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The Applied SQL Data Analytics Workshop - Second Edition
The Applied SQL Data Analytics Workshop - Second Edition

The Applied SQL Data Analytics Workshop: Develop your practical skills and prepare to become a professional data analyst, Second Edition

By Matt Goldwasser , Upom Malik , Benjamin Johnston
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Book Feb 2020 484 pages 2nd Edition
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Product Details

Publication date : Feb 28, 2020
Length 484 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781800203679
Category :
Data
Languages :
SQL (Intermediate)
Concepts :
Data Analysis
Tools :
Anaconda (Beginner) , PostgreSQL (Beginner)
Table of content icon View table of contents Preview book icon Preview Book

The Applied SQL Data Analytics Workshop - Second Edition

2. SQL for Data Preparation

Overview

In this chapter, you will learn how to clean and prepare data for analysis using SQL techniques. We will begin by first learning to combine multiple tables and queries together into a dataset using joins, unions, subqueries, and functions to transform data before moving on to more advanced material. By the end of this chapter, you will be able to transform and clean data using SQL functions and remove duplicate data using the DISTINCT and DISTINCT ON commands.

Introduction

In the previous chapter, we discussed the basics of data analysis and SQL. We also used CRUD (create, read, update, and delete) operations on a table. These techniques are the foundation for all the work undertaken in analytics. One such task we will implement is the creation of clean datasets.

According to Forbes, it is estimated that almost 80% of the time spent by analytics professionals involves preparing data for use in analysis and building models with unclean data, which harms analysis by leading to poor conclusions. SQL can help in this tedious but important task by providing efficient ways to build clean datasets.

We will start by discussing how to assemble data using JOIN and UNION. Then, we will use different functions, such as CASE WHEN, COALESCE, NULLIF, and LEAST/GREATEST, in order to clean data. We will then discuss how to transform and remove duplicate data from queries using the DISTINCT command.

Assembling Data

We have previously discussed how to perform operations with a single table. But what if you need data from two or more tables? In this section, we will assemble data in multiple tables using joins and unions.

Connecting Tables Using JOIN

In the previous chapter, we discussed how to query data from a table. However, most of the time, the data you are interested in is spread across multiple tables. Fortunately, SQL has methods for bringing related tables together using the JOIN keyword.

To illustrate, let's take a look at two tables in our database—dealerships and salespeople.

Figure 2.1: Dealerships table structure

Figure 2.2: Salespeople table structure

Figure 2.2: Salespeople table structure

In the salespeople table, we observe that we have a column called dealership_id. This dealership_id column is a direct reference to the dealership_id column in the dealerships table. When table A has a column that references the primary key of...

Transforming Data

Often, the raw data presented in a query output may not be in the form we would like it to be. We may want to remove values, substitute values, or map values to other values. To accomplish these tasks, SQL provides a wide variety of statements and functions. Functions are keywords that take in inputs (such as a column or a scalar value) and change those inputs into some sort of output. We will discuss some very useful functions for cleaning data in the following sections.

The CASE WHEN Function

CASE WHEN is a function that allows a query to map various values in a column to other values. The general format of a CASE WHEN statement is as follows:

CASE WHEN condition1 THEN value1
WHEN condition2 THEN value2
…
WHEN conditionX THEN valueX
ELSE else_value END;

Here, condition1 and condition2, through conditionX, are Boolean conditions; value1 and value2, through valueX, are values to map the Boolean conditions; and else_value is the value that is mapped...

Summary

SQL provides us with many tools for mixing and cleaning data. We have learned how joins allow users to combine multiple tables, while UNION and subqueries allow us to combine multiple queries. We have also learned how SQL has a wide variety of functions and keywords that allow users to map new data, fill in missing data, and remove duplicate data. Keywords such as CASE WHEN, COALESCE, NULLIF, and DISTINCT allow us to make changes to data quickly and easily.

Now that we know how to prepare a dataset, we will learn how to start making analytical insights in the next chapter, using aggregates and window functions.

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Key benefits

  • Explore ways to use SQL for data analytics and gain key insights from your data
  • Study advanced analytics, such as geospatial and text analytics
  • Discover ways to integrate your SQL pipelines with other analytics technologies

Description

Every day, businesses operate around the clock and a huge amount of data is generated at a rapid pace. Hidden in this data are key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. Are you ready to enter the exciting world of data analytics and unlock these useful insights? Written by a team of expert data scientists who have used their data analytics skills to transform businesses of all shapes and sizes, The Applied SQL Data Analytics Workshop is a great way to get started with data analysis, showing you how to effectively sieve and process information from raw data, even without any prior experience. The book begins by showing you how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you'll learn how to write SQL queries to aggregate, calculate and combine SQL data from sources outside of your current dataset. You'll also discover how to work with different data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you'll finally be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of The Applied SQL Data Analytics Workshop, you'll have the skills you need to start identifying patterns and unlocking insights in your own data. You will be capable of looking and assessing data with the critical eye of a skilled data analyst.

