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Data Science Interviews Exposed, by Yanping Huang, Jane You, Iris Wang, Feng Cao, Ian Gao
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Data Science Interviews Exposed offers data science career advice and REAL interview questions to help you get the six-figures salary jobs! A data science job is extremely rewarding. It empowers to you make real impact in the world! And besides, it offers competitive salaries, and it develops your creative as well as quantitative skills. No wonder the data science job is rated as one of the sexist jobs in 21st century. So what you are waiting for ?
- Are you still wondering how to join data science work force ?
- Are you lost in the tremendous amount of online data science courses and resources ?
- Are you endlessly searching online to find data science interview questions and answers?
- This book is written by data science professionals from Facebook, LinkedIn, Amazon, Google and Microsoft, with years of first hand working and interviewing experience.
- This is the first book in the industry that systematically covers everything for preparing for a data science career and interviews, and with real interview questions and detailed answers.
- This book provides both career guidance for entry level candidates as well as interview questions practice for intermediate candidates.
- Probability Theory
- Statistical Inference
- Dataset Manipulation
- Product, Metrics and Analytics
- Experiment Design
- Coding
- Machine Learning
- Brain Teasers
Here is a full list of topics:
Introduction
This chapter presents an overview to the data science job market and the book organization.
Find the Right Job Roles
Get confused about the various data science job titles? This chapter provides a detailed description for each of them, the differences among them, as well as the guidance for choosing the one that suits you the most.
Find the Right Experience
Don't know how to prepare yourself with the right experience to meet the job requirements and your career goals? This chapter helps you to identify the experience you need to land your dream position. It also provides suggestions for new graduates as well as candidates from a different industry who want to transfer to data science field.
Get Ready for the Interviews
Think you have a clear goal and have possessed all the required skill sets, but just don't know how to get job interviews? This chapter walks you through how to build good resumes and professional profiles that would bring you the right exposure to the right person -- recruiters and hiring managers.
Polish Your Soft Skills
Heard of your competent peers failing job interviews and want to know why? This chapter reveals the secrets that most companies don t talk about publicly -- the soft skills. What are behavior questions, why are they important, how do you prepare for them? You will find the answer here.
Technical Interview Questions
An interview is not a pop quiz. You should take the time to practice on real interview problems and learn their patterns. This chapter lists eight major topics that are frequently covered by data science job interviews, associated with example interview questions for each of them. All of them are either real interview questions or adapted from real interview questions:
This chapter attaches the solutions and thought process for each question in the previous chapter. We hope the readers can grasp the key points behind each of them, hence be able to apply the approaches to other similar questions in the real interviews.
- Sales Rank: #124203 in Books
- Published on: 2015-05-25
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .53" w x 6.00" l, .70 pounds
- Binding: Paperback
- 230 pages
Review
"Finally, we have a book on data science career planning, interviews preparation, and the skill set development. The book is written with great care and enthusiasm by a crew of leading data scientists who have been working in the field for years with extensive hands-on experience. The technical interview questions provide an overview of the essential topics in data science, along with a great selection of in-depth example questions and carefully prepared solutions. The book demonstrates the technical requirements for data science positions by specific examples, and explains the thought process for approaching some challenging problems. The non-technical introduction offers a very insightful and informative view of the data science job market, followed by extremely useful guidelines on career planning and development. I enjoyed reading the book and highly recommend it for anyone who is interested in starting a data science career." --Shuang Yang, Lead Data Scientist at Twitter
The whole Internet industry and many others value data more and more these days, executives want to make decisions based on the facts derived from the data. We witnessed a rapidly rising demand for data scientists over the past years. This book is a `must-have' book for preparing interviews for data scientists. The questions in technical interview section cover most of the required skill sets that are being assessed in the interviews, and the solutions illustrate how to use those skills through real examples. The solutions are `right to the point' but are also designed to inspire candidates to get their own thoughts. The non-tech section is the key for entry level candidates to improve their business understanding as well as professionalism. What I like most about this book is how it gives an overview of the day-to-day work of data scientists, so people can have an idea whether this is the right position for their career goals. This overview is also very helpful for people with different backgrounds to improve their skills to fit in a particular data science job. --A Google Quantitative Manager
The non technical introduction offers very informative view of the data science job market, followed by extremely useful guidelines on career planning and development. --Data Scientist at Microsoft
About the Author
We, the Davocado team, is a group of five passionate data science professionals from leading internet companies: Jane an Amazon machine learning scientist; Iris a LinkedIn data scientist; Yanping a former Facebook research scientist and a now Googler; Feng an Amazon data and algorithm product software engineer;Ian a Microsoft data scientist
Data Science Interviews Exposed is our effort to show you there is a more effective and less stressful way to nail your data science job interviews, and develop your data science career. After helping and consulting with quite a few hiring managers and candidates, we understood the dire difficulty of hiring or landing a job in data science. We want to share our experience and help hundreds of thousands of candidates who are looking for jobs in data science.
Most helpful customer reviews
12 of 13 people found the following review helpful.
Potential to be the companion to CTCI, but rather poorly formatted.
By Allen
I was hoping that this would be the equivalent of Cracking the Coding Interview for data science, and it definitely has a similar format but....
PROS:
Nice selection of questions, which hopefully they will expand on in a future edition if there will be one. Great that they provide answers to the questions in the back - this is the most valuable part of the book.
CONS:
The fluffy part of the book is not very helpful in some cases. For example, the part on references for machine learning books - they list the hardest textbooks in publication. The Pattern Recognition book is the hardest beast ML textbook I have come across. They need to list more references to intermediate step books such as Alpaydin's ML book, or Hastie's I.S.L. book, etc. However, considering that this isn't "Data Science Education Exposed," I suppose it doesn't matter very much.
Also, the introductory linear algebra course work cannot possibly give a student enough knowledge to tackle a real ML textbook, so more is required to be written on the mathematical background/education required for a data scientist.
The pictures/illustrations in the book sometimes add no value to the discussion, and the large font juxtaposed to clipart makes me feel like this book was a bit rushed.
Typos in the graphs near the beginning with "Washington" spelled wrong. Also, the paperback is in black and white but the authors did not bother to put arrows for pie charts with 10+ keys. i.e. there are a bunch of keys in several shades of gray without pointers to which areas of the pie chart they refer to - this is rather important given that there is no color.
4 of 4 people found the following review helpful.
Too many grammar, usage and (even) technical errors
By Paulo C. Rios Jr.
The errors become almost intolerable and truly numerous by chapter 3. This short book has too many grammar, usage, technical and structural errors. It is inadmissible that a book went into publication in such a state. But the book was self-published. From its writing it also seems that there is a lack of expertise in some key areas of Data Science. The intention was certainly good. But, as it stands, the book is a lack of copy editors and technical reviewers exposed.
4 of 4 people found the following review helpful.
The book is full of mathematical mistakes. Many of ...
By Estienne Granet
The book is full of mathematical mistakes. Many of the exercises are poorly phrased. Not to mention that some of the assumptions required to solve the exercise are not given. The book gives a large part to managing your career as a data scientists. The number of pages dedicated to actual practice is a shame!
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