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Applied Computational Thinking with Python

Applied Computational Thinking with Python

By : Sofía De Jesús, Dayrene Martinez
4.6 (11)
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Applied Computational Thinking with Python

Applied Computational Thinking with Python

4.6 (11)
By: Sofía De Jesús, Dayrene Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Table of Contents (21 chapters)
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1
Section 1: Introduction to Computational Thinking
9
Section 2:Applying Python and Computational Thinking
14
Section 3:Data Processing, Analysis, and Applications Using Computational Thinking and Python
20
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Problem 7 – Using Python to analyze stocks

Time to play with some stocks. You can access a lot of data through Quandl, which allows for the use of a free API for educational uses. There are also premium datasets available. We're sticking to educational purposes, so that should be enough for our requirements.

In this problem, we're going to learn how to pull data from Quandl and look at the VZ stock prices. VZ is the code for Verizon stock prices. We're going to use them to predict the prices using quandl, which is a package for Python in addition to being a website full of useful information. Let's take a look at how we grab the information we want. The full code, minus the API key, can be found in our repository under the ch16_stockAnalysis.py file:

  1. Let's take a look at how we can import the data. You'll need your own API for this. If you want to check another stock, say for AMZN, you'd substitute 'EOD/VZ' with &apos...
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Applied Computational Thinking with Python
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