Book Image

The Applied SQL Data Analytics Workshop - Second Edition

By : Matt Goldwasser, Upom Malik, Benjamin Johnston
3.5 (2)
Book Image

The Applied SQL Data Analytics Workshop - Second Edition

3.5 (2)
By: Matt Goldwasser, Upom Malik, Benjamin Johnston

Overview of this book

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.
Table of Contents (9 chapters)
Preface
7
7. The Scientific Method and Applied Problem Solving

Introduction

In the previous chapter, we developed the skills necessary to effectively analyze data within a SQL database, and in this chapter, we will turn our attention to the efficiency of this analysis, investigating how we can increase the performance of our SQL queries. Efficiency and performance are key components of data analytics since without considering these factors, physical constraints such as time and processing power can significantly affect the outcome of an analysis. To elaborate on these limitations, we can consider two separate scenarios.

Say that we are performing post hoc analysis (analysis after the fact or event). In this first scenario, we have completed a study and have collected a large dataset of individual observations of a variety of different factors or features. One such example is described within our dealership sales database, which analyzes the sales data for each customer.

With the data collection process, we want to analyze the data for patterns...