Python for Data Science For Dummies

John Paul Mueller, Luca Massaron

• Publisher: For Dummies
• Year: 2019
• Print ISBN: 9781119547624
• eBook ISBN: 9781119547662
• Edition: 2
• Topic: Computer Science
• Subtopic: Programming
• Index: Computer Science

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to poweringInstagram, to building video games with the PyGame library.

Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.

• Get started with data science and Python
• Visualize information
• Wrangle data
• Learn from data

The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Part 1

Getting Started with Data Science and Python

IN THIS PART …

Understanding how Python can make data science easier.

Defining the Python features commonly used for data science.

Creating a Python setup of your own.

Working with Google Colab on alternative devices.

Chapter 1

Discovering the Match between Data Science and Python

IN THIS CHAPTER

Discovering the wonders for data science

Exploring how data science works

Creating the connection between Python and data science

Getting started with Python

Data science may seem like one of those technologies that you’d never use, but you’d be wrong. Yes, data science involves the use of advanced math techniques, statistics, and big data. However, data science also involves helping you make smart decisions, creating suggestions for options based on previous choices, and making robots see objects. In fact, people use data science in so many different ways that you literally can’t look anywhere or do anything without feeling the effects of data science on your life. In short, data science is the person behind the partition in the experience of the wonderment of technology. Without data science, much of what you accept as typical and expected today wouldn’t even be possible. This is the reason that being a data scientist is the sexiest job of the twenty-first century.

To make data science doable by someone who’s less than a math genius, you need tools. You could use any of a number of tools to perform data science tasks, but Python is uniquely suited to making it easier to work with data science. For one thing, Python provides an incredible number of math-related libraries that help you perform tasks with a less-than-perfect understanding of precisely what is going on. However, Python goes further by supporting multiple coding styles (programming paradigms) and doing other things to make your job easier. Therefore, yes, you could use other languages to write data science applications, but Python reduces your workload, so it’s a natural choice for those who really don’t want to work hard, but rather to work smart.

This chapter gets you started with Python. Even though this book isn’t designed to provide you with a complete Python tutorial, exploring some basic Python issues will reduce the time needed for you to get up to speed. (If you do need a good starting tutorial, please get Beginning Programming with Python For Dummies, 2nd Edition, by John Mueller (Wiley). You’ll find that the book provides pointers to tutorials and other aids as needed to fill in any gaps that you may have in your Python education.

CHOOSING A DATA SCIENCE LANGUAGE

There are many different programming languages in the world — and most were designed to perform tasks in a certain way or even make it easier for a particular profession’s work to be done with greater ease. Choosing the correct tool makes your life easier. It’s akin to using a hammer to drive a screw rather than a screwdriver. Yes, the hammer works, but the screwdriver is much easier to use and definitely does a better job. Data scientists usually use only a few languages because they make working with data easier. With this in mind, here are the top languages for data science work in order of preference:

• Python (general purpose): Many data scientists prefer to use Python because it provides a wealth of libraries, such as NumPy, SciPy, MatPlotLib, pandas, and Scikit-learn, to make data science tasks significantly easier. Python is also a precise language that makes it easy to use multi-processing on large datasets — reducing the time required to analyze them. The data science community has also stepped up with specialized IDEs, such as Anaconda, that implement the Jupyter Notebook concept, which makes working with data science calculations significantly easier (Chapter 3 demonstrates how to use Jupyter Notebook, so don’t worry about it in this chapter). Besides all of these things in Python’s favor, it’s also an excellent language for creating glue code with languages such as C/C++ and Fortran. The Python documentation actually shows how to create the required extensions. Most Python users rely on the language to see patterns, such as allowing a robot to see a group of pixels as an object. It also sees use for all sorts of scientific tasks.
• R (special purpose statistical): In many respects, Python and R share the same sorts of functionality but implement it in different ways. Depending on which source you view, Python and R have about the same number of proponents, and some people use Python and R interchangeably (or sometimes in tandem). Unlike Python, R provides its own environment, so you don’t need a third-party product such as Anaconda. However, R doesn’t appear to mix with other languages with the ease that Python provides.
• SQL (database management): The most important thing to remember about Structured Query Language (SQL) is that it focuses on data rather than tasks. Businesses can’t operate without good data management — the data is the business. Large organizations use some sort of relational database, which is normally accessible with SQL, to store their data. Most Database Management System (DBMS) products rely on SQL as their main language, and DBMS usually has a large number of data analysis and other data science features built in. Because you’re accessing the data natively, there is often a significant speed gain in performing data science tasks this way. Database Administrators (DBAs) generally use SQL to manage or manipulate the data rather than necessarily perform detailed analysis of it. However, the data scientist can also use SQL for various data science tasks and make the resulting scripts available to the DBAs for their needs.
• Java (general purpose): Some data scientists perform other kinds of programming that require a general purpose, widely adapted and popular, language. In addition to providing access to a large number of libraries (most of which aren’t actually all that useful for data science, but do work for other needs), Java supports object orientation better than any of the other languages in this list. In addition, it’s strongly typed and tends to run quite quickly. Consequently, some people prefer it for finalized code. Java isn’t a good choice for experimentation or ad hoc queries.
• Scala (general purpose): Because Scala uses the Java Virtual Machine (JVM) it does have some of the advantages and disadvantages of Java. However, like Python, Scala provides strong support for the functional programming paradigm, which uses lambda calculus as its basis (see Functional Programmming For Dummies, by John Mueller [Wiley] for details). In addition, Apache Spark is written in Scala, which means that you have good support for cluster computing when using this language; — think huge dataset support. Some of the pitfalls of using Scala are that it’s hard to set up correctly, it has a steep learning curve, and it lacks a comprehensive set of ...

Table of contents

• Cover
• Title Page
• Table of Contents
• Introduction
• Part 1: Getting Started with Data Science and Python
• Part 2: Getting Your Hands Dirty with Data
• Part 3: Visualizing Information
• Part 4: Wrangling Data
• Part 5: Learning from Data
• Part 6: The Part of Tens
• Index
• About the Authors
• Advertisement Page
• Connect with Dummies
• End User License Agreement