Data Wrangling, 1st ed.
Munging in R with SQL and MongoDB for Financial Applications

Language: English
Publication date:
280 p. · 15.5x23.5 cm · Paperback
Publication Abandoned

Use R to gather, clean, and manage financial data in structured and unstructured databases. Learn how to read and write the increasing volume and complexity of data from and between SQL and MongoDB databases.

Data Wrangling teaches practitioners and students of financial data analysis the SQL and MongoDB database management skills they need to succeed in their analytic work. The authors, who have deep experience in the financial industry as well as in teaching quantitative finance, take most of the operational and programming examples that enrich their book from the financial arena, including both market data and text-based data. The concepts presented through these examples are nonetheless applicable to a wide range of fields, so data analysts from all industries will profit from this book.

What You'll Learn

  • Use a rich feature set of R for financial data analytics
  • Employ an integrated comparison-based learning approach to SQL and NoSQL database management, including query and insert constructs
  • Understand data wrangling best practices and solutions
  • Be exposured to cutting-edge database technologies such as text-based analytics and their financial applications
  • Study an abundance of practical examples from the real world of finance

Who This Book Is For

Data analysts in the financial industry, data analysts in nonfinancial fields, and those who deal with data in their professional or academic work

Patrick Houlihan is a Lecturer in Quantitative Finance at the Stevens Institute of Technology, with 15 years of professional industry experience. He was a quantitative analyst for Jefferies LLC; senior field applications engineer for Nvidia supporting GPU and compute products for Dell Consumer (Dimension); senior field applications engineer for Altera, covering Hewlett Packard's workstation and server lines and field application engineering roles at Altium and Arrow Electronics. Patrick received an MSFE from Stevens Institute of Technology and an MBA in Investment Management and BSEE in Electrical Engineering from Drexel University. He is pursuing his doctorate in Financial Engineering at Stevens.

Teaches practitioners and students how to gather, clean, and manage financial data in R for data analysis of SQL and MongoDB databases

Covers data wrangling, the most universally portable skill set in the realm of data science because it is a prerequisite of any real-world data analysis task