Book Image

Data Science Using Python and R

By : Chantal D. Larose, Daniel T. Larose
Book Image

Data Science Using Python and R

By: Chantal D. Larose, Daniel T. Larose

Overview of this book

Data science is hot. Bloomberg named a data scientist as the ‘hottest job in America’. Python and R are the top two open-source data science tools using which you can produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Each chapter in the book presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. You’ll learn how to prepare data, perform exploratory data analysis, and prepare to model the data. As you progress, you’ll explore what are decision trees and how to use them. You’ll also learn about model evaluation, misclassification costs, naïve Bayes classification, and neural networks. The later chapters provide comprehensive information about clustering, regression modeling, dimension reduction, and association rules mining. The book also throws light on exciting new topics, such as random forests and general linear models. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. By the end of this book, you’ll have enough knowledge and confidence to start providing solutions to data science problems using R and Python.
Table of Contents (20 chapters)
Free Chapter
1
ABOUT THE AUTHORS
17
INDEX
18
END USER LICENSE AGREEMENT

2.1 DOWNLOADING PYTHON

To run Python code, you need to use a Python compiler. In this text, we will be using the Spyder compiler, which is included in the Anaconda software package. By downloading and installing Anaconda, we will also download and install Python at the same time.

To download Anaconda, go to the Spyder installation page1 and select the Anaconda link under either the Windows or MacOS X options. After the installation is complete, locate the Spyder program and open it.

When you open Spyder for the first time, you will see the screen shown in Figure 2.1. The left‐hand box is where you will write Python code. That box is where we will spend most of our time. The top‐right box lists data sets and other items that have been created by running Python code. The bottom‐right box is where our output will appear, as well as any error messages or other information.

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Figure 2.1 The Spyder window when you first open the program.