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

Using R with Our Database

At this point, you can now copy data to and from a database. This gives you the freedom to expand beyond SQL to other data analytics tools (such as Excel) and incorporate any program that can read a CSV file as input into your pipeline. While almost any analytics tool can read a CSV file, you will still need to download the data. Adding more steps to your analytics pipeline can make your workflow more complex. Complexity can be undesirable because it necessitates additional maintenance and because it increases the number of failure points.

Another approach is to connect to your database directly in your analytics code. In this part of the chapter, we are going to look at how to do this in R—a programming language designed specifically for statistical computing. Later in the chapter, we will look at integrating our data pipelines with Python as well.

Why Use R?

While we have managed to perform aggregate-level descriptive statistics on our data...