Day 1 | Day 2 | |

Lecture 1: Foundations of kdb+ and the q programming language | Lecture 1: Introduction to Data science and machine learning crash course | |

Tutorial 1 | Tutorial 1 | |

Lecture 2: Working with tables and q-sql | Lecture 2: Tree-based regression and classification, random forests | |

Tutorial 2 | Tutorial 2 | |

Lecture 3: Big data in kdb+/q | Lecture 3: Neural networks in kdb+/q | |

Tutorial 3 | Tutorial 3 | |

Lecture 4: Tickerplant architecture for data captures | Lecture 4: Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search | |

Tutorial 4 | Tutorial 4 | |

Lab | Lab |

**Big Data, High-Frequency Data, and Machine Learning with kdb+/q Workshop**

q is a programming language for array processing, developed by Arthur Whitney on the basis of Kenneth E. Iverson’s APL. The kdb+ database built on top of q is a *de facto* standard technology for dealing with rapidly arriving, high-frequency, big data.

kdb+/q has taken the world of electronic, including algorithmic, trading by storm. It is used by numerous sell-side and buy-side institutions, including some of the most successful hedge funds and electronic market makers.

Beyond the world of electronic trading, kdb+/q is used in retail, gaming, manufacturing, telco, IoT, life sciences, utilities, and aerospace industries.

Your course will take you through the foundations of kdb+/q and explain why it is a language of choice for Big Data, high-frequency data, and real-time event processing.

We shall explain how to work with tables and q-sql effectively, how to set up tickerplants, real-time, and historical instances, and how to apply kdb+/q to machine learning problems.

We shall consider advanced applications to tree-based regression and classification, random forests, deep learning, Google DeepMind and Monte Carlo search, producing demonstrations on real-life data examples.

This event is in conjunction with The Thalesians.

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

- Foundations of kdb+ and the q programming language
- Working with tables and q-sql
- Big data in kdb+/q
- Tickerplant architecture for data captures
- Data science and machine learning crash course
- Tree-based regression and classification, random forests
- Neural networks in kdb+/q
- Applications of machine learning in kdb+/q; Google DeepMind and Monte Carlo search