Wednesday, July 27, 2016

Book Review: Getting Started With Data Science

I PROGRAMMER's Kay Ewbank's reviews Getting Started with Data Science: Making Sense of Data with Analytics.

By Kay Ewbank

If 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 use that as the basis for some data analysis.

The aim is to teach you how to do your own analyses. Haider works through the examples in R, Stata, SPSS and SAS. Within the book the examples are worked mainly in R, and one of the other languages. The code for the other languages is available for download from the IBM Press website, along with details of how to use it. 

The book opens with a chapter called 'the bazaar of storytellers' that discusses what data science is and gives the author's definition of a data scientist. The next chapter, data in the 24/7 connected world, identifies sources of data that you can analyse, and also introduces the concept of big data. Chapter three looks at how data becomes meaningful when it is used as the basis for 'stories'. Haider's view is that the strength of data science lies in the power of the narrative, and that is what underpins most of the book.

"Overall, this is a book that is accessible, interesting and still manages to introduce the statistical techniques you need to use for real data analytical work. A good way to get into data analysis."

From a practical perspective, the book begins to get useful in chapter four,  which looks at how you can generate summary tables, including multi-dimensional tables. Next is a chapter on graphics and how to generate them. If you're thinking that it seems a bit odd to concentrate on the 'end result' first, you have to remember that the author's view is that data analysis is only useful if your audience actually looks at the results and understands them.

The next chapter gets more into the workings of data analysis with an examination of hypothesis testing using techniques such as t-tests and correlation analysis. Regression analysis is looked at next, based on the notions "why tall parents don't have even taller children". This is a fun chapter, with examples including consumer spending on food and alcohol, housing markets, and whether the appearance of teachers affects their evaluations by students.

A chapter on analysis of binary variables considers logit and probit models using data from New York transit use. Categorical data and multinomial variables are the topic of the next chapter, which expands on the ideas of logit models.

Spatial data analysis is covered next, taking us into the use of GIS systems and how these have expanded the options for data analysis. There's a good chapter on time series analysis looking at how regression models can be used with time series data, using the examples of forecasting housing markets.

The final chapter introduces the field of data mining. It's more of a taster discussing some of the techniques that can be used, but fun anyway.

Overall, this is a book that is accessible, interesting and still manages to introduce the statistical techniques you need to use for real data analytical work. A good way to get into data analysis. 

Related Reviews

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Sunday, July 24, 2016

The collaborative innovation landscape in data science

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 for software development.

Earlier in July, I received in an email the table of contents for the Stata Journal’s latest issue. I was expecting to see one or maybe two articles of interest. What I found was quite surprising. I was intrigued by almost every article, which made me wonder if I had lost my academic focus so that almost anything is now of interest?  

As I browsed through the journal, I noticed that the authors contributing to the journal were truly international. From academic colleagues in Germany and the United States to colleagues working for central banks in Europe, the diversity was hard to ignore. And that’s where I spotted the apparent similarity between R and Stata. Even though Stata is a proprietary computing platform, the innovation landscape is not restricted to the core team at Stata. This is similar to the R environment where literally thousands of packages (algorithms) for R are contributed by independent researchers.

For R, such collaborative ecosystem comes naturally for R being free software. Stata, on the other hand, follows a more traditional market approach of charging for the use of the software. Yet, Stata and R are able to attract leading data scientists (my preferred term for statisticians, econometricians, and others) to volunteer their innovation expertise that they readily share with the larger community.   

Returning to the latest issue, I was first attracted to the article on assessing inequality using percentile shares. As the author, Ben Jann from the University of Bern, noted income inequality has come to the forefront of academic and social discourse since the publication of Thomas Piketty’s Capital in the Twenty-First Century. I have been intrigued by the topic for years, primarily influenced by the incredible works of Joseph Stiglitz, Angus Deaton, and others.  Piketty’s Capital, despite the criticism (watch Deidre Mccloskey’s careful, yet blunt, review of the Capital), has made percentile shares familiar to analyze distributional inequalities.

Ben Jann has contributed pshare to Stata that readily estimates inequalities with the convenience of a single-line syntax. Using the data from the 1988 US National Study of Young Women, the command easily computes the income distribution showing that the top 10-percent women received 27% of the wages.

