Friday, 22 of February of 2019

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News galore

Data Mining for Dummies, my epic tome for beginning data miners, is available now.

Here’s the scoop:

Data Mining for Dummies, an easy-to-read new book for beginners in data mining, published by John Wiley and Sons, and available through your favorite bookseller.
Data Mining for Dummies is for business people, information technology professionals and students who want to…
• Know what data mining is all about
• See what’s really involved in data mining, icky parts and all
• Find friendly expert guidance for getting started as a hands-on data miner
Data Mining for Dummies is written in a light, yet no-nonsense, style for readers who are new to data mining. You won’t need any special expertise to read and understand this book.
Beginners can learn the basics of data mining, including
• Understanding data mining concepts
• Embracing a comprehensive data mining process
• Planning for data mining
• Gathering data from internal, public and commercial sources
• Preparing data for exploration and predictive modeling
• Building predictive models
• Selecting software and dealing with vendors
Author Meta S. Brown is a hands-on data miner who has educated thousands of beginners from industry, government and academia in the fundamentals of data mining. She’s known in the analytics community for her articles, books and talks on data mining, text mining and classical statistics, reaching out to audiences from novices to working professionals.
Here’s what Tom Khabaza, pioneering data miner and Founding Chairman of the Society of Data Miners has to say about Data Mining for Dummies:
Meta S. Brown tells it like it is, more than anyone else in the field.
Data Mining for Dummies is the first data mining book for beginners which gives an accurate picture of what we data miners do. This is a landmark for the profession, and an essential tool for anyone learning or teaching practical data mining. I will be recommending it to everyone I meet: business people, students and teachers alike.
Where to find Data Mining for Dummies:
Your favorite independent bookseller (find one on Indiebound
Powell’s City of Books
Barnes and Noble
• Ask your local library to get it. ISBN: 978-1-118-89317-3

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Meta is Number 1 (for the moment)

LifeIsData. TV, “A Video News & Entertainment Channel for Data Pros”, just put out a video on top blog posts from women in the data game. The tweets said Week 29, but I can’t find the other 28, so somebody please clue me in. Also give me a hint about who is behind this channel, because I have no idea.

They do put together nice, professional looking (and sounding) videos. And how could I resist plugging this?

Top 3 Data Women of the Week – Claudia Imhoff, Hilary Mason, Meta Brown (Week 29)

Seems they liked a post of mine from last week, “O’Reilly Strata: Deluded About Diversity?”
What prestigious company! Much to my surprise, the video declares me as Number 1. And snarky!

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Where the girls are

Who says women are a minority in science, technology, engineering and math (STEM)? Well, if you put it that way, I do, and so do lots of others. And there’s data to support that claim.

But those fields are not all one homogeneous block. As I’ve written before, the worst of the lot is computer sciences, where the proportion of women has been dropping for more than two decades.

But I know which STEM field has women galore. Do you? I just wrote an article about it – will post next week.

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Stuff I learned from my web stats

Visits have increased by nearly a factor of ten since I added a blog to the site.

Pre-blog, activity varied little by time of day. Post-blog, there’s a clear spike in activity when posts appear in the morning.

A lot of people visit my site using the links left with comments on other sites.

Google’s spiders crawl the site with amazing frequency! (You’d think I was CNN or something.)

There was a dramatic spike in visits on February 28. (I have no idea why.)

Someone found me by searching for the term “douchegrammer.” (Wonder if he or she was pleased or disappointed?)

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More on secrets

In yesterday’s post, I mentioned an article from The New York Times which discussed customer research at Target. The folks at Target weren’t so happy about the journalist’s research. He offered them a draft for review, and they replied telling him the piece had a number of errors, but wouldn’t say what they were.

Let me take a stab at pointing out some concerns that come to mind as I read the article. I have no knowledge of what they do inside Target, so this comes purely from my own experience as an analyst.

The article says that Target tries to track every customer with an ID.

It’s not easy to do this well, but Target has a lot to work with. The usual means of tracking purchasing behavior is through a loyalty program. Target does not have a loyalty program, per se, but they offer a house credit card, which many people use. With their huge customer base, customers using that card represent a lot of information. However, customers may not always use the same card, and sometimes they may even pay in (gasp) cash.

The article says that Target used their baby shower registry to study the buying habits of pregnant women.

That made me think for a minute, as it slowly dawned on me that I was in that registry. My name was there, but was my husband’s? Maybe, maybe not. We probably paid for our Target purchases with his credit card much of the time. Another tracking problem.

