What Is Data Science? eBook Mike Loukides
Download As PDF : What Is Data Science? eBook Mike Loukides
We've all heard it according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.
What Is Data Science? eBook Mike Loukides
Good for a general knowledge of Data Science. Includes links in text to useful additional info and examples. Describes the branches and tools of the trade. It does not go into detail (remember it's $0). You will need to read some of the other short kindle books available to explore the many branches of Data Science; and some heavy texts to learn how to use the many different tools mentioned.Product details
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What Is Data Science? eBook Mike Loukides Reviews
Trying to decide on whether or not to go for a career in data? Start here. From what it takes to be a data scientist to some current issues facing data workers and te tools that are being used right now.
The book doesn't have all the answers itself, but instead, it contains a huge collection of resources that are hyperlinked, such as videos, articles, examples, that will lead you to the answers you are looking for!
Great overview and great resources.
I am a statistician who enjoys collecting, organizing, and analyzing data. I found the author's terminology so arcane that I have been unable to understand what he means by data science. The subject seemed to be one that I would really like to know about but was turned off by his overuse of trade terminology. How about an introductory book about data science for non-data scientists.
This book won't make you a data scientist, but in about 30 minutes of reading, it will allow you to speak intelligently about the subject at a cocktail party. More importantly, it's a fantastic overview of data science and it provides copious links and references to more in depth information. If you are interested in data science but don't have a clue where to start, this book is for you.
It’s a very intro level and somewhat repetitive, seems to be created from a blog author wrote. Good read for someone who is very new to the field.
Not a bad introduction. I didn't really understand this term "big data" and there doesn't seem to be a clear learning path to grasping it. Mike summarily explained both in a short booklet.
I got this for free as an ebook on and I felt it worth the small investment of time. This text was a good little summary over a very large and complex subject, which is what I expected it to be. I'm still new to the subject of data science, but I think this is a good addition to the start my journey to build my knowledge and skill set in this fascinating and ever increasingly important profession.
Aside from the core subject matter and summarized format, I specifically enjoyed the references to other material on some of the related subtopics.
There were some minor grammatical errors that I noted, which could've been related to its ebook format. However, I didn't find it overly distracting and take away from the text itself.
Good for a general knowledge of Data Science. Includes links in text to useful additional info and examples. Describes the branches and tools of the trade. It does not go into detail (remember it's $0). You will need to read some of the other short kindle books available to explore the many branches of Data Science; and some heavy texts to learn how to use the many different tools mentioned.
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