# Seminar »Data Processing with NumPy« from September 27 to September 28, 2017

When | Sep 27, 2017 09:00 AM
to
Sep 28, 2017 05:00 PM |
---|---|

Where | Veit Schiele Communications GmbH, Mansteinstr. 7, D-10783