For R users, I would recommend the ineq package by Achim Zeileis and Christian Kleiber for generating inequality and poverty indices.  

My primary area of interest lies at the intersection of real estate and transportation in urban settings. I am always struggling with how location impacts rents, growth, and other socio-economic outcomes. Determining the location or, for that matter, distances between entities is usually a struggle. Thanks to the GIS software, such as QGIS, MapInfo, and Maptitude, the task of spatial computation has become a lot easier. Still, one has to get proficient on several computing platforms to achieve the necessary tasks of getting distances or travel times to and from locations. Stata offers two interesting solutions for these tasks. The latest one is reported in the latest issue. Stephan Huber and Christoph Rust from the University of Regensburg have a contributed a new command that computes network distances (not just the straight-line Euclidean distances) and network travel times for the shortest paths that rely on Open Source Routing Machine and OpenStreetMap.

Earlier in 2011, Adam Ozimek and Daniel Miles contributed commands to geocode and compute travel times between origins and destinations for different modes of travel, including public transit.

R is equally equipped for similar tasks. Timothée Giraud, Robin Cura, and Matthieu Viry programmed an R package osrm to determine travel time and distances. Other R packages include gdistance and gmapdistance, to name a few.

In summary, I remain delightedly optimistic about the future of both open source and proprietary computing platforms. Altruism is the name of the game where thousands of innovators are making their generous contributions available for the larger benefit of the society making it easier for applied data scientists to satisfy their curiosities by applying readily available algorithms to solve riddles. 

Monday, July 18, 2016

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 The 9-week long Boot Camp concluded on July 15. 

We received a total of 1,137 registrations and the attendance ranged between 100 to 150 participants each week. 

I have made the resources (software codes, PowerPoints, etc.) available online at We recorded 24 hours of video, which we will be online soon.

I restricted the hands-on training to R, hence the Boot Camp serves as an introduction to analytics with R. You are welcome to share these resources.

A breakdown of weekly schedule is provided in the following hyperlinked list:

Thursday, May 12, 2016

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

The Boot Camp is largely based on the contents of my recently published book, Getting Started with Data Science: Making Sense of Data with Analytics. You can read more about the book by Clicking HERE.

Logistical details:

When: Fridays (2:00 - 5:00 pm)
Where: 55 Dundas Street West, Toronto, 9th floor, Room 3-109
     Ted Rogers School of Management, Ryerson University
Cost: Free (Courtesy Ryerson University & BigDataUniversity)
Starting on: May 13 for introductions. Actual launch is on May 20.
Spaces: I'd like to cap enrollment at 15.
Registration: Email us or use Registration Form at BigDataUniversity.
Prerequisites: Curiosity, high-school math, prescribed book, a laptop computer, and willingness to learn R.

BigDataUniversity will live stream the sessions for those who are unable to attend, but interested in the topic.

Tentative Schedule

May 13, 2016- Introductions, software details, and logistical details.
Week 1 - Taking the first step
  • Detailed hands-on examples of analytics to understand what you will be able to accomplish by the end of the boot camp.
Week 2 - Data: It’s shapes, sizes, and formats
Week 3 - Regression: The tool that fixes everything, or almost everything.
  • Applied analytics with teaching evaluations. 
  • Do good-looking instructors get higher teaching evaluations?
Week 4 - Correlations, causations, and manufactured facts
Week 5 - Aerobics with data: Taming your data to meet your needs.
Week 6 - Time is money: Analytics with time series data.
Week 7 - Case study 1: 
  • Do women who lack health insurance from their spouse’s employer more likely to work full-time?
Week 8 - Case Study 2: 
  • Do higher taxes result in lower cigarette sales? Did Land Transfer Tax impact housing sales in Toronto?
Week 9 - Case Study 3: 
  • To smoke or not to smoke: that is the question.
Week 10 - Case study 4: 
  • Is space the new frontier? Map it to know it.

Wednesday, January 13, 2016

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 earlier at the IBM Insight2015 Conference.

