Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Introduction

Data visualization is a powerful tool that allows users to digest large amounts of data very quickly. There are different types of plots that serve various purposes. In business, line plots and bar graphs are very common to display trends over time and compare metrics across groups, respectively. Statisticians, on the other hand, may be more interested in checking correlations between variables using a scatterplot or correlation matrix. They may also use histograms to check the distribution of a variable or boxplots to check for outliers. In politics, pie charts are widely used for comparing the total data between or among categories. Data visualizations can be very intricate and creative, being limited only by one's imagination.

The Python library Matplotlib is a well-documented, two-dimensional plotting library that can be used to create a variety of powerful data visualizations and aims to "...make easy things easy and hard things possible" (https://matplotlib...