kdb+ and q: Analyze Time Series Data Training Course

Overview

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

Requirements

  • An understanding of statistics
  • Experience in the financial industry is helpful
  • An understanding of relational databases
  • Some experience with programming is helpful, but not required

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