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Data Smart: Using Data Science to Transform Information into Insight
Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.
But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.
Each chapter will cover a different technique in a spreadsheet so you can follow along:
Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
409 pages, Kindle Edition
First published October 31, 2013
549 people are currently reading
4931 people want to read
About the author
John W. Foreman
5 books34 followersJohn is the Chief Data Scientist for MailChimp.com. He's also a recovering management consultant who's done a lot of analytics work for large businesses (Coke, Royal Caribbean, Intercontinental Hotels) and the government (DoD, IRS, DHS).
These days John does all sorts of awesome data science for MailChimp, and he blogs for fun about analytics through narrative fiction at AnalyticsMadeSkeezy.com. Spoiler alert: the characters who do meth are frequently confused or in peril. John does not do meth.
These days John does all sorts of awesome data science for MailChimp, and he blogs for fun about analytics through narrative fiction at AnalyticsMadeSkeezy.com. Spoiler alert: the characters who do meth are frequently confused or in peril. John does not do meth.
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Displaying 1 - 30 of 55 reviews
February 18, 2020
چقدر این کتاب خوب بود و به خواندنش می‌ارزید! ایده کتاب ساده و جذاب است: تحلیل داده را، حتی ابزارها و روش‌هایی که به نظر پیچیده می‌آیند، در اکسل یاد بگیرید تا خوب بفهمید چه اتفاقی دارد می‌افتد، بعد با هر برنامه و زبانی که خواستید در آینده استفاده کنید. نویسنده در اجرای این طرح کاملا موفق است. یعنی اگر هر کسی می‌توانست با این شفافیت و دقت حوزه تخصصی خودش را توضیح و آموزش دهد رابطه صنایع و دانشگاهها فرق زیادی با امروز داشت
من بسیار یاد گرفتم و البته قدم بعدی این است که آدم دست به کار شود و داده های تحقیق یا کسب و کار خودش را بیاورد و آموخته هایش را محک بزند. یعنی کتابی است که باید به آن بازگشت
من بسیار یاد گرفتم و البته قدم بعدی این است که آدم دست به کار شود و داده های تحقیق یا کسب و کار خودش را بیاورد و آموخته هایش را محک بزند. یعنی کتابی است که باید به آن بازگشت
August 22, 2016
I'm not an expert in the field of Data Science (yet ;) ), but this seemed like a very good introduction. I'm familiar with many AI & Machine Learning techniques and I know the difference between supervised and unsupervised learning, but all those basics are reviewed in the text. The author's voice is witty and engaging throughout, which helps with a topic like this.
The topics covered included Cluster Analysis (K means, Network Graphs and Community Detection), Naive Bayes, Optimization Models, Regression, Ensemble Models, Forecasting, and Outlier Detection. Each chapter walks you through some sample data that is available to download and coaches you how to manipulate it by hand using Excel. This is strictly as a hands-on learning technique; the second to last chapter is about how to do everything a lot more easily (once you understand what you are doing) using R. The conclusion addresses what you need to be as a data scientist that isn't actually data science: understanding the true problem to be solved, avoiding focusing on things that don't matter (performance & accuracy at the expense of usability), and the fact that, as a data scientist, you are not the most important part of a business -- you are there to help make the most important part better.
While the Excel coaching gets a little tired during a read through, it would probably be much better for someone who actually works the examples :) Still a good read!
The topics covered included Cluster Analysis (K means, Network Graphs and Community Detection), Naive Bayes, Optimization Models, Regression, Ensemble Models, Forecasting, and Outlier Detection. Each chapter walks you through some sample data that is available to download and coaches you how to manipulate it by hand using Excel. This is strictly as a hands-on learning technique; the second to last chapter is about how to do everything a lot more easily (once you understand what you are doing) using R. The conclusion addresses what you need to be as a data scientist that isn't actually data science: understanding the true problem to be solved, avoiding focusing on things that don't matter (performance & accuracy at the expense of usability), and the fact that, as a data scientist, you are not the most important part of a business -- you are there to help make the most important part better.
While the Excel coaching gets a little tired during a read through, it would probably be much better for someone who actually works the examples :) Still a good read!
February 16, 2016
We all know that the field of data science is lurking in the depths of literally everything we consume, but more often than not, I’ve only been offered a hand-wavy description whenever I ask someone to explain it to me. Or even worse, I’m bombarded with maths equations whenever I venture onto a Wikipedia article about statistics. Will data science forever be ensconced in the halls of academia, boardrooms and creepy sci-fi movies outside our grasp?
Data Smart is John Foreman’s response to this question. In the book, we are introduced to eight common data science techniques. Thankfully, Foreman wrote the book with the beginner in mind: all examples are done in Excel and he keeps the statistical concepts to a minimum. However, there are glimpses into the depths of statistical theory, and a rehashing of all eight methods in R for the more technically minded.