For research purposes, you’d want to compare the behavior of pregnant women with demographically similar women who are not pregnant. No registry for those. Some educated guesswork needed to solve that problem.

The article gives a fictional example and a fictional probability that the fictional woman is pregnant and due in a certain month.

It’s just an example, and a little too perfect example, too. The idea is useful, but don’t get too wound up in the details of the example. Real life models are rarely, if ever, as perfect as that.

Feel free to add some thoughts of your own….

Analytics: What’s Passion Got to Do with It?

Long, long ago, in a land far, far away, there were frogs. Lots and lotsa frogs. Oh, the frogs were really beautiful princesses and handsome princes, but you’d never know it. They never got around to any kissing! Why not? They must have lacked passion.

Read more about the frogs, passion and analytics in “Analytics: What’s Passion Got to Do with It?”, my new post on Smart Data Collective.

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But Can She Type?

A few days ago, Gary Cokins asked, “Could Beethoven have implemented business analytics?” A strange question, indeed. After all, Beethoven had his own job to do. Can the average analytics manager write a symphony? Can any analytics manager write a symphony? Does Beethoven have to do everything around here? Or, as Seth Grimes put it, “Would Beethoven Have Given a Rat’s Ass about Business Analytics?”

If I may take the liberty of paraphrasing Mr. Grimes’ response, the answer is, “No.”

This reminds me of the once-famous poster: a large photograph of Golda Meir, then prime minister of Israel, with the caption, “But Can She Type?”

More importantly for us in the analytics trade, change to present tense and substitute any name you want: Does [fill in] Give a Rat’s Ass about Business Analytics? Unless the fill in is one of our own kind, the answer is still, “No.”

The gal riding next to you on the commuter train doesn’t care about business analytics. Your manager’s manager’s manager doesn’t care about business analytics. Your prospect doesn’t care about business analytics. People care about themselves, and their own business interests. While it may be true that business analytics has everything to do with that, they probably don’t know that and it’s going to take a heck of a lot of effort to make that connection in their brains.

What’s the lesson here? Next time you want someone to care about what you do, don’t talk about what you do. Instead, ask yourself: “What does this gal/guy care about? How can I relate to issues that resonate with this person?”

If you’d like a few hints on how to do that, read my piece, “Talk Analytics with Executives: 4 Things You Must Understand.”


Funky new title: Data Scientist

A post on Joe McKendrick’s “Service Oriented” blog led me to Mary Pratt’s Up-and-coming tech jobs — and how to land one. Among funky new roles like Socialite and Augmented Reality Specialist lies Data Scientist. According to Pratt, “These new specialists will not only find and deliver the data; they will also be the ones using it for extensive forecasting.”

Without a doubt, that’s what many organizations want – not a working team, not business processes that facilitate cooperation among differing functional groups, but individual, do-it-all people who make everything happen. And they expect to hire people who already meet the full set of requirements. She says that Al Delattre of Korn/Ferry International, “describes the ideal candidate as someone with an undergraduate degree in computer science and a master’s in marketing with some operations management expertise.”

Computer science, marketing and operations management. A typical computer science program does not require data analysis training – no statistics, no data mining, no operations research, though there are some programs which offer special tracks featuring several statistics classes. Master’s programs in Marketing usually include a little statistics, just a little. So most people with those degrees don’t have much training in data analysis. What’s more, those who pursue graduate studies in marketing have in mind careers in (drum roll, please) marketing. They plan to work in marketing management or perhaps creative roles. The person with operations management expertise just might hope to manage operations.

It’s a big world. No doubt a few people meeting Delattre’s ideal candidate checklist exist, yet I wouldn’t count on them actually knowing much about data analysis or data collection. They could learn, of course, but where’s the motivation? Is it really their goal to become Data Scientists?

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People who really, really love science

The Wall Street Journal has an article on a new book called “Science Ink,” about people who love science so much they tattoo equations and diagrams onto themselves. A-maz-ing photos!

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Analytics Resolutions?

It’s the end of 2011, and what do I have to show for the year, professionally speaking? About 20 new articles and an assortment of speaking engagements from New York to California. A renewed focus on text analytics with a new cross-lingual twist. Hundreds of new contacts.

In 2012, I plan to write at least 50 articles (OK, I said that last year, too.) Speaking to new and different audiences is on the agenda, so if you know an organization interested in my kind of material, give me a shout.

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