledge of the subject. Answering StackOverflow questions is great practice.

All of the benefits of being an active member of StackOverflow were apparent to me for a while, but I registered an account only this week. Let me walk you t[h]rough thoughts that hindered me. (Chances are, you’ve had them too!)

I plead guilty to using StackOverFlow but not contributing back to it.

Another “intimidation” to avoid is thinking you must have the complete and killer answer to any question.

That can and does happen, but don’t wait for a question where you can supply such an answer.

Jump in! (Advice to myself as well as any readers.)

### Pandas

Wednesday, August 17th, 2016

From the post:

Serious practitioners of data science use the full scientific method, starting with a question and a hypothesis, followed by an exploration of the data to determine whether the hypothesis holds up. But in many cases, such as when you aren’t quite sure what your data contains, it helps to perform some exploratory data analysis—just looking around, trying to see if you can find something.

And, that’s what I’m going to cover here, using tools provided by the amazing Python ecosystem for data science, sometimes known as the SciPy stack. It’s hard to overstate the number of people I’ve met in the past year or two who are learning Python specifically for data science needs. Back when I was analyzing data for my PhD dissertation, just two years ago, I was told that Python wasn’t yet mature enough to do the sorts of things I needed, and that I should use the R language instead. I do have to wonder whether the tables have turned by now; the number of contributors and contributions to the SciPy stack is phenomenal, making it a more compelling platform for data analysis.

In my article “Analyzing Data“, I described how to filter through logfiles, turning them into CSV files containing the information that was of interest. Here, I explain how to import that data into Pandas, which provides an additional layer of flexibility and will let you explore the data in all sorts of ways—including graphically. Although I won’t necessarily reach any amazing conclusions, you’ll at least see how you can import data into Pandas, slice and dice it in various ways, and then produce some basic plots.

Of course, scientific articles are written as though questions drop out of the sky and data is interrogated for the answer.

Aside from being rhetoric to badger others with, does anyone really think that is how science operates in fact?

Whether you have delusions about how science works in fact or not, you will find that Pandas will assist you in exploring data.

### Ten Simple Rules for Effective Statistical Practice

Sunday, June 12th, 2016

Ten Simple Rules for Effective Statistical Practice by Robert E. Kass, Brian S. Caffo, Marie Davidian, Xiao-Li Meng, Bin Yu, Nancy Reid (Ciation: Kass RE, Caffo BS, Davidian M, Meng X-L, Yu B, Reid N (2016) Ten Simple Rules for Effective Statistical Practice. PLoS Comput Biol 12(6): e1004961. doi:10.1371/journal.pcbi.1004961)

From the post:

Several months ago, Phil Bourne, the initiator and frequent author of the wildly successful and incredibly useful “Ten Simple Rules” series, suggested that some statisticians put together a Ten Simple Rules article related to statistics. (One of the rules for writing a PLOS Ten Simple Rules article is to be Phil Bourne [1]. In lieu of that, we hope effusive praise for Phil will suffice.)

I started to copy out the “ten simple rules,” sans the commentary but that would be a disservice to my readers.

Nodding past a ten bullet point listing isn’t going to make your statistics more effective.

Re-write the commentary on all ten rules to apply them to every project. The focusing of the rules on your work will result in specific advice and examples for your field.

Who knows? Perhaps you will be writing a ten simple rule article in your specific field, sans Phil Bourne as a co-author. (Do be sure and cite Phil.)

PS: For the curious: Ten Simple Rules for Writing a PLOS Ten Simple Rules Article by Harriet Dashnow, Andrew Lonsdale, Philip E. Bourne.

### Reboot Your $100+ Million F-35 Stealth Jet Every 10 Hours Instead of 4 (TM Fusion) Wednesday, April 27th, 2016 Pentagon identifies cause of F-35 radar software issue From the post: The Pentagon has found the root cause of stability issues with the radar software being tested for the F-35 stealth fighter jet made by Lockheed Martin Corp, U.S. Defense Acquisition Chief Frank Kendall told a congressional hearing on Tuesday. Last month the Pentagon said the software instability issue meant the sensors had to be restarted once every four hours of flying. Kendall and Air Force Lieutenant General Christopher Bogdan, the program executive officer for the F-35, told a Senate Armed Service Committee hearing in written testimony that the cause of the problem was the timing of “software messages from the sensors to the main F-35” computer. They added that stability issues had improved to where the sensors only needed to be restarted after more than 10 hours. “We are cautiously optimistic that these fixes will resolve the current stability problems, but are waiting to see how the software performs in an operational test environment,” the officials said in a written statement. … (emphasis added) At$100+ Million plane that requires rebooting every ten hours? I’m not a pilot but that sounds like a real weakness.

The precise nature of the software glitch isn’t described but you can guess one of the problems from Lockheed Martin’s, Software You Wish You Had: Inside the F-35 Supercomputer:

The human brain relies on five senses—sight, smell, taste, touch and hearing—to provide the information it needs to analyze and understand the surrounding environment.

Similarly, the F-35 relies on five types of sensors: Electronic Warfare (EW), Radar, Communication, Navigation and Identification (CNI), Electro-Optical Targeting System (EOTS) and the Distributed Aperture System (DAS). The F-35 “brain”—the process that combines this stellar amount of information into an integrated picture of the environment—is known as sensor fusion.

At any given moment, fusion processes large amounts of data from sensors around the aircraft—plus additional information from datalinks with other in-air F-35s—and combines them into a centralized view of activity in the jet’s environment, displayed to the pilot.

In everyday life, you can imagine how useful this software might be—like going out for a jog in your neighborhood and picking up on real-time information about obstacles that lie ahead, changes in traffic patterns that may affect your route, and whether or not you are likely to pass by a friend near the local park.

