Data Wrangling
Munging in R with SQL and MongoDB for Financial Applications
(Sprache: Englisch)
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...
Data Wrangling teaches practitioners and...
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Klappentext zu „Data Wrangling “
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 andinsert 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
Data Wrangling is a pain management program for financial data analysts. Data analysts spend 90 percent of their work time wrangling (munging) data into a usable format. A corollary of this rule of thumb is that the tasks of gathering, cleaning, massaging, and managing data account for 80 percent of the costs of data warehousing projects. With the volume and complexity of financial data increasing exponentially, nothing is more crippling to efficient data analysis than inefficient data wrangling.
Yet practical instruction for data analysts in how to deal with the data management headaches that fill up a majority of their working hours is scattered and disorganized. Nor is it specifically taught in programming courses, where the focus is on the language and not data management. Most students learn the programming language R, but they do not learn the concomitant data management skills that are essential to data analysis: in particular, how to read and write data from and between structured database technologies—notably SQL—and unstructured DBs—notably MongoDB . As a result, graduates are left ill-prepared for jobs in the real world, where financial companies seek analysts whose skill sets encompass data management in tandem with data analysis.
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.
Yet practical instruction for data analysts in how to deal with the data management headaches that fill up a majority of their working hours is scattered and disorganized. Nor is it specifically taught in programming courses, where the focus is on the language and not data management. Most students learn the programming language R, but they do not learn the concomitant data management skills that are essential to data analysis: in particular, how to read and write data from and between structured database technologies—notably SQL—and unstructured DBs—notably MongoDB . As a result, graduates are left ill-prepared for jobs in the real world, where financial companies seek analysts whose skill sets encompass data management in tandem with data analysis.
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.
Inhaltsverzeichnis zu „Data Wrangling “
Autoren-Porträt von Patrick Houlihan, Alexander Moreno
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.
Bibliographische Angaben
- Autoren: Patrick Houlihan , Alexander Moreno
- 2020, 1st ed., 280 Seiten, Maße: 17,8 x 25,4 cm, Kartoniert (TB), Englisch
- Verlag: APress
- ISBN-10: 1484206126
- ISBN-13: 9781484206126
- Erscheinungsdatum: 23.03.2020
Sprache:
Englisch
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