Blog Archives

Data Science 101, now online

September 14, 2016
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We are delighted to note that IBM's BigDataUniversity.com has launched the quintessential introductory course on data science aptly named Data Science 101.The target audience for the course is the uninitiated cohort that is curious about data science and would like to take the baby steps to a career in data and analytics. Needless to say, the course is for...

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The X-Factors: Where 0 means 1

September 1, 2016
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The X-Factors: Where 0 means 1

Hadley Wickham in a recent blog post mentioned that "Factors have a bad rap in R because they often turn up when you don’t want them." I believe Factors are an even bigger concern. They not only turn up where you don't want them, but they also turn things around when you don't want them to.Consider the following example...

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Five Questions about Data Science

August 21, 2016
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Five Questions about Data Science

From Safari Books Online (https://www.safaribooksonline.com/blog/2016/02/10/data-science-qa/) ---Recently, we were able to ask five questions of Murtaza Haider, about the new book from IBM Press called “Getting Started with Data Science: Making Sense of Data with Analytics.” Below, the author talks about the benefits of data science in today’s professional world.Getting Started with Data ScienceRead more »

So you want to be a data scientist

August 10, 2016
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So you want to be a data scientist

From HuffingtonPostThe New York Times made it look so easy. Take a few courses in data science and a web-based startup will readily pay top dollars for your newly acquired skills.Since the McKinsey Global Institute reported on the impending shortage of data crunchers, the wanna be data scientists are searching for...

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Book Review: Getting Started With Data Science

July 27, 2016
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I PROGRAMMER's Kay Ewbank's reviews Getting Started with Data Science: Making Sense of Data with Analytics.By Kay EwbankIf you've enjoyed books such as Freakonomics or Outliers, you'll feel at home reading this book as it uses a similar approach; take an interesting question such as 'Does the higher price of cigarettes deter smoking?', and...

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The collaborative innovation landscape in data science

July 24, 2016
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Computing platforms should be like Lego. That is, they should provide the fundamental building blocks and enable the users' imagination to innovate. The latest issue of Stata Journal exemplifies how Stata and, by the same account, R provide the platform for the users to innovate beyond the innate capacity of the core group responsible...

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Data Science Boot Camp completed at Ryerson University

July 18, 2016
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Data Science Boot Camp completed at Ryerson University

I am pleased to update you on the Data Science Boot Camp we ran at the Ted Rogers School of Management at Ryerson University in Toronto in collaboration with IBM’s www.BigDataUniversity.com. The 9-week long Boot Camp concluded on July 15. We received a total...

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Data Science Boot Camp

May 12, 2016
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Data Science Boot Camp

If you live in or near Toronto, are interested in learning about data science, and can spare Friday afternoons, then you are in luck. I am offering a Data Science Boot Camp at Ryerson University in collaboration with IBM's BigDataUniversity.com. The Boot Camp is largely...

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Getting Started with Data Science: Storytelling with Data

January 13, 2016
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Getting Started with Data Science: Storytelling with Data

Earlier this month, IBM Press and Pearson have published my book titled: Getting Started with Data Science: Making Sense of Data with Analytics. You can download sample pages, including a complete chapter. There are 104 pages in the sample. You can also watch a brief interview about the book recorded...

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Not so sweet sixteen!

December 6, 2015
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Not so sweet sixteen!

In the world of big data and real-time analytics, Microsoft users are still living with the constraints of the bygone days of little data and basic numeracy.If you happen to use Microsoft Excel for running Regressions, you will soon realize your limits:  The Windows version of Excel 2013 permits no more than 16 explanatory variables.

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