F-35 fusion not only combines data, but figures out what additional information is needed and automatically tasks sensors to gather it—without the pilot ever having to ask.
… (emphasis added)

The fusion of data from other in-air F-35s is a classic topic map merging of data problem.

You have one subject, say an anti-aircraft missile site, seen from up to four (in the F-35 specs) F-35s. As is the habit of most physical objects, it has only one geographic location but the fusion computer for the F-35 doesn’t come up with than answer.

“When you have two, three or four F-35s looking at the same threat, they don’t all see it exactly the same because of the angles that they are looking at and what their sensors pick up,” Bogdan told reporters Tuesday. “When there is a slight difference in what those four airplanes might be seeing, the fusion model can’t decide if it’s one threat or more than one threat. If two airplanes are looking at the same thing, they see it slightly differently because of the physics of it.”

For example, if a group of F-35s detect a single ground threat such as anti-aircraft weaponry, the sensors on the planes may have trouble distinguishing whether it was an isolated threat or several objects, Bogdan explained.

As a result, F-35 engineers are working with Navy experts and academics from John’s Hopkins Applied Physics Laboratory to adjust the sensitivity of the fusion algorithms for the JSF’s 2B software package so that groups of planes can correctly identify or discern threats.

“What we want to have happen is no matter which airplane is picking up the threat – whatever the angles or the sensors – they correctly identify a single threat and then pass that information to all four airplanes so that all four airplanes are looking at the same threat at the same place,” Bogdan said.

Unless Bogdan is using “sensitivity” in a very unusual sense, that doesn’t sound like the issue with the fusion computer of the F-35.

Rather the problem is the fusion computer has no explicit doctrine of subject identity to use when it is merging data from different F-35s, whether it be two, three, four or even more F-35s. The display of tactical information should be seamless to the pilot and without human intervention.

I’m sure members of Congress were impressed with General Bogdan using words like “angles” and “physics,” but the underlying subject identity issue isn’t hard to address.

At issue is the location of a potential target on the ground. Within some pre-defined metric, anything located within a given area is the “same target.”

The Air Force has already paid for this type of analysis and the mathematics of what is called Circular Error Probability (CEP) has been published in Use of Circular Error Probability in Target Detection by William Nelson (1988).

You need to use the “current” location of the detecting aircraft, allowances for inaccuracy in estimating the location of the target, etc., but once you call out the subject identity as an issue, its a matter of making choices of how accurate you want the subject identification to be.

Before you forward this to Gen. Bogdan as a way forward on the fusion computer, realize that CEP is only one aspect of target identification. But, calling the subject identity of targets out explicitly, enables reliable presentation of single/multiple targets to pilots.

Your call, confusing displays or a reliable, useful display.

PS: I assume military subject identity systems would not be running XTM software. Same principles apply even if the syntax is different.

### Women in Data Science (~632) – Twitter List

Monday, April 25th, 2016

Data Science Renee has a twitter list of approximately 632 women in data science.

I say “approximately” because when I first saw her post about the list it had 630 members. When I looked this AM, it had 632 members. By the time you look, that number will be different again.

If you are making a conscious effort to seek a diversity of speakers for your next data science conference, it should be on your list of sources.

Enjoy!

### 4330 Data Scientists and No Data Science Renee

Monday, April 11th, 2016

After I posted 1880 Big Data Influencers in CSV File, I got a tweet from Data Science Renee pointing out that her name wasn’t in the list.

Renee does a lot more on “data science” and not so much on “big data,” which sounded like a plausible explanation.

Even if “plausible,” I wanted to know if there was some issue with my scrapping of Right Relevance.

Knowing that Renee’s influence score for “data science” is 81, I set the query to scrape the list between 65 and 98, just to account for any oddities in being listed.

The search returned 1832 entries. Search for Renee, nada, no got. Here’s the 1832-data-science-list.

In an effort to scrape all the listings, which should be 10,375 influencers, I set the page delay up to Ted Cruz reading speed. Ten entries every 72,000 milliseconds. 😉

That resulted in 4330-data-science-list.

No joy, no Renee!

It isn’t clear to me why my scraping fails before recovering the entire data set but in any reasonable sort order, a listing of roughly 10K data scientists should have Renee in the first 100 entries, much less the first 1,000 or even first 4K.

Something is clearly amiss with the data but what?

Check me on the first ten entries for data science as the search term but I find:

• Hilary Mason
• Kirk Borne – no data science
• Nathan Yau
• Gregory Piatetsky – no data science
• Randy Olson
• Jeff Hammerbacher – no data science
• Chris Dixon @cdixon – no data science
• dj patil @dpatil
• Doug Laney – no data science
• Big Data Science no data science

The notation, “no data science,” means that entry does not have a label for data science. Odd considering that my search was specifically for influencers in “data science.” The same result obtains if you choose one of the labels instead of searching. (I tried.)

Clearly all of these people could be listed for “data science,” but if I am searching for that specific category, why is that missing from six of the first ten “hits?”

As far as Data Science Renee, I can help you with that to a degree. Follow @BecomingDataSci, or @DataSciGuide, @DataSciLearning & @NewDataSciJobs. Visit her website: http://t.co/zv9NrlxdHO. Podcasts, interviews, posts, just a hive of activity.

On the mysteries of Right Relevance and its data I’m not sure what to say. I posted feedback a week ago mentioning the issue with scraping and ordering, but haven’t heard back.

The site has a very clever idea but looking in from the outside with a sample size of 1, I’m not impressed with its delivery on that idea.

Issues I don’t know about with Web Scraper?

If you have contacts with Right Relevance could you gently ping them for me? Thanks!

### Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Thursday, April 7th, 2016

From the description at Amazon:

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O’Neil reveals in this shocking book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his race or neighborhood), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.

