Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David 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)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Understanding dynamic dashboards

An alternative approach to building a dashboard of your own is to make an actual web application, with a live server running Python on a backend; this will, upon request, show you a dashboard. This approach is, essentially, the exact opposite of a static dashboard in terms of pros and cons: it requires maintenance, needs to be scaled if the traffic is heavy, and could be slower. It also allows you to configure access, customize dashboards for any user or group of users, and compute the results live, even for a comparatively large dataset, without the need to share this dataset as a whole with the audience.

Of course, we could build an entire web application, controlling each and every feature (we won't do that), or use one of the specialized dashboard packages, such as uperset (essentially, a full-blown platform that requires database access...