Although you could skip ahead to the end for the quicker R versions of each technique, the level of granularity with which he presents each technique offers a lot for beginners and amateurs alike. If all reference books had Foreman’s writing style, I would have enjoyed my Statistics classes a lot more. This book definitely added a lot to my professional repertoire; the fact that I can finally understand AI descriptions in sci-fi movies is just the icing on the cake.
Data Smart is John Foreman’s response to this question. In the book, we are introduced to eight common data science techniques. Thankfully, Foreman wrote the book with the beginner in mind: all examples are done in Excel and he keeps the statistical concepts to a minimum. However, there are glimpses into the depths of statistical theory, and a rehashing of all eight methods in R for the more technically minded.
Although you could skip ahead to the end for the quicker R versions of each technique, the level of granularity with which he presents each technique offers a lot for beginners and amateurs alike. If all reference books had Foreman’s writing style, I would have enjoyed my Statistics classes a lot more. This book definitely added a lot to my professional repertoire; the fact that I can finally understand AI descriptions in sci-fi movies is just the icing on the cake.
September 9, 2014
Great. Even if it has some flaws, the overall feeling is very clear - it's a great book that easily proves that data science may be fun & sexy.
Pros:
* it's very practical - just action, just meat, everything presented in practical cases
* the examples are perfect: very clear, easy to understand & they don't seem 'virtual'
* author disassemblies all the activities into atomic steps to make his considerations easy to follow - no shortcuts, no simplifications, but he still manages to not bore the reader
* the language used is very clear and doesn't resemble typical, pompous scientific mumblings
* the chapter about R simply kicks ass: doesn't teach you the language, but shows its capabilities: that's what I expected
Cons:
* In some cases book seems to be too practical - a bit of theory wouldn't hurt anyone and could make the content more clear
* 2 or 3 times the dive from simple to wtf-is-that happened withing a paragraph or two: well, no-one guaranteed it will be smooth as butter, right? :)
To summarize: I love the book. First 3 chapters were as gripping as a good thriller :) Recommended.
Pros:
* it's very practical - just action, just meat, everything presented in practical cases
* the examples are perfect: very clear, easy to understand & they don't seem 'virtual'
* author disassemblies all the activities into atomic steps to make his considerations easy to follow - no shortcuts, no simplifications, but he still manages to not bore the reader
* the language used is very clear and doesn't resemble typical, pompous scientific mumblings
* the chapter about R simply kicks ass: doesn't teach you the language, but shows its capabilities: that's what I expected
Cons:
* In some cases book seems to be too practical - a bit of theory wouldn't hurt anyone and could make the content more clear
* 2 or 3 times the dive from simple to wtf-is-that happened withing a paragraph or two: well, no-one guaranteed it will be smooth as butter, right? :)
To summarize: I love the book. First 3 chapters were as gripping as a good thriller :) Recommended.
January 24, 2014
really great intro to modern data science.
I really liked the way he broke the material up by scenarios (e.g. predicting which Target customers are pregnant based on their previous purchases, figuring out how customers cluster so you can target them with marketing, etc). The book walks you through the calculations first in Excel so you can get a sense of how they work and then quickly at the end shows you how to achieve the same thing in R in 3 lines. I think it's a good first book that will help you figure out what techniques you need to drill in to that are more specific to your particular domain.
it's both clear and written with enough humor to keep you going.
I really liked the way he broke the material up by scenarios (e.g. predicting which Target customers are pregnant based on their previous purchases, figuring out how customers cluster so you can target them with marketing, etc). The book walks you through the calculations first in Excel so you can get a sense of how they work and then quickly at the end shows you how to achieve the same thing in R in 3 lines. I think it's a good first book that will help you figure out what techniques you need to drill in to that are more specific to your particular domain.
it's both clear and written with enough humor to keep you going.
January 30, 2015
Good book that covers a wide variety of data analytics. Some chapters had my head spinning and I would have appreciated a bit more explaining rather than just a excel formula. That being said, all the datasets used in the book are available online so that helped in the deciphering.
I'm glad the author listed other resources at the end of each chapter as I feel like those will be very helpful when I read this book a second time.
I'm glad the author listed other resources at the end of each chapter as I feel like those will be very helpful when I read this book a second time.
August 26, 2016
This book was utterly outstanding, however, to truly engage with it, I'm going to have to read it again, with a computer open, following all the examples in Excel. What Foreman does is extraordinary. Using simple language (well, as simple as you can get, given some of the trickiness of the topics), he introduces you to some of the latest and greatest data science topics. But they're all explained in entertaining, funny ways but with a great clarity. It's a great example of how to explain complicated ideas in as simple a way as possible.
But it gets even better, because then he shows us step-by-step how to use all these formulas in Excel. Which means that nearly everyone can have a crack at doing some calculations and become a data science master. I'm already thinking of ways I could start to use this stuff at work.