Tracing the arc of a person’s life, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops. They don’t simply describe reality, as proponents claim, they change reality, by expanding or limiting the opportunities people have. O’Neil calls on modelers to take more responsibility for how their algorithms are being used. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

Even if you have qualms about Cathy’s position, you have to admit that is a great book cover!

When I was in law school, I had F. Hodge O’Neal for corporation law. He is the O’Neal in O’Neal and Thompson’s Oppression of Minority Shareholders and LLC Members, Rev. 2d.

The publisher’s blurb is rather generous in saying:

Cited extensively, O’Neal and Thompson’s Oppression of Minority Shareholders and LLC Members shows how to take appropriate steps to protect minority shareholder interests using remedies, tactics, and maneuvers sanctioned by federal law. It clarifies the underlying cause of squeeze-outs and suggests proven arrangements for avoiding them.

You could read Oppression of Minority Shareholders and LLC Members that way but when corporate law is taught with war stories from the antics of the robber barons forward, you get the impression that isn’t why people read it.

Not that I doubt Cathy’s sincerity, on the contrary, I think she is very sincere about her warnings.

Where I disagree with Cathy is in thinking democracy is under greater attack now or that inequality is any greater problem than before.

carefully, you will leave it with deep uncertainty about the relationship of American government, federal, state and local to any recognizable concept of democracy. Or for that matter to the “equality” of its citizens.

Unlike Cathy as well, I don’t expect that shaming people is going to result in “better” or more “honest” data analysis.

What you can do is arm yourself to do battle on behalf of your “side,” both in terms of exposing data manipulation by others and concealing your own.

Perhaps there is room in the marketplace for a book titled: Suppression of Unfavorable Data. More than hiding data, what data to not collect? How to explain non-collection/loss? How to collect data in the least useful ways?

You would have to write it as a how to avoid these very bad practices but everyone would know what you meant. Could be the next business management best seller.

### Avoid “Complete,” “Data Science,” in Titles

Tuesday, March 1st, 2016

This is a useful tutorial but it isn’t:

1. Complete
2. Does NOT cover all of Data Science

But, this tutorial was tweeted and has been retweeted at least seven times that I know of, possibly more.

Using vague and/or inaccurate terms in titles makes tutorials more difficult to find.

That alone should be reason enough to use better titles.

A more accurate title would be:

R for Predictive Modeling, From Installation to Modeling

That captures the use of R, that the main focus is on predictive modeling and that it will start with the installation of R and proceed to modeling.

Not a word said about all of “data science,” or being “complete,” whatever that means in a discipline with daily advances on multiple fronts.

Just a little effort on the part of authors could improve the lives of all of us desperately searching to find their work.

Yes?

### Streaming 101 & 102 – [Stream Processing with Batch Identities?]

Sunday, February 21st, 2016

From part 1:

Streaming data processing is a big deal in big data these days, and for good reasons. Amongst them:

• Businesses crave ever more timely data, and switching to streaming is a good way to achieve lower latency.
• The massive, unbounded data sets that are increasingly common in modern business are more easily tamed using a system designed for such never-ending volumes of data.
• Processing data as they arrive spreads workloads out more evenly over time, yielding more consistent and predictable consumption of resources.

Despite this business-driven surge of interest in streaming, the majority of streaming systems in existence remain relatively immature compared to their batch brethren, which has resulted in a lot of exciting, active development in the space recently.

Since I have quite a bit to cover, I’ll be splitting this across two separate posts:

1. Streaming 101: This first post will cover some basic background information and clarify some terminology before diving into details about time domains and a high-level overview of common approaches to data processing, both batch and streaming.
2. The Dataflow Model: The second post will consist primarily of a whirlwind tour of the unified batch + streaming model used by Cloud Dataflow, facilitated by a concrete example applied across a diverse set of use cases. After that, I’ll conclude with a brief semantic comparison of existing batch and streaming systems.

The world beyond batch: Streaming 102

In this post, I want to focus further on the data-processing patterns from last time, but in more detail, and within the context of concrete examples. The arc of this post will traverse two major sections:

• Streaming 101 Redux: A brief stroll back through the concepts introduced in Streaming 101, with the addition of a running example to highlight the points being made.
• Streaming 102: The companion piece to Streaming 101, detailing additional concepts that are important when dealing with unbounded data, with continued use of the concrete example as a vehicle for explaining them.

By the time we’re finished, we’ll have covered what I consider to be the core set of principles and concepts required for robust out-of-order data processing; these are the tools for reasoning about time that truly get you beyond classic batch processing.

You should also catch the paper by Tyler and others, The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing.

Cloud Dataflow, known as Beam at the Apache incubator, offers a variety of operations for combining and/or merging collections of values in data.

I mention that because I would hate to hear of you doing stream processing with batch identities. You know, where you decide on some fixed set of terms and those are applied across dynamic data.

Hmmm, fixed terms applied to dynamic data. Doesn’t even sound right does it?

Sometimes, fixed terms (read schema, ontology) are fine but in linguistically diverse environments (read real life), that isn’t always adequate.

Enjoy the benefits of stream processing but don’t artificially limit them with batch identities.

I first saw this in a tweet by Bob DuCharme.

### People NOT Technology Produce Data ROI

Monday, February 15th, 2016

From the webpage:

Don’t let your enterprise make the expensive mistake of thinking that buying tons of proprietary tools will solve your data analytics challenges.

tl;dr = The enterprise needs to invest in core data science skills, not proprietary tools.

Most of the world’s largest corporations are flush with data, but frequently still struggle to achieve the vast performance increases promised by the hype around so called “big data.” It’s not that the excitement around the potential of harvesting all that data was unwarranted, but rather these companies are finding that translating data into information and ultimately tangible value can be hard… really hard.

