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The topics covered included Cluster Analysis (K means, Network Graphs and Community Detection), Naive Bayes, Optimization Models, Re
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!
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Data Smart is John Foreman's response to this que
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.
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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 read
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.
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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 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.
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There is no reason why you should not buy this book, if you even are remotely connected with things like 'Data Science', 'Analytics', 'Forecasting' etc.
I enjoyed all chapters and especially Chapters 4 (Optimization), 6 (Regression), 8 (Forecasting).
Seriously buy this book, now.
It's very easy read, and yet the author does not merely skimp important concepts, so you get best of both worlds, a good solid foundation and practical implementation.
One thing I like is for 90% of the time,
Full 5 stars.There is no reason why you should not buy this book, if you even are remotely connected with things like 'Data Science', 'Analytics', 'Forecasting' etc.
I enjoyed all chapters and especially Chapters 4 (Optimization), 6 (Regression), 8 (Forecasting).
Seriously buy this book, now.
It's very easy read, and yet the author does not merely skimp important concepts, so you get best of both worlds, a good solid foundation and practical implementation.
One thing I like is for 90% of the time, the subject matter and the spreadsheet diagrams are on the same set of pages, so you don't have to go back and forth between pages to sync text and images.
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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.
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.
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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.
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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 th
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.
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Side note: there is also a quick and dirty introduction to Gephi for network visualization.
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 discuss
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.
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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.
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 th
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!
...more This book is good for someone who wants to know a basic idea about Data Science 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. ...more
The calculation are all done in Excel so they are easier to try for yourself even if you don't have any coding experience.
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.
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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. Sp
John 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.
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Source: https://www.goodreads.com/book/show/17682206-data-smart
