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Applied Computational Thinking with Python

Applied Computational Thinking with Python - Second Edition

By : Sofía De Jesús, Martinez
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Applied Computational Thinking with Python

Applied Computational Thinking with Python

By: Sofía De Jesús, 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 (25 chapters)
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1
Part 1: An Introduction to Computational Thinking
10
Part 2: Applying Python and Computational Thinking
16
Part 3: Data Processing, Analysis, and Applications Using Computational Thinking and Python

Problem 6 – using Python to analyze genetic data

Let’s shift focus to looking at a larger dataset. You’re working with laboratory mice and getting data for trisomy mice and protein expressions in these mice. We’ve truncated some of the data from the public domain file in Kaggle for this due to its huge size. We’re only focusing on six protein expressions for the mice and again, only the trisomy (down syndrome) mice in the study. The full file can be found on the Kaggle website at https://www.kaggle.com/ruslankl/mice-protein-expression. The truncated file can be found in this book’s GitHub repository.

Let’s say you don’t know where to start with this data. What should you even be looking at? Well, that’s often the first thing we encounter in data science. We don’t always get to be part of the study design or data collection process. Many times, we receive large data files and need to figure out what to look for...

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Applied Computational Thinking with Python
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