In your typical new tech-based startup the entire computing ecosystem was likely built from day one around the need to generate, store, analyze and create value from data. That ecosystem was also likely backed from day one with a team of qualified data scientists. Such ecosystems spawned a wave of new data science technologies that have since been productized into tools for sale. Backed by mind-blowingly large sums of VC cash many of these tools have set their eyes on the large enterprise market. A nice landscape of such tools was recently prepared by Matt Turck of FirstMark Capital (host of Data Driven NYC, one of the best data science meetups around).

Consumers stopped paying money for software a long time ago (they now mostly let the advertisers pay for the product). If you want to make serious money in pure software these days you have to sell to the enterprise. Large corporations still spend billions and billions every year on software and data science is one of the hottest areas in tech right now, so selling software for crunching data should be a no-brainer! Not so fast.

The problem is, the enterprise data environment is often nothing like that found within your typical 3-year-old startup. Data can be strewn across hundreds or thousands of systems that don’t talk to each other. Devices like mainframes are still common. Vast quantities of data are generated and stored within these companies, but until recently nobody ever really envisioned ever accessing — let alone analyzing — these archived records. Often, it’s not initially even clear how the all data generated by these systems directly relates to a large blue chip’s core business operations. It does, but a lack of in-house data scientists means that nobody is entirely even sure what data is really there or how it can be leveraged.

I would delete “proprietary” from the above because non-proprietary tools create data problems just as easily.

Thus I would re-write the second quote as:

Tools won’t replace skilled talent, and skilled talent doesn’t typically need many particular tools.

I substituted “particular” tools to avoid religious questions about particular non-proprietary tools.

Understanding data, recognizing where data integration is profitable and where it is a dead loss, creating tests to measure potential ROI, etc., are all tasks of a human data analyst and not any proprietary or non-proprietary tool.

That all enterprise data has some intrinsic value that can be extracted if it were only accessible is an article of religious faith, not business ROI.

If you want business ROI from data, start with human analysts and not the latest buzzwords in technological tools.

### Agile Data Science [Free Download]

Tuesday, February 9th, 2016

Agile Data Science by Russell Jurney.

From the preface:

I wrote this book to get over a failed project and to ensure that others do not repeat my mistakes. In this book, I draw from and reflect upon my experience building analytics applications at two Hadoop shops.

Agile Data Science has three goals: to provide a how-to guide for building analyticsapplications with big data using Hadoop; to help teams collaborate on big data projectsin an agile manner; and to give structure to the practice of applying Agile Big Data analytics in a way that advances the field.

From 2013 and data science has moved quite a bit in the meantime but the principles Russell illustrates remain sound and people do still use Hadoop.

Depending on what you gave up for Lent, you should have enough non-work time to work through Agile Data Science by the end of Lent.

Maybe this year you will have something to show for the forty days of Lent. 😉

### The Danger of Ad Hoc Data Silos – Discrediting Government Experts

Monday, February 8th, 2016

From the post:

Four years ago, Yvonne Marchand lost custody of her daughter.

Even though child services found no proof that she was a negligent parent, that didn’t count for much against the overwhelmingly positive results from a hair test. The lab results said she was abusing alcohol on a regular basis and in enormous quantities.

The test results had all the trappings of credible forensic science, and was presented by a technician from the Motherisk Drug Testing Laboratory at Toronto’s Sick Kids Hospital, Canada’s foremost children’s hospital.

“I told them they were wrong, but they didn’t believe me. Nobody would listen,” Marchand recalls.

Motherisk hair test results indicated that Marchand had been downing 48 drinks a day, for 90 days. “If you do the math, I would have died drinking that much” Marchand says. “There’s no way I could function.”

The court disagreed, and determined Marchand was unfit to have custody of her daughter.

Some parents, like Marchand, pursued additional hair tests from independent labs in a bid to fight their cases. Marchand’s second test showed up as negative. But, because the lab technician couldn’t testify as an expert witness, the second test was thrown out by the court.

Marchand says the entire process was very frustrating. She says someone should have noticed a pattern when parents repeatedly presented hair test results from independent labs which completely contradicted Motherisk results. Alarm bells should have gone off sooner.

Tess’ post and a 366-page report make it clear that Motherisk has impaired the fairness of a large number of child-protection service cases.

Child services, the courts, state representatives, the only one would would have been aware of contradictions of Motherisk results over multiple cases, had not interest in “connecting the dots.”

Each case, with each attorney, was an ad hoc data silo that could not present the pattern necessary to challenge the systematic poor science from Motherisk.

The point is that not all data silos are in big data or nation-state sized intelligence services. Data silos can and do regularly have tragic impact upon ordinary citizens.

Privacy would be an issue but mechanisms need to be developed where lawyers and other advocates can share notice of contradiction of state agencies so that patterns such as by Motherisk can be discovered, documented and hopefully ended sooner rather than later.

BTW, there is an obvious explanation for why:

“No forensic toxicology laboratory in the world uses ELISA testing the way [Motherisk] did.”

Child services did not send hair samples to Motherisk to decide whether or not to bring proceedings.

Child services had already decided to remove children and sent hair samples to Motherisk to bolster their case.

How bright did Motherisk need to be to realize that positive results were expected outcome?

Does your local defense bar collect data on police/state forensic experts and their results?

Looking for suggestions?

### Clojure for Data Science [Caution: Danger of Buyer’s Regret]

Saturday, February 6th, 2016

Clojure for Data Science by Mike Anderson.

From the webpage:

Presentation given at the Jan 2016 Singapore Clojure Users’ Group

You will have to work at the presentation because there is no accompanying video, but the effort will be well spent.

Before you review these slides or pass them onto others, take fair warning that you may experience “buyer’s regret” with regard to your current programming language/paradigm (if not already Clojure).

However powerful and shiny your present language seems now, its luster will be dimmed after scanning over this slides.

