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The Data Science Workshop

You're reading from   The Data Science Workshop A New, Interactive Approach to Learning Data Science

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838981266
Length 818 pages
Edition 1st Edition
Languages
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Authors (5):
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Thomas Joseph Thomas Joseph
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Thomas Joseph
Andrew Worsley Andrew Worsley
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Andrew Worsley
Robert Thas John Robert Thas John
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Robert Thas John
Anthony So Anthony So
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Anthony So
Dr. Samuel Asare Dr. Samuel Asare
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Dr. Samuel Asare
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Table of Contents (18) Chapters Close

Preface 1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning 16. Machine Learning Pipelines 17. Automated Feature Engineering

Overview of Python

As mentioned earlier, Python is one of the most popular programming languages for data science. But before diving into Python's data science applications, let's have a quick introduction to some core Python concepts.

Types of Variable

In Python, you can handle and manipulate different types of variables. Each has its own specificities and benefits. We will not go through every single one of them but rather focus on the main ones that you will have to use in this book. For each of the following code examples, you can run the code in Google Colab to view the given output.

Numeric Variables

The most basic variable type is numeric. This can contain integer or decimal (or float) numbers, and some mathematical operations can be performed on top of them.

Let's use an integer variable called var1 that will take the value 8 and another one called var2 with the value 160.88, and add them together with the + operator, as shown here:

var1 =...
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