SQL for Data Analytics

Perform fast and efficient data analysis with the power of SQL

Häftad, Engelska, 2019

AvUpom Malik,Matt Goldwasser,Benjamin Johnston

1 319 kr

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Take your first steps to become a fully qualified data analyst by learning how to explore large relational datasetsKey FeaturesExplore a variety of statistical techniques to analyze your dataIntegrate your SQL pipelines with other analytics technologiesPerform advanced analytics such as geospatial and text analysisBook DescriptionUnderstanding 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.What you will learnPerform advanced statistical calculations using the WINDOW functionUse SQL queries and subqueries to prepare data for analysisImport and export data using a text file and psqlApply special SQL clauses and functions to generate descriptive statisticsAnalyze special data types in SQL, including geospatial data and time dataOptimize queries to improve their performance for faster resultsDebug queries that won't runUse SQL to summarize and identify patterns in dataWho this book is forIf you’re a database engineer looking to transition into analytics, or a backend engineer who wants to develop a deeper understanding of production data, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL. Knowledge of basic SQL and database concepts will aid in understanding the concepts covered in this book.

Produktinformation

Utgivningsdatum2019-08-23
Mått191 x 235 x 21 mm
Vikt719 g
FormatHäftad
SpråkEngelska
Antal sidor386
FörlagPackt Publishing Limited
ISBN9781789807356

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Upom Malik is a data science and analytics leader who has worked in the technology industry for over eight years. He holds 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 throughout the United States. He uses SQL and other tools to solve complex challenges in finance, energy, and consumer technology. Outside of work, he enjoys reading, hiking the trails of the Northeastern United States, and savoring ramen bowls from around the world. Matt Goldwasser is Vice President and Head of AI and Data Science for Global Distribution at T. Rowe Price. He leads strategic initiatives using machine learning (ML) and advanced analytics across the organization. With over 8 years at T. Rowe Price, he brings expertise in applied data science, MLOps, and AWS, with a strong focus on operationalizing AI at scale. Previously, Matt held multiple roles at OnDeck, leading marketing analytics and building predictive models and automated ML pipelines. He also worked in data engineering, risk analysis, and product management at Millennium Management, GE, and the Port Authority of NY and NJ. He is known for turning complex challenges into scalable solutions and bridging strategy with hands-on innovation. 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 ML, 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 a first-class honors bachelor's degree in both engineering and medical science from the University of Sydney, Australia.