Don’t say you weren’t warned ahead of time!

BTW, if you search for “clojure for data science” (with the quotes) you will find among other things:

Clojure for Data Science Progressing by Henry Garner (Packt)

Repositories for the Clojure for Data Science Processing book.

@cljds Clojure Data Science twitter feed (Henry Garner). VG!

Clojure for Data Science Some 151 slides by Henry Garner.

Plus:

Planet Clojure, a metablog that collects posts from other Clojure blogs.

As a close friend says from time to time, “clojure for data science,”

G*****s well.” 😉

Enjoy!

### The Ethical Data Scientist

Thursday, February 4th, 2016

From the post:

….
After the financial crisis, there was a short-lived moment of opportunity to accept responsibility for mistakes with the financial community. One of the more promising pushes in this direction was when quant and writer Emanuel Derman and his colleague Paul Wilmott wrote the Modeler’s Hippocratic Oath, which nicely sums up the list of responsibilities any modeler should be aware of upon taking on the job title.

The ethical data scientist would strive to improve the world, not repeat it. That would mean deploying tools to explicitly construct fair processes. As long as our world is not perfect, and as long as data is being collected on that world, we will not be building models that are improvements on our past unless we specifically set out to do so.

At the very least it would require us to build an auditing system for algorithms. This would be not unlike the modern sociological experiment in which job applications sent to various workplaces differ only by the race of the applicant—are black job seekers unfairly turned away? That same kind of experiment can be done directly to algorithms; see the work of Latanya Sweeney, who ran experiments to look into possible racist Google ad results. It can even be done transparently and repeatedly, and in this way the algorithm itself can be tested.

The ethics around algorithms is a topic that lives only partly in a technical realm, of course. A data scientist doesn’t have to be an expert on the social impact of algorithms; instead, she should see herself as a facilitator of ethical conversations and a translator of the resulting ethical decisions into formal code. In other words, she wouldn’t make all the ethical choices herself, but rather raise the questions with a larger and hopefully receptive group.

First, the link for the Modeler’s Hippocratic Oath takes you to a splash page at Wiley for Derman’s book: My Life as a Quant: Reflections on Physics and Finance.

The Financial Modelers’ Manifesto (PDF) and The Financial Modelers’ Manifesto (HTML), are valid links as of today.

I commend the entire text of The Financial Modelers’ Manifesto to you for repeated reading but for present purposes, let’s look at the Modelers’ Hippocratic Oath:

~ I will remember that I didn’t make the world, and it doesn’t satisfy my equations.

~ Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.

~ I will never sacrifice reality for elegance without explaining why I have done so.

~ Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.

~ I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension

It may just be me but I don’t see a charge being laid on data scientists to be the ethical voices in organizations using data science.

Do you see that charge?

To to put it more positively, aren’t other members of the organization, accountants, engineers, lawyers, managers, etc., all equally responsible for spurring “ethical conversations?” Why is this a peculiar responsibility for data scientists?

I take a legal ethics view of the employer – employee/consultant relationship. The client is the ultimate arbiter of the goal and means of a project, once advised of their options.

Their choice may or may not be mine but I haven’t ever been hired to play the role of Jiminy Cricket.

It’s heady stuff to be responsible for bringing ethical insights to the clueless but sometimes the clueless have ethical insights on their on, or not.

Data scientists can and should raise ethical concerns but no more or less than any other member of a project.

As you can tell from reading this blog, I have very strong opinions on a wide variety of subjects. That said, unless a client hires me to promote those opinions, the goals of the client, by any legal means, are my only concern.

PS: Before you ask, no, I would not work for Donald Trump. But that’s not an ethical decision. That’s simply being a good citizen of the world.

### 9 “Laws” for Data Mining [Be Careful With #5]

Saturday, January 30th, 2016

9 “Laws” for Data Mining

A Forbes piece on “laws” for data mining, that are equally applicable to data science.

Being Forbes, technology is valuable because it has value for business, not because “everyone is doing it,” “it’s really cool technology,” “it’s a graph,” or “it will bring all humanity to a new plane of existence.”

To be honest, Forbes is a welcome relief some days.

But even Forbes stumbles, as with law #5:

5. There are always patterns: In practice, your data always holds useful information to support decision-making and action.

What? “…your data always holds useful information to support decision-making and action.

That’s as nutty as the “new plane of existence” stuff.

When I say “nutty,” I mean that in a professional sense. The term apohenia was coined to label the tendency to see meaningful patterns in random data. (Yes, that includes your data.) Apophenia.

The original work described the “…onset of delusional thinking in pyschosis.”

No doubt you will find patterns in your data but that the patterns “…holds useful information to support decision-making and action” isn’t a given.

That is an echo of the near fanatic belief that if businesses used big data, they would be more profitable.

Most of the other “laws” are more plausible than #5, but even there, don’t abandon your judgement even if Forbes says that something is so.

I first saw this in a tweet by Data Science Renee.

### Improve Your Data Literacy: 16 Blogs to Follow in 2016

Friday, January 22nd, 2016

From the post:

Learning data literacy is a never-ending process. Going to workshops and hands-on practice are important, but to really become acquainted with the “culture” of data literacy, you’ll have to do a lot of reading. Don’t worry, we’ve got your back: below is a curated list of 16 blogs to follow in 2016 if you want to: improve your data-visualisation skills; see the best examples of data journalism; discover the methodology behind the best data-driven projects; and pick-up some essential tips for working with data.

There are aggregated feeds to add to Feedly but it would have been more convenience to have one collection for all the feeds.

As you add feeds to Feedly or elsewhere, you will quickly find there are more feeds and stories than hours in the day.

The open question is how much data curation is required to make a viable publication? There are lots of lists, some with more or less comments, but what level of detail is required to create a financially viable publication?

