SQL for Data Scientists

Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning

SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls.

You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.

This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset."

• Gain an understanding of relational database structure, query design, and SQL syntax

• Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms

• Review strategies and approaches so you can design analytical datasets

• Practice your techniques with the provided database and SQL code

In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward!

Auteur

RENÉE M. P. TEATE is the Director of Data Science at HelioCampus, a higher ed tech startup based in the Washington, DC area. She prepares datasets with SQL, develops predictive models with Python, and designs interactive dashboards in Tableau for university decision-makers. She created the Becoming a Data Scientist podcast, helped build the data science learning community on Twitter, and is a sought-after speaker at industry conferences.

Texte du rabat

Jumpstart your data science career with crucial SQL skills

Today, many organizations expect their data scientists to be able to design and generate their own datasets by extracting and combining raw data from the company's data warehouses without the assistance of data engineers.

In SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis, experienced data scientist and database developer Renée M. P. Teate delivers a singular guide to the SQL skills and techniques every data scientist should know. You'll discover how to approach query design and develop SQL code to construct datasets for exploration, analysis, and data science.

SQL for Data Scientists shows you how to create datasets for use in applications like interactive reports and dashboards, as well as in machine learning algorithms. You'll skip right to the subset of SQL skills that data scientists and analysts use most frequently, and receive expert advice on extracting insights from data while avoiding common pitfalls.

• Understand fundamental SQL syntax and design effective SQL queries
• Conduct Exploratory Data Analysis with SQL
• Construct, filter, and sort your own datasets from pre-existing databases
• Use SQL JOINs to combine data from multiple database tables
• Design datasets for analytical reports and machine learning applications
• Apply more advanced SQL techniques such as Window Functions and Common Table Expressions
• Create database tables and views to store and retrieve the results of your queries

Résumé

Jump-start your career as a data scientistlearn to develop datasets for exploration, analysis, and machine learning

SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls.

You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data.

This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset."

• Gain an understanding of relational database structure, query design, and SQL syntax
• Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms
• Review strategies and approaches so you can design analytical datasets
• Practice your techniques with the provided database and SQL code
  In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward!

Contenu

Introduction xix

Chapter 1 Data Sources 1

Data Sources 1

Tools for Connecting to Data Sources and Editing SQL 2

Relational Databases 3

Dimensional Data Warehouses 7

Asking Questions About the Data Source 9

Introduction to the Farmer's Market Database 11

A Note on Machine Learning Dataset Terminology 12

Exercises 13

Chapter 2 The SELECT Statement 15

The SELECT Statement 15

The Fundamental Syntax Structure of a SELECT Query 16

Selecting Columns and Limiting the Number of Rows Returned 16

The ORDER BY Clause: Sorting Results 18

Introduction to Simple Inline Calculations 20

More Inline Calculation Examples: Rounding 22

More Inline Calculation Examples: Concatenating Strings 24

Evaluating Query Output 26

SELECT Statement Summary 29

Exercises Using the Included Database 30

Chapter 3 The WHERE Clause 31

The WHERE Clause 31

Filtering SELECT Statement Results 32

Filtering on Multiple Conditions 34

Multi-Column Conditional Filtering 40

More Ways to Filter 41

BETWEEN 41

IN 42

LIKE 43

IS NULL 44

A Warning About Null Comparisons 44

Filtering Using Subqueries 46

Exercises Using the Included Database 47

Chapter 4 CASE Statements 49

CASE Statement Syntax 50

Creating Binary Flags Using CASE 52

Grouping or Binning Continuous Values Using CASE 53

Categorical Encoding Using CASE 56

CASE Statement Summary 59

Exercises Using the Included Database 60

Chapter 5 SQL JOINs 61

Database Relationships and SQL JOINs 61

A Common Pitfall when Filtering Joined Data 71

JOINs with More than Two Tables 74

Exercises Using the Included Database 76

Chapter 6 Aggregating Results for Analysis 79

GROUP BY Syntax 79…