- Perfect Paperback: 300 pages
- Publisher: Technics Publications LLC; First edition (1 May 2014)
- Language: English
- ISBN-10: 193550469X
- ISBN-13: 978-1935504696
- Product Dimensions: 15.2 x 1.6 x 22.9 cm
- Average Customer Review: 2 customer reviews
- Amazon Bestsellers Rank: #1,44,025 in Books (See Top 100 in Books)
Data Scientist: The Definitive Guide to Becoming a Data Scientist Perfect Paperback – 1 May 2014
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Description for Data Scientist: The Definitive Guide to Becoming a Data Scientist
From the Inside Flap
As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading.
About the Author
Dr. Zacharias Voulgaris was born and raised in Greece. Upon completing a 5-year Engineering degree at the Technical University of Crete, he enrolled in the City University of London for a Masters course in Information Systems and Technology. Afterwards, he pursued a PhD in Birkbeck College (University of London), under the joint supervision of Prof. G. Magoulas and Prof. B. Mirkin, in the field of Machine Learning. Upon receiving his doctorate, he was recruited by the Georgia Institute of Technology as a research fellow. Since January 2013 he has been working as a Data Scientist.
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Top customer reviews
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This book came to me just the right time (sometime in April 2015). Without this book, I would have lost the interest but this book just brought me back on track with required focus. Journey towards data scientist is still long. From here on if I lose the way, it will only be my fault by not persisting the further practice and learning curve. But if I will be successful in pursuit of becoming Data Scientist, the major credit will go to this book and the author "Zacharias Voulgaris"
So here is what I liked about this book a lot,
- It not just a book it’s the first hand experiences shared on how to & what it takes to become Data Scientist.
- First chapter beautifully explains the Big Data, Data Analyst and Data Science (Scientist) differences. Its sets the stage nicely toward exploring data science.
- Explanation of big data with four V's (Volume, Velocity, Variety & Veracity) gives clear understanding.
- I personally feel, documenting mindset requirement is the toughest in any learning. Specially in today's world where people go through various levels of stress to keep up with the competition. The chapter on mindset requirement can help immensely to assess and work-out on building required mindset to become data scientist.
- Building further on mindset requirements the Chapters 5, 6 & 7 nicely explain Technical Qualification, Experience and most importantly Networking to build not just skills but connection to establish yourself in the scientist communities.
- Chapter 8 explains on Software used. In Chapter 9 it builds further explaining how to keep Learning New Things (which is the most important aspect) and Tackling Problems (the real reason to be a scientist)
- Chapter 10 is well summarized on Machine Learning, R & Statistics. The scenarios on when use which one really gives good perspectives towards looking at tools and problems at hand.
- Chapter 11 dives into the Data Science processes. Whatever learnt in the earlier chapters will start making more sense with the way topic is written.
- Building further in Chapter 12 it talks about specific skills required. What I liked is, it covers variety of profiles from experienced to student and gives guidance on reviewing/building required skills for the job. Here the introspection becomes easy to assess skill gaps and start thinking about learning plan.
- Chapter 13 & 14 are nicely crafted around Where to Look for a Data Science Job, Presenting your candidature for applying jobs/work. In Chapter 15 it also talks about Freelance Track. It will help both types of people, the one who want to pursue freelancing while in job to build alternate career/income. And the others who are willing to be in full time freelancing at their will/choice.
- In any learning case studies make them more relevant as all of us like to hear real stories and examples. Chapter 16-18 share stories of real people from junior to experienced data scientist.
- Keeping yourself updated with the trends, tools & techniques is the super important to be good data scientist. The glossary, reference websites and offline books sections is overwhelming add-on which will ensure you have required pointers to stay on track to goodness.
I have only 1 suggestion. There is lot of text which is expected on such crucial topic and lets not expects shortcuts for that. However some good visuals can make lot of difference in keeping reader engaged and interested.
Conclusion – If you want to put your data science learning on fast track, go grab this book.
Most helpful customer reviews on Amazon.com
It helps to understand the topic, the actual requirements, and lots of references to be a Data Scientist.
The book's guideline style is an excellent approach to prepare interested people for the next movement.
I would highly recommend the book.
Part about how to apply for job and part about how to make the transition from a current position as DBA, Student, Statistician etc I found less interesting.