The very purpose of authoring this book was to rethink the way we have been teaching statistics and analytics to students and practitioners. It is no secret that most students required to take the mandatory stats course dislike it. I believe it has something to do with the way we have been teaching the subject than to do with the aptitude of our students. Furthermore, I believe there is a greater opportunity to equip the students with the skills needed in a world awash with data where competing on analytics defines the real competitive advantage.

No wonder, the latest issue of the leading publication on the subject, The American Statistician, is dedicated to reimagining how statistics should be taught in the undergraduate curriculum. The editors noted:
“We hope that this collection of articles as well as the online discussion provide useful fodder for further review, assessment, and continuous improvement of the undergraduate statistics curriculum that will allow the next generation to take a leadership role by making decisions using data in the increasingly complex world that they will inhabit.”
I am confident that my book will do its small part in equipping the next generation of students with the kind of skills needed to succeed in a data-centric world. For one, I have taken a storytelling approach to statistics. This book reinforces the point that data science and analytics training should be applied rather than theoretical, and the ultimate purpose of producing or consuming statistical analysis is to tell fascinating stories from it. Therefore, the book opens with the chapter titled, The Bazaar of Storytellers.

Who is this book for?

While the world is awash with large volumes of data, inexpensive computing power, and vast amounts of digital storage, the skilled workforce capable of analyzing data and interpreting it is in short supply. A 2011 McKinsey Global Institute report suggests that “the United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.”

Getting Started with Data Science (GSDS) is a purpose-written book targeted at those professionals who are tasked with analytics, but they do not have the comfort level needed to be proficient in data-driven analytics. GSDS appeals to those students who are frustrated with the impractical nature of the prescribed textbooks and are looking for an affordable text to serve as a long-term reference. GSDS embraces the 24-7 streaming of data and is structured for those users who have access to data and software of their choice, but do not know what methods to use, how to interpret the results, and most importantly how to communicate findings as reports and presentations in print or on-line.

GSDS is a resource for millions employed in knowledge-driven industries where workers are increasingly expected to facilitate smart decision-making using up-to-date information that sometimes takes the form of continuously updating data.

At the same time, the learning-by-doing approach in the book is equally suited for independent study by senior undergraduate and graduate students who are expected to conduct independent research for their coursework or dissertations.

Praise for the book

I am also pleased to share with you the praise for my book by Dr. Munir Sheikh, Canada’s former chief statistician:
“The power of data, evidence, and analytics in improving decision-making for individuals, businesses, and governments is well known and well documented. However, there is a huge gap in the availability of material for those who should use data, evidence, and analytics but do not know how. This fascinating book plugs this gap, and I highly recommend it to those who know this field and those who want to learn.”
— Munir A. Sheikh, Ph.D., Distinguished Fellow and Adjunct Professor at Queen’s University

Tom Davenport, author of the bestselling books Competing on Analytics and Big Data @ Work.has the following to say about my book:
“A coauthor and I once wrote that data scientists held ‘the sexiest job of the 21st century.’ This was not because of their inherent sex appeal, but because of their scarcity and value to organizations. This book may reduce the scarcity of data scientists, but it will certainly increase their value. It teaches many things, but most importantly it teaches how to tell a story with data.”
—Thomas H. Davenport, Distinguished Professor, Babson College; Research Fellow, MIT.

Dr. Patrick Surry
, Chief Data Scientist at had the following to say:
“This book addresses the key challenge facing data science today, that of bridging the gap between analytics and business value. Too many writers dive immediately into the details of specific statistical methods or technologies, without focusing on this bigger picture. In contrast, Haider identifies the central role of narrative in delivering real value from big data.

“The successful data scientist has the ability to translate between business goals and statistical approaches, identify appropriate deliverables, and communicate them in a compelling and comprehensible way that drives meaningful action. To paraphrase Tukey, ‘Far better an approximate answer to the right question, than an exact answer to a wrong one.’ Haider’s book never loses sight of this central tenet and uses many realworld examples to guide the reader through the broad range of skills, techniques, and tools needed to succeed in practical data-science. “Highly recommended to anyone looking to get started or broaden their skillset in this fast-growing field.”
And finally, Professor Atif Mian, author of the best-selling book: The House of Debt offered the following assessment:
“We have produced more data in the last two years than all of human history combined. Whether you are in business, government, academia, or journalism, the future belongs to those who can analyze these data intelligently. This book is a superb introduction to data analytics, a must-read for anyone contemplating how to integrate big data into their everyday decision making.”
— Professor Atif Mian, Theodore A. Wells ’29 Professor of Economics and Public Affairs,
Princeton University; Director of the Julis-Rabinowitz Center for Public Policy and Finance at the Woodrow Wilson School.

