Selected Store
Skroutz Buyers Protection
Set the delivery location to see products according to your choice.
© 20[0-9]{2} Skroutz SA All Rights and Lefts reserved. FAQ | Terms of use | Privacy Policy | Cookie Policy
Selected Store
Skroutz Buyers Protection
Scientific Books
Scientific Books
Prices are calculated for:Malta, Other Payment Options
To truly learn data science, you must not only master the data science libraries, frameworks, modules, and toolkits, but also understand the ideas and principles that govern their operation.
This second edition of Data Science: Essential Principles and Applications with Python, updated for Python 3.6, shows you how these tools and algorithms work by applying them from the ground up. If you have an inclination towards mathematics and programming skills, author Joel Grus will help you feel comfortable with the mathematics and statistics at the core of data science, as well as the necessary "hacking" knowledge required to get started as a data scientist. With new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the hidden gems within today's chaotic data jungle.
Specifications are collected from official manufacturer websites. Please verify the specifications before proceeding with your final purchase. If you notice any problem you can report it here.
The book is aimed at those who truly want to professionally engage with Python applications in data science and all its subsets (Machine Learning, Deep Learning, etc). It is partly good that it does not use the libraries scipy, scikit-learn, and tensorflow. Instead, it implements simple statistical metrics and relatively complex classifier models (Naive Bayes) in detail. This is the best approach for someone who has no prior experience in data analysis, as it allows them to see the basic mathematical tools that play a role. The book assumes basic knowledge of Python, and anyone who understands, understands. It comments on each method and occasionally provides advice. It might overwhelm you a bit with the frequent use of functions, but in the end, it will prove useful as you see how to write reusable code. Additionally, you will never be asked to write knn, k-means, or naive bayes from scratch like Grus does. Once you finish this book, you will be able to start exploring the scipy, scikit-learn, and tensorflow libraries and call each model with just one command. You may ask why then should you read this book that does everything from scratch? I would say that a data scientist should be a good mathematician-statistician, in addition to being a competent programmer. Therefore, by understanding the mathematics behind classification, clustering, and regression algorithms, you will gain an understanding of each algorithm's concept, which method is best suited for specific problems, and of course, how to optimize each method. Personally, I would have liked more comments and advice targeted at working with data (preprocessing, filling empty values) and dense clustering algorithms. In conclusion, if you partly agree with what I'm saying, take it and you don't need to read all the chapters right away. I recommend chapters 1 to 18 and 20.
Verified purchase
Verified purchase