### Data Science Ethics: Who’s Lying to Hillary Clinton?

Sunday, December 20th, 2015

The usual ethics example for data science involves discrimination against some protected class. Discrimination on race, religion, ethnicity, etc., most if not all of which is already illegal.

That’s not a question of ethics, that’s a question of staying out of jail.

A better ethics example is to ask: Who’s lying to Hillary Clinton about back doors for encryption?

I ask because in the debate on December 19, 2015, Hillary says:

Secretary Clinton, I want to talk about a new terrorist tool used in the Paris attacks, encryption. FBI Director James Comey says terrorists can hold secret communications which law enforcement cannot get to, even with a court order.

You’ve talked a lot about bringing tech leaders and government officials together, but Apple CEO Tim Cook said removing encryption tools from our products altogether would only hurt law-abiding citizens who rely on us to protect their data. So would you force him to give law enforcement a key to encrypted technology by making it law?

CLINTON: I would not want to go to that point. I would hope that, given the extraordinary capacities that the tech community has and the legitimate needs and questions from law enforcement, that there could be a Manhattan-like project, something that would bring the government and the tech communities together to see they’re not adversaries, they’ve got to be partners.

It doesn’t do anybody any good if terrorists can move toward encrypted communication that no law enforcement agency can break into before or after. There must be some way. I don’t know enough about the technology, Martha, to be able to say what it is, but I have a lot of confidence in our tech experts.

And maybe the back door is the wrong door, and I understand what Apple and others are saying about that. But I also understand, when a law enforcement official charged with the responsibility of preventing attacks — to go back to our early questions, how do we prevent attacks — well, if we can’t know what someone is planning, we are going to have to rely on the neighbor or, you know, the member of the mosque or the teacher, somebody to see something.

CLINTON: I just think there’s got to be a way, and I would hope that our tech companies would work with government to figure that out. Otherwise, law enforcement is blind — blind before, blind during, and, unfortunately, in many instances, blind after.

So we always have to balance liberty and security, privacy and safety, but I know that law enforcement needs the tools to keep us safe. And that’s what i hope, there can be some understanding and cooperation to achieve.

Who do you think has told Secretary Clinton there is a way to have secure encryption and at the same time enable law enforcement access to encrypted data?

That would be a data scientist or someone posing as a data scientist. Yes?

I assume you have read: Keys Under Doormats: Mandating Insecurity by Requiring Government Access to All Data and Communications by H. Abelson, R. Anderson, S. M. Bellovin, J. Benaloh, M. Blaze, W. Diffie, J. Gilmore, M. Green, S. Landau, P. G. Neumann, R. L. Rivest, J. I. Schiller, B. Schneier, M. Specter, D. J. Weitzner.

Abstract:

Twenty years ago, law enforcement organizations lobbied to require data and communication services to engineer their products to guarantee law enforcement access to all data. After lengthy debate and vigorous predictions of enforcement channels “going dark,” these attempts to regulate security technologies on the emerging Internet were abandoned. In the intervening years, innovation on the Internet flourished, and law enforcement agencies found new and more effective means of accessing vastly larger quantities of data. Today, there are again calls for regulation to mandate the provision of exceptional access mechanisms. In this article, a group of computer scientists and security experts, many of whom participated in a 1997 study of these same topics, has convened to explore the likely effects of imposing extraordinary access mandates.

We have found that the damage that could be caused by law enforcement exceptional access requirements would be even greater today than it would have been 20 years ago. In the wake of the growing economic and social cost of the fundamental insecurity of today’s Internet environment, any proposals that alter the security dynamics online should be approached with caution. Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Whether you agree on policy grounds about back doors to encryption or not, is there any factual doubt that back doors to encryption leave users insecure?

That’s an important point because Hillary’s data science advisers should have clued her in that her position is factually false. With or without a “Manhattan Project.”

Here are the ethical questions with regard to Hillary’s position on back doors for encryption:

1. Did Hillary’s data scientist(s) tell her that access by the government to encrypted data means no security for users?
2. What ethical obligations do data scientists have to advise public office holders or candidates that their positions are at variance with known facts?
3. What ethical obligations do data scientists have to caution their clients when they persist in spreading mis-information, in this case about encryption?
4. What ethical obligations do data scientists have to expose their reports to a client outlining why the client’s public position is factually false?

Many people will differ on the policy question of access to encrypted data but that access to encrypted data weakens the protection for all users is beyond reasonable doubt.

If data scientists want to debate ethics, at least make it about an issue with consequences. Especially for the data scientists.

Questions with no risk aren’t ethics questions, they are parlor entertainment games.

PS: Is there an ethical data scientist in the Hillary Clinton campaign?

### 20 Big Data Repositories You Should Check Out [Data Source Checking?]

Wednesday, December 16th, 2015

Vincent lists some additional sources along with a link to Bernard Marr’s original selection.

One of the issues with such lists is that they are rarely maintained.

For example, Bernard listed:

Topsy http://topsy.com/

Free, comprehensive social media data is hard to come by – after all their data is what generates profits for the big players (Facebook, Twitter etc) so they don’t want to give it away. However Topsy provides a searchable database of public tweets going back to 2006 as well as several tools to analyze the conversations.

But if you follow http://topsy.com/, you will find it points to:

Use Search on your iPhone, iPad, or iPod touch

With iOS 9, Search lets you look for content from the web, your contacts, apps, nearby places, and more. Powered by Siri, Search offers suggestions and updates results as you type.

That sucks doesn’t it? Expecting to be able to search public tweets back to 2006, along with analytical tools and what you get is a kiddie guide to search on a malware honeypot.

For a fuller explanation or at least the latest news on Topsy, check out: Apple shuts down Twitter analytics service Topsy by Sam Byford, dated December 16, 2015 (that’s today as I write this post).

