| Title | : | The Data Science Design Manual |
| Author | : | |
| Rating | : | |
| ISBN | : | 3319554433 |
| ISBN-10 | : | 9783319554433 |
| Language | : | English |
| Format Type | : | Hardcover |
| Number of Pages | : | 445 |
| Publication | : | Published July 1, 2017 |
In particular, the book stresses the following basic principles as fundamental to becoming a good data scientist: "Valuing Doing the Simple Things Right," laying the groundwork of what really matters in analyzing data; "Developing Mathematical Intuition," so that readers can understand on an intuitive level why these concepts were developed, how they are useful and when they work best, and; "Thinking Like a Computer Scientist, but Acting Like a Statistician," following approaches which come most naturally to computer scientists while maintaining the core values of statistical reasoning. The book does not emphasize any particular language or suite of data analysis tools, but instead provides a high-level discussion of important design principles.
This book covers enough material for an "Introduction to Data Science" course at the undergraduate or early graduate student levels. A full set of lecture slides for teaching this course are available at an associated website, along with data resources for projects and assignments, and online video lectures.
Other Pedagogical features of this book include: "War Stories" offering perspectives on how data science techniques apply in the real world; "False Starts" revealing the subtle reasons why certain approaches fail; "Take-Home Lessons" emphasizing the big-picture concepts to learn from each chapter; "Homework Problems" providing a wide range of exercises for self-study; "Kaggle Challenges" from the online platform Kaggle; examples taken from the data science television show "The Quant Shop," and; concluding notes in each tutorial chapter pointing readers to primary sources and additional references.
The Data Science Design Manual Reviews
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This was a nice read. The war stories are very illuminating, it eases from practice into theory quite nicely and the funny quotes interspersed into the war stories are enjoyable. The examples given were sometimes quite illuminating. Some examples are the intuition of p-values via the concept of permutation tests and the conceptual difference between SVMs and logistic regression (maximising margin between the closest points from each side versus maximizing the total confidence of our classification over all points). Other times, things were supposed to be illuminating, but weren't so much (an example is the duality between points and lines in linear regression). This might have to do with the background knowledge of the reader, of course. Theoretical parts were sometimes hard to follow, because they were described very briefly due to the book's character to be a summary of techniques, instead of a deep dive. An is the sudden jump into the explanation of how eigenvalues can be used for clustering, even though the explanations for clustering were otherwise insightful and simple. I settled on a 4-star rating, because it was a nice book I learned a lot from, but there were bits that felt they could use some more editing so that they can be more easily palatable to the reader and this is what kept me from giving a 5-star one.
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I used this for my masters thesis and it really helped with all the tasks and methods used in data science. I do wish there was a little more about verification and validation, but I found the rest of the book very useful.
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Great reference book . Mostly from a CS perspective with lot's of intuition . Currently a reference book for all my ML/DS related projects.
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Incredibly well written and complete book. It’s my go-to book to recommend to technical people wanting to dig deeper into data science.
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Best book to start a career in data science.
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Very good book to understand concepts and math behind machine learning