If you've been noticing (like me) the growing number of data science jobs appearing and wondering where you can learn all that stuff nowadays, this is a great easy way in to that world.
But it gets even better, because then he shows us step-by-step how to use all these formulas in Excel. Which means that nearly everyone can have a crack at doing some calculations and become a data science master. I'm already thinking of ways I could start to use this stuff at work.
If you've been noticing (like me) the growing number of data science jobs appearing and wondering where you can learn all that stuff nowadays, this is a great easy way in to that world.
February 7, 2017
Summary: Great book taught using the more approachable Excel as a tool. Strong points. Great book for those looking to take their basic analytical skills to the next level.
I give Foreman a lot of credit. A lot of data books are touting the need for increasing complication. Foreman - though providing nice technical information for those less familiar with Excel, does offer great elements for how to think through creating relevant data analysis. I like that he groups analysis by types and discusses what they are for. That goes beyond what most folks do within data analysis.
It is one of the better books. I do think the book is better at explaining "what" within excel, leading to better analytics, vs. describing "why" which leads to insight.
Still, it's great. Read if excel is a part of your life or if you can't seem to figure out what chart to use. It should help.
I give Foreman a lot of credit. A lot of data books are touting the need for increasing complication. Foreman - though providing nice technical information for those less familiar with Excel, does offer great elements for how to think through creating relevant data analysis. I like that he groups analysis by types and discusses what they are for. That goes beyond what most folks do within data analysis.
It is one of the better books. I do think the book is better at explaining "what" within excel, leading to better analytics, vs. describing "why" which leads to insight.
Still, it's great. Read if excel is a part of your life or if you can't seem to figure out what chart to use. It should help.
August 28, 2015
Pretty awesome.
I have the vague suspicion that this book taught me way more complex things than it felt like I was learning.
Easier to learn when following along with the excel notebooks, otherwise the equations are incomprehensible.
I'm not sure what I learned exactly (I mean in terms of how much theory vs applied whatev), but it did well at whatever it was trying to do, and seems like it will make learning the theory or more applications easier. I'll have a good set of fun examples to compare things to.
I have the vague suspicion that this book taught me way more complex things than it felt like I was learning.
Easier to learn when following along with the excel notebooks, otherwise the equations are incomprehensible.
I'm not sure what I learned exactly (I mean in terms of how much theory vs applied whatev), but it did well at whatever it was trying to do, and seems like it will make learning the theory or more applications easier. I'll have a good set of fun examples to compare things to.
July 20, 2018
A fun little book on data science approaches using nothing but Excel spreadsheets. Perhaps not the most practical option, as most individuals interested in data science approaches will likely use more niche tools-- but I was surprised at the how Foreman is able to extract a lot of functionality out of Excel worksheets.
July 11, 2018
Explains data science concepts clearly, but I feel the constant use of excel for examples is simultaneously a great idea and a hindrance. How would these tasks be done in the real world? Definitely not using a spreadsheet.
September 12, 2019
I read this as part of a study group. We met on Saturday mornings for 2 hours and discussed each chapter, sometimes we spent 2 weeks on a chapter. There is a companion website with the data. He uses Excel to explain various data science techniques. At the end he transitions the same examples from Excel to R. Excellent.
July 27, 2016
Like this book a lot. Not too basic so it doesn't hide from tough questions, not too hard making you requiere an advance mathematical degree. Examples are very useful in explaning the subject matter.
December 4, 2016
Great introduction to excel analysis and data science
Appreciate that each chapter was organized around a realistic business problem. It made the content more approachable. And the first chapter is a great crash course in the Excel skills you pick up as a management consultant.
Appreciate that each chapter was organized around a realistic business problem. It made the content more approachable. And the first chapter is a great crash course in the Excel skills you pick up as a management consultant.
March 7, 2015
Good book if Excel is your primary tool.
January 9, 2016
Liked it in concept. Large portions of explaining how to do certain analyses in Excel...which is kind of a waste of time. Wish he'd skipped these bits.
November 8, 2019
To be entirely honest, I think that this is too old by now. The publication date of 2014 is five years ago, and my wizened computer science professor told me to watch out in this field about learning facts that are five years old or older. HOWEVER that is on the cusp, so let's assume that it is closer to four years old rather than six.
Shan't we?
So, Foreman's book Data Smart: Using Data Science to Transform Information into Insight explains how to look at data science/spreadsheets well.
I think the cosine similarity customer-to-customer graph on page 175 is pretty.
Hmm, Matthew Russell's Mining the social web looks really popular as in it was only recently returned. I feel sheepish putting it on hold since I'm not going to be going back until Monday. Maybe I'll wait until Sunday to make sure anyone else who wants it has a fair chance.