Sunday, December 6, 2015

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.

Excel has made great progress in expanding its capabilities in the recent past. Unlike the few thousand rows in the past, the current version permits about a million rows per Sheet (a single data set). But when it comes to regression, you may have several thousand observations in the data set, you are still limited by a hard constraint of sixteen explanatory variables.

Some would argue that for parsimony, we should be content with the restriction. True, but with categorical variables, the number of explanatory variables stretch beyond the artificial constraints set by Microsoft Excel.

Others might inquire why do statistical analyses in Excel in the first place. Despite the inherent limitations in Microsoft Excel, business schools in particular and other social science undergraduate programs in general, are increasingly turning to Excel to teach courses in statistics. If you were to take a quick look at the curriculum of the undergraduate business and numerous MBA programs, you would realize how widespread is the use of Excel for courses in statistics and analytics.

At Ryerson University, I switched to R years ago for my MBA courses. Thanks to John Fox’s R Commander, the transition to R was without much hassle. The students were told in the very beginning that they were now part of the big league, and hiding behind spreadsheets was no longer an option.

I must mention that Microsoft Excel continues to be my platform of choice for a variety of tasks. I use Excel several times a day, but not for statistical analysis. I am not suggesting that Excel cannot do statistics; I am arguing that it can do a much better job of it.

As I see it, Microsoft has several options. First is do nothing. After all, Microsoft Excel has no real competition in the Windows environment. Second, it could turn to the team that has programmed the linest function in Excel and ask them to add some muscle to it. That will be the wrong approach.

Instead, Microsoft should explore ways to integrate R or another freeware with Excel to add a complete analytics menu. Microsoft should learn from what the leaders in analytics are already doing. SPSS, an industry leader in analytics category, has already integrated R, allowing the SPSS users to merge the robust data management strengths of SPSS with the state-of-the-art analytics bundled with R. SAS, another big name in analytics, is about to do the same.

And since Microsoft has recently acquired Revolution R, it makes even more sense to build a bridge between Excel and Revolution R Open (RRO).

R Through Excel is one example of integrating R with Excel. If Microsoft were to put its weight behind the initiative, it could build a seamless coupling with R expanding the analytic capabilities for hundreds of million Excel users.

As for the SPSS, I recommend they also consider another option. If Microsoft were to integrate RRO with Excel, they could acquire an advanced analytics software and integrate it with SPSS. For this option, I would recommend Limdep, which I have found to be the most diverse software for statistical analysis and econometrics. Even though R is a collective effort of thousands of software developers, Limdep offers numerous routines and post-estimation options that are not available in the thousands of R packages. SPSS integrated with Limdep could become the most diversely capable commercial software in the market as it will bridge the gap with SAS and Stata.

As for the colleagues in business faculties pondering over what platform to adopt for the analytics/software courses, I would say know your limits, especially with Microsoft Excel while deciding upon the curriculum.

Friday, October 30, 2015

Curious about big data in Montreal?

Are you in Montreal and curious about big data? Well here is your chance to attend a session about the same at Concordia University on Tuesday, Nov. 03 at 6:00 pm., which is an IBM-led initiative is running meetups across North America to create awareness about, and training in, big data analytics.

BigDataUniversity runs MOOCs and through its online data scientist workbench provides access to python, R, and even Spark. Also, you can learn about Watson Analytics and see how you can work with the state-of-the-art in computing.

Further details are available at:

Getting started with Data Science and Introduction to Watson Analytics

When: Tuesday, November 3rd at 6-9 PM

Where: H1269, 12th floor of the Hall Bldg 
(1455, blvd. De Maisonneuve ouest - Metro Guy-Concordia)