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