Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
9
9. Using SQL to Uncover the Truth – a Case Study

Using Python with Our Database

While R has a breadth of functionality, many data scientists and data analysts are starting to use Python. Why? Because Python offers a similarly high-level language that can be easily used to process data. While the number of statistical packages and functionality in R can still have an edge on Python, Python is growing fast, and has generally overtaken R in most of the recent polls. A lot of the Python functionality is also faster than R, in part because so much of it is written in C, a lower-level programming language.

The other large advantage that Python has is that it is very versatile. While R is generally only used in the research and statistical analysis communities, Python can be used to do anything from statistical analysis to standing up a web server. As a result, the developer community is much larger for Python. A larger development community is a big advantage because there is better community support (for example, on StackOverflow)...