What you will learn

Understand what data analytics is and why it is important Experiment with data analytics using basic and advanced queries Interpret data through descriptive statistics and aggregate functions Export data from external sources using powerful SQL queries Work with and manipulate data using SQL joins and constraints Speed up your data analysis workflow by automating tasks and optimizing queries

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Product Details

Publication date : Feb 28, 2020
Length 484 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781800203679
Category :
Data
Languages :
SQL (Intermediate)
Concepts :
Data Analysis
Tools :
Anaconda (Beginner) , PostgreSQL (Beginner)

Table of Contents

9 Chapters
Preface Chevron down icon Chevron up icon
About the Book
1. Introduction to SQL for Analytics Chevron down icon Chevron up icon
1. Introduction to SQL for Analytics
Introduction
The World of Data
Methods of Descriptive Statistics
Statistical Significance Testing
Relational Databases and SQL
Basic Data Types of SQL
Reading Tables: The SELECT Query
Creating Tables
Updating Tables
Deleting Data and Tables
SQL and Analytics
Summary
2. SQL for Data Preparation Chevron down icon Chevron up icon
2. SQL for Data Preparation
Introduction
Assembling Data
Transforming Data
Summary
3. Aggregate and Window Functions Chevron down icon Chevron up icon
3. Aggregate and Window Functions
Introduction
Aggregate Functions
Aggregate Functions with GROUP BY
The HAVING Clause
Using Aggregates to Clean Data and Examine Data Quality
Window Functions
Statistics with Window Functions
Summary
4. Importing and Exporting Data Chevron down icon Chevron up icon
4. Importing and Exporting Data
Introduction
The COPY Command
Using R with Our Database
Using Python with Our Database
Best Practices for Importing and Exporting Data
Summary
5. Analytics Using Complex Data Types Chevron down icon Chevron up icon
5. Analytics Using Complex Data Types
Introduction
Date and Time Data Types for Analysis
Performing Geospatial Analysis in PostgreSQL
Using Array Data Types in PostgreSQL
Using JSON Data Types in PostgreSQL
Text Analytics Using PostgreSQL
Summary
6. Performant SQL Chevron down icon Chevron up icon
6. Performant SQL
Introduction
Database Scanning Methods
Performant Joins
Functions and Triggers
Summary
7. The Scientific Method and Applied Problem Solving Chevron down icon Chevron up icon
7. The Scientific Method and Applied Problem Solving
Introduction
Case Study
Summary
Appendix Chevron down icon Chevron up icon
1. Introduction to SQL for Analytics
2. SQL for Data Preparation
3. Aggregate and Window Functions
4. Importing and Exporting Data
5. Analytics Using Complex Data Types
6. Performant SQL
7. The Scientific Method and Applied Problem Solving

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Authors (7)

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Profile icon Upom Malik
Upom Malik is a data science and analytics leader who has worked in the technology industry for over 8 years. He has a master's degree in chemical engineering from Cornell University and a bachelor's degree in biochemistry from Duke University. As a data scientist, Upom has overseen efforts across machine learning, experimentation, and analytics at various companies across the United States. He uses SQL and other tools to solve interesting challenges in finance, energy, and consumer technology. Outside of work, he likes to read, hike the trails of the Northeastern United States, and savor ramen bowls from around the world.
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Profile icon Benjamin Johnston
Github
Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
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Profile icon Surendranatha Reddy Chappidi
Contacted MCQ TR for "SQL for Data Analytics" on 29/1/20 by Anindya Sil
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Profile icon Joshua Görner
LinkedIn Github
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Profile icon Mr. Matt Goldwasser
Matt Goldwasser is the Head of Applied Data Science at the T. Rowe Price NYC Technology Development Center. Prior to his current role, Matt was a data science manager at OnDeck, and prior to that, he was an analyst at Millennium Management. Matt holds a bachelor of science in mechanical and aerospace engineering from Cornell University.
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Profile icon Scott Cosentino
Scott Cosentino is a software developer and teacher currently working in computer security. Scott has worked extensively with both low- and high-level languages, working on operating system- and enterprise-level applications. Scott has a passion for teaching and currently writes and creates videos on computer security and other programming topics. He has developed an extensive library of courses and has taught over 45,000 students through courses with Udemy, Packt, and CodeRed. He maintains a blog on Medium, and is active on YouTube and LinkedIn, where he enjoys creating content and interacting with students.
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