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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Deploying and testing your API loads with Locust

Once the application is deployed, but before it is publicly announced or used, it is a good idea to estimate how many requests it can handle. Usually, you can roughly predict the requirements for the service by estimating the number of requests it needs to execute at peak periods, how long those periods are, how fast it should respond, and so on. Once you're clear on the requirements, you'll need to test-load your application.

Test-loads should be performed on the actual, deployed server, not your localhost. Here, we skip over the whole topic of deploying your model. We also didn't use ngnix or any similar gateway servers, which would cache requests, boosting the performance of the API significantly. Deployment of the application deserves a separate book and can be achieved in many ways, depending on your existing...

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