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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

Summary

In this chapter, we introduced you to serverless functions—a different approach to APIs and computation in general. Serverless functions don't need maintenance, scale automatically, are secure, and are simple to write. They may be a great option for operations that don't need a huge amount of requests, or for when demand spikes unpredictably. In addition to serving as APIs, lambdas can be scheduled with one line of code or triggered by an external event, such as a new file landing in an S3 bucket. The downside of serverless applications is that they have strict memory limitations that could be a serious barrier for certain tasks. The response time could also be longer for the first time after a long break—but there are ways to solve that issue to some extent.

As a practice exercise, we were able to recreate our 311 API endpoints as serverless applications...

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