Then again, I've never heard of GitHub before. OH, BUT I HAVE HEARD OF RUBY ON RAILS, from the Wikipedia page!
I liked the Octodex that looked like Nyancat the most: here! (If you click on it that kooky song doesn't start playing.)
You just have to keep checking the computer for updates on... um... computer science. Hahaha!
Shan't we?
So, Foreman's book Data Smart: Using Data Science to Transform Information into Insight explains how to look at data science/spreadsheets well.
I think the cosine similarity customer-to-customer graph on page 175 is pretty.
Hmm, Matthew Russell's Mining the social web looks really popular as in it was only recently returned. I feel sheepish putting it on hold since I'm not going to be going back until Monday. Maybe I'll wait until Sunday to make sure anyone else who wants it has a fair chance.
Then again, I've never heard of GitHub before. OH, BUT I HAVE HEARD OF RUBY ON RAILS, from the Wikipedia page!
I liked the Octodex that looked like Nyancat the most: here! (If you click on it that kooky song doesn't start playing.)
You just have to keep checking the computer for updates on... um... computer science. Hahaha!
Read
June 21, 2022Thank you for publishing this book. It will be very helpful for so many students and professionals who want to have a great career in data science.
Data scientists will be in high demand in the years ahead, especially as we approach 2022. As you consider how to shape your education and knowledge base, keep in mind that a highly specific, highly specialized skill set is becoming increasingly in demand.
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Data scientists will be in high demand in the years ahead, especially as we approach 2022. As you consider how to shape your education and knowledge base, keep in mind that a highly specific, highly specialized skill set is becoming increasingly in demand.
Advance Data Science Courses In Delhi
Learnbay is one of the best online institutes to provide high-quality advanced data science courses in Delhi. They provide certification, which is accredited by IBM, and 15+ real-time live projects related to data science, AI, and ML. Classes are conducted 100% live and interactive. In addition to this, offline classes are also provided for project purposes. They also have a 100% job guarantee program, which is the best part
For more information, visit:
Advance Data Science Course In Delhi
https://www.learnbay.co/data-science-...
July 20, 2023
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For any questions or comments, please contact us at info@skillslash.com.
Phone: 8391-911-911
Visit Website :- https://skillslash.com/data-science-c...
Visit Our Website:- https://skillslash.com/data-science-c...
#DataScienceCourse #DataScienceTraining #DataScienceCertification #DataScience #Skillslash #Pune #DataScienceCoursePune
October 6, 2020
This book explains the typical problems that a Data Scientist should deal with in real life. The author explained the theory in a easy to understand manner. The author demonstrates how to implement some analysis on a simple set of data to extract business insights. The demonstration is well-done with Excel. The selection of data set is very thought to show "beginners" what information we should expect after analysis.
This book is good for someone who wants to know a basic idea about Data Science in quick way.
This book is good for someone who wants to know a basic idea about Data Science in quick way.
March 5, 2018
I'm not completely done with this one, but going to move it off my "currently reading" shelf as I have returned it to my boss. What I have read is amazing. Anyone with an interest or love for Excel should take a look at this one. I feel like Foreman is the Bob Ross of data analysis in Excel. It stretched me and is one I will return to again.
April 2, 2021
Very good introduction to Data Science for people who have no prior experience with it. He explains things in simple analogies and examples an average person can relate to, both in terms of how different algorithms work and why they are useful.
The calculation are all done in Excel so they are easier to try for yourself even if you don't have any coding experience.
The calculation are all done in Excel so they are easier to try for yourself even if you don't have any coding experience.
December 21, 2017
A practical guide to introduce yourself to data science. This book explains many key concepts in a simple straight-forward way; each concept includes example using simple spreadsheets (Excel) on how to solve.
September 26, 2020
One weird, weird, weird book! This was my first book on Data Science. I thought, since my background is in analytics and I knew Excell, it would be a good place to start. Well, it is but it might make you cry. I learnt a lot from it.
August 9, 2021
Author has explained the concepts of Data Analytics in a very authentic way using Excel and R. You will not only understand the analytics fundamental models but also become a pro in Excel data analysis !
June 13, 2017
Good intro book with real world examples. Mentioned how we can work with data and understand the process by using excel, and how easily all of them can be done using R.
July 7, 2017
It covers pretty basic stuff, but a nice intro of DS using excel.
October 15, 2017
This book helps me to understand why we have to learn math in school. This is also a great book that demystifies machine learning.
October 20, 2017
THE. BEST. This book has saved me in my data class about 10 times. Super good writing for people who know nothing about a) excel, b) stats, and c) data science in general. Very highly recommended.
February 2, 2018
The first chapter and the last chapter were the most useful for me since I wanted to do everything he was teaching in R. Great resource overall. He makes the concepts pretty easy to understand.
Displaying 1 - 30 of 55 reviews