So, strike Topsy off your list of big data sources.

Rather than bare lists, what big data needs is a curated list of big data sources that does more than list sources. Those sources need to be broken down to data sets to enable big data searchers to find all the relevant data sets and retrieve only those that remain accessible.

Like “link checking” but for big data resources. Data Source Checking?

That would be the “go to” place for big data sets and as bad as I hate advertising, a high traffic area for advertising to make it cost effective if not profitable.

### Data Science Lessons [Why You Need To Practice Programming]

Monday, December 14th, 2015

Shantnu has authored several programming books using Python and has a series of videos (with more forthcoming) on doing data science with Python.

Shantnu had me when he used data from the Hubble Space telescope in his Introduction to Pandas with Practical examples.

The videos build one upon another and new users will appreciate that not very move is the correct one. 😉

If I had to pick one video to share, of those presently available, it would be:

It’s not new advice but it certainly is advice that needs repeating.

This anecdote is told about Pablo Casals (world famous cellist):

When Casals (then age 93) was asked why he continued to practice the cello three hours a day, he replied, “I’m beginning to notice some improvement.”

What are you practicing three hours a day?

### Data Science Learning Club

Sunday, December 13th, 2015

Data Science Learning Club by Renee Teate.

From the Hello and welcome message:

I’m Renee Teate, the host of the Becoming a Data Scientist Podcast, and I started this club so data science learners can work on projects together. Please browse the activities and see what we’re up to!

What is the Data Science Learning Club?

This learning club was created as part of the Becoming a Data Scientist Podcast [coming soon!]. Each episode, there is a “learning activity” announced. Anyone can come here to the club forum to get details and resources, participate in the activity, and share their results.

Participants can use any technology and any programming language to do the activities, though I expect most will use python or R. No one is “teaching” how to do the activity, we’ll just share resources and all do the activity during the same time period so we can help each other out if needed.

How do I participate?

Just register for a free account, and start learning!

If you’re joining in a “live” activity during the 2 weeks after a podcast episode airs (the original “assignment” period listed in the forum description), then you can expect others to be doing the activity at the same time and helping each other out. If you’re working through the activities from the beginning after the original assignment period is over, you can browse the existing posts for help and you can still post your results. If you have trouble, feel free to post a question, but you may not get a timely response if the activity isn’t the current one.

• If you are brand new to data science, you may want to start at activity 00 and work your way through each activity with the help of the information in posts by people that did it before you. I plan to make them increase in difficulty as we go along, and they may build on one another. You may be able to skip some activities without missing out on much, and also if you finish more than 1 activity every 2 weeks, you will be going faster than new activities are posted and will catch up.
• If you know enough to have done most of the prior activities on your own, you don’t have to start from the beginning. Join the current activity (latest one posted) with the “live” group and participate in the activity along with us.
• If you are more advanced, please join in anyway! You can work through activities for practice and help out anyone that is struggling. Show off what you can do and write tutorials to share!

If you have challenges during the activity and overcome them on your own, please post about it and share what you did in case others come across the same challenges. Once you have success, please post about your experience and share your good results! If you write a post or tutorial on your own blog, write a brief summary and post a link to it, and I’ll check it out and promote the most helpful ones.

The only “dues” for being a member of the club are to participate in as many activities as possible, share as much of your work as you can, give constructive feedback to others, and help each other out as needed!

I look forward to this series of learning activities, and I’ll be participating along with you!

Renee’s Data Science Learning Club is due to go live on December 14, 2015!

With the various free courses, Stack Overflow and similar resources, it will be interesting to see how this develops.

Hopefully recurrent questions will develop into tutorials culled from discussions. That hasn’t happened with Stack Overflow, not that I am aware of, but perhaps it will happen here.

Stop by and see how the site develops!

### DataGenetics (blog)

Saturday, December 12th, 2015

I mentioned Nick’s post Estimating “known unknowns” but his blog merits more than a mention of that one post.

As of today, Nick has 217 posts that touch on topics relevant to data science and have illustrations that make them memorable. You will remember those illustrations for discussions among data scientists, customers and even data science interviewers.

Follow Berry’s posts long enough and you may acquire the skill of illustrating data science ideas and problems in straight-forward prose.

Good luck!

### Estimating “known unknowns”

Saturday, December 12th, 2015

Estimating “known unknowns” by Nick Berry.

From the post:

There’s a famous quote from former Secretary of Defense Donald Rumsfeld:

“ … there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know.”

I write this blog. I’m an engineer. Whilst I do my best and try to proof read, often mistakes creep in. I know there are probably mistakes in just about everything I write! How would I go about estimating the number of errors?

The idea for this article came from a book I recently read by Paul J. Nahin, entitled Duelling Idiots and Other Probability Puzzlers (In turn, referencing earlier work by the eminent mathematician George Pólya).

Proof Reading2

Imagine I write a (non-trivially short) document and give it to two proof readers to check. These two readers (independantly) proof read the manuscript looking for errors, highlighting each one they find.

Just like me, these proof readers are not perfect. They, also, are not going to find all the errors in the document.

Because they work independently, there is a chance that reader #1 will find some errors that reader #2 does not (and vice versa), and there could be errors that are found by both readers. What we are trying to do is get an estimate for the number of unseen errors (errors detected by neither of the proof readers).*

*An alternate way of thinking of this is to get an estimate for the total number of errors in the document (from which we can subtract the distinct number of errors found to give an estimate to the number of unseen errros.

A highly entertaining posts on estimating “known unknowns,” such as the number of errors in a paper that has been proofed by two independent proof readers.

Of more than passing interest to me because I am involved in a New Testament Greek Lexicon project that is an XML encoding of a 500+ page Greek lexicon.

The working text is in XML, but not every feature of the original lexicon was captured in markup and even if that were true, we would still want to improve upon features offered by the lexicon. All of which depend upon the correctness of the original markup.

You will find Nick’s analysis interesting and more than that, memorable. Just in case you are asked about “estimating ‘known unknowns'” in a data science interview.

Only Rumsfeld could tell you how to estimate an “unknown unknowns.” I think it goes: “Watch me pull a number out of my ….”

😉

I was found this post by following another post at this site, which was cited by Data Science Renee.

### 3 ways to win “Practical Data Science with R”! (Contest ends December 12, 2015 at 11:59pm EST)

Friday, December 4th, 2015

Renee is running a contest to give away three copies of “Practical Data Science with R” by Nina Zumel and John Mount!

You must enter on or before December 12, 2015 at 11:59pm EST.

Three ways to win, see Renee’s post for the details!

### A Challenge to Data Scientists

Sunday, November 22nd, 2015

From the post:

As data scientists, we are aware that bias exists in the world. We read up on stories about how cognitive biases can affect decision-making. We know that, for instance, a resume with a white-sounding name will receive a different response than the same resume with a black-sounding name, and that writers of performance reviews use different language to describe contributions by women and men in the workplace. We read stories in the news about ageism in healthcare and racism in mortgage lending.

Data scientists are problem solvers at heart, and we love our data and our algorithms that sometimes seem to work like magic, so we may be inclined to try to solve these problems stemming from human bias by turning the decisions over to machines. Most people seem to believe that machines are less biased and more pure in their decision-making – that the data tells the truth, that the machines won’t discriminate.

Renee’s post summarizes a lot of information about bias, inside and outside of data science and issues this challenge:

Data scientists, I challenge you. I challenge you to figure out how to make the systems you design as fair as possible.

An admirable sentiment but one hard part is defining “…as fair as possible.”

Being professionally trained in a day to day “hermeneutic of suspicion,” as opposed to Paul Ricoeur‘s analysis of texts (Paul Ricoeur and the Hermeneutics of Suspicion: A Brief Overview and Critique by G.D. Robinson.), I have yet to encounter a definition of “fair” that does not define winners and losers.

Data science relies on classification, which has as its avowed purpose the separation of items into different categories. Some categories will be treated differently than others. Otherwise there would be no reason to perform the classification.

Another hard part is that employers of data scientists are more likely to say:

Analyze data X for market segments responding to ad campaign Y.

As opposed to:

What do you think about our ads targeting tweens by the use of sexual-content for our unhealthy product A?

Or change the questions to fit those asked of data scientists at any government intelligence agency.

The vast majority of data scientists are hired as data scientists, not amateur theologians.

Competence in data science has no demonstrable relationship to competence in ethics, fairness, morality, etc. Data scientists can have opinions about the same but shouldn’t presume to poach on other areas of expertise.

How you would feel if a competent user of spreadsheets decided to label themselves a “data scientist?”

Keep that in mind the next time someone starts to pontificate on “ethics” in data science.

PS: Renee is in the process of creating and assembling high quality resources for anyone interested in data science. Be sure to explore her blog and other links after reading her post.

### Introduction to Data Science (3rd Edition)

Monday, October 19th, 2015

Introduction to Data Science, 3rd Edition by Jeffrey Stanton.

From the webpage:

In this Introduction to Data Science eBook, a series of data problems of increasing complexity is used to illustrate the skills and capabilities needed by data scientists. The open source data analysis program known as “R” and its graphical user interface companion “R-Studio” are used to work with real data examples to illustrate both the challenges of data science and some of the techniques used to address those challenges. To the greatest extent possible, real datasets reflecting important contemporary issues are used as the basis of the discussions.

A very good introductory text on data science.

I originally saw a tweet about the second edition but searching on the title and Stanton uncovered this later version.

In the timeless world of the WWW, the amount of out-dated information vastly exceeds the latest. Check for updates before broadcasting your latest “find.”

### 16+ Free Data Science Books

Sunday, October 18th, 2015

From the webpage:

As a data scientist at Quora, I often get asked for my advice about becoming a data scientist. To help those people, I’ve took some time to compile my top recommendations of quality data science books that are either available for free (by generosity of the author) or are Pay What You Want (PWYW) with \$0 minimum.

Please bookmark this place and refer to it often! Click on the book covers to take yourself to the free versions of the book. I’ve also provided Amazon links (when applicable) in my descriptions in case you want to buy a physical copy. There’s actually more than 16 free books here since I’ve added a few since conception, but I’m keeping the name of this website for recognition.

The authors of these books have put in much effort to produce these free resources – please consider supporting them through avenues that the authors provide, such as contributing via PWYW or buying a hard copy [Disclosure: I get a small commission via the Amazon links, and I am co-author of one of these books].

Some of the usual suspects are here along with some unexpected titles, such as A First Course in Design and Analysis of Experiments by Gary W. Oehlert.

From the introduction:

Researchers use experiments to answer questions. Typical questions might be:

• Is a drug a safe, effective cure for a disease? This could be a test of how AZT affects the progress of AIDS
• Which combination of protein and carbohydrate sources provides the best nutrition for growing lambs?
• How will long-distance telephone usage change if our company offers a different rate structure to our customers
• Will an ice cream manufactured with a new kind of stabilizer be as palatable as our current ice cream?
• Does short-term incarceration of spouse abusers deter future assaults?
• Under what conditions should I operate my chemical refinery, given this month’s grade of raw material?

This book is meant to help decision makers and researchers design good experiments, analyze them properly, and answer their questions.

It isn’t short, six hundred and fifty-nine pages, but taken in small doses you will learn a great deal about experimental design. Not only how to properly design experiments but how to spot when they aren’t well designed.

Think of it as training to go big-game hunting in the latest issue of Nature or Science. Adds a bit of competitiveness to the enterprise.