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kdb+ and q: Analyze Time Series Data

21 Hours
kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc. In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q. By the end of this training, participants will be able to:
  • Understand the difference between a row-oriented database and a column-oriented database
  • Select data, write scripts and create functions to carry out advanced analytics
  • Analyze time series data such as stock and commodity exchange data
  • Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
  • Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
  • Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
  • Developers
  • Database engineers
  • Data scientists
  • Data analysts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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KDB+/Q/Python Financial Data Ananlysis

21 Hours
Audience *KDB+/Q developer Format of the course 50% lectures, 40% labs, 10% tests
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Testimonials (1)

Good introduction to the subject, the trainer totally adapted to my requests

Mathieu - Crédit Agricole Corporate & Investment Bank
Course - KDB+/Q/Python Financial Data Ananlysis

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