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SQL for Data Analytics

You're reading from SQL for Data Analytics Harness the power of SQL to extract insights from data

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781801812870

Length 540 pages

Edition 3rd Edition

Languages

SQL

Concepts

Data Analysis

Authors (4):

Benjamin Johnston

Matt Goldwasser

Jun Shan

Upom Malik

View More author details

Table of Contents (11) Chapters

Preface

1. Understanding and Describing Data

2. The Basics of SQL for Analytics FREE CHAPTER

3. SQL for Data Preparation

4. Aggregate Functions for Data Analysis

5. Window Functions for Data Analysis

6. Importing and Exporting Data

7. Analytics Using Complex Data Types

8. Performant SQL

9. Using SQL to Uncover the Truth: A Case Study

Appendix

Summary

Data analytics is a powerful method through which you analyze raw data to find patterns and gather predictions that help you to understand the world. The goal of analytics is to turn data into information and knowledge. To accomplish this goal, statistics, or descriptive statistics and statistical significance testing, are used to understand data.

Univariate analysis, a branch of descriptive statistics, can be utilized to understand a single variable of data. It can also be used to find outliers and the distribution of data by utilizing frequency distributions and quantiles. It is useful in finding the central tendency of a variable by calculating the mean, median, and mode of data and the dispersion of data using the range, standard deviation, and IQR.

Bivariate analysis is also used to understand the relationship between datasets. You can determine trends, changes in trends, periodic behavior, and anomalous points regarding two variables by using scatterplots. You can...

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Authors (4)

Jun Shan

Jun Shan

Jun Shan is an expert information technology professional who has been designing and implementing data management systems for more than 20 years. He also teaches SQL and Relational Database at Columbia University in the City of New York and Saint Peter's University. He completed his Master of Science in Computer Science from Virginia Tech and is currently a solution architect in a top 3 cloud computing service provider.

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Matt Goldwasser

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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Upom Malik

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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Benjamin Johnston

Benjamin Johnston

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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