The Applied SQL Data Analytics Workshop, Second Edition

Malik, Upom, Goldwasser, Matt, Johnston, Benjamin

  • 出版商: Packt Publishing
  • 出版日期: 2020-02-27
  • 售價: $1,170
  • 貴賓價: 9.5$1,112
  • 語言: 英文
  • 頁數: 404
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800203675
  • ISBN-13: 9781800203679
  • 相關分類: SQL資料科學

下單後立即進貨 (約1~2週)

相關主題

商品描述

You already know that you want to learn data analysis with SQL, and a smarter way to learn is to learn by doing. The Applied SQL Data Analytics Workshop focuses on building up your practical skills so that you can navigate and compose custom reports like an expert data analyst. You'll learn from real examples that lead to real results.

 

Throughout The Applied SQL Data Analytics Workshop, you'll take an engaging step-by-step approach to understand data analytics with SQL. You won't have to sit through any unnecessary theory. You can jump into a single exercise each day if you're short on time, or you can spend an entire weekend tinkering with SQLAlchemy and Python. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding.

 

Every physical print copy of The Applied SQL Data Analytics Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book.

 

Fast-paced and direct, The Applied SQL Data Analytics Workshop is the ideal companion for SQL beginners. You'll perform SQL queries like a professional data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.

作者簡介

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.