End-to-End Data Science with SAS

A Hands-On Programming Guide

James Gearheart

• Publisher: SAS Institute
• Year: 2020
• ISBN:
  9781642958065
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Learn data science concepts with real-world examples in SAS!

End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step.

Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user's guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model's effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.

Chapter 1: Data Science Overview

Introduction to This Book

Minimum Effective Dose

The Current Data Science Landscape

Types of Analytics

Data Science Skills

Introduction to Data Science Concepts

Supervised Versus Unsupervised

Parametric Versus Non-parametric

Regression Versus Classification

Overfitting Versus Underfitting

Batch Versus Online Learning

Bias-Variance Tradeoff

Step-by-Step Example of Finding Optimal Model Complexity

Curse of Dimensionality

Transparent Versus Black Box Models

No Free Lunch

Chapter Review

Introduction to This Book

Over the past decade, there has been an explosion of interest in the application of statistics to the vast amounts of data being generated every second by nearly everyone on the planet. This interest has been driven primarily by tech giants that learned how to monetize this information by providing free services to individuals in exchange for the data that these individuals generate. Every click, tweet, picture upload, page view, like, share, follow, purchase, comment, retweet, email, and logon is stored in vast data warehouses maintained by tech giants and made available to their army of data scientists to turn your data into actionable insights.

You may feel flattered that you have a secret admirer that obsesses about your every click, your interests, your hopes and dreams, your wants, your insecurities, and your wildest fantasies. You may envision tech billionaires like Mark Zuckerberg or Jeff Bezos lying awake at night thinking about you and how to give you exactly what you want. Maybe that image is a bit of a stretch, but these CEOs, and all of their competitors, do have the customer at the center of their business models. This phenomenon is not limited to tech companies. Traditional brick and mortar stores (Walmart, Target, Best Buy), telecommunication companies (AT&T, Verizon, Comcast), medical institutions, entertainment conglomerates, utility companies, and government offices all have teams of data scientists whose sole function is to know what you want before you even know it yourself.

Amazon knows that because of your interest in data science books that you might be interested in this one. Netflix knows that because you binged the entire first three seasons of Black Mirror over the past weekend, that you will also be interested in the reboot of The Outer Limits. These insights are the result of teams of data scientists who access the information that you have freely provided and develop models that categorize individuals into similar groups based on a variety of attributes. They also develop predictive models that try to determine what you will do or want in the future.

Don’t believe me? Here is a quick test. Based on the only information that I have about you (your interest in data science books focusing on SAS), I will assume that you are a male aged between 25 and 40 years old with graduate-level education and live in an English-speaking country. You probably work in a large corporation. Maybe your desk is a bit messy, but you have a method to the madness.

How’d I do? If these guesses were a complete miss, then you must be a unique individual to not fall into any of the traditional demographic and interest categories of data scientists. But I would guess that at least half of these descriptions pertain to you. These guesses might appear to be a bit broad or general. But remember, they are based on only a single general piece of information: your interest in data science books focused on SAS.

Now imagine that I have a database of your entire web history, including all your past purchases, your social connections, your financial statements, your search terms, your emails, and your viewing habits. Imagine that I took all of this information and inp...