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  • Use SQL to summarize and identify patterns in data
  • Apply special SQL clauses and functions to generate descriptive statistics
  • Use SQL queries and subqueries to prepare data for analysis
  • Perform advanced statistical calculations using the window function
  • Analyze special data types in SQL, including geospatial data and time data
  • Import and export data using a text file and PostgreSQL
  • Debug queries that won't run
  • Optimize queries to improve their performance for faster results

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 business insights from data, this book is for you.

SQL for Data Analytics covers everything you need progress from simply knowing basic SQL to telling stories and identifying trends in data. You’ll be able to start exploring your data by identifying patterns 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 understand how to become productive with SQL with the help of profiling and automation to gain insights faster.

By the end of the book, you’ll able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of analytics professional.

  • Explore a variety of statistical techniques to analyze your data
  • Integrate your SQL pipelines with other analytics technologies
  • Perform advanced analytics such as geospatial and text analysis
Page Count 386
Course Length 11 hours 34 minutes
ISBN 9781789807356
Date Of Publication 23 Aug 2019


Upom Malik

Upom Malik is a data scientist who has worked in the technology industry for over 6 years. He has a master's degree in chemical engineering from Cornell University and a bachelor's degree in biochemistry from Duke University. He uses SQL and other tools to solve interesting challenges in finance, energy, and consumer technologies. While working on analytical problems, he has lived out of a suitcase and spent a year as a digital nomad. Outside of work, he likes to read, hike the trails of the Northeastern United States, and savor ramen bowls from around the world.

Matt Goldwasser

Matt Goldwasser is a manager on the data science team at T. Rowe Price, where he has leveraged machine learning techniques to solve business problems. In this role, he has helped the company build its data science capability from the ground up in the newly formed technology development center. Prior to his current role, Matt was a data science manager at OnDeck, where he led efforts to use machine learning to increase customer acquisition. He has a bachelors's in mechanical and aerospace engineering from Cornell University, where he met his wife. In his spare time, he enjoys teaching his infant son the basics of data science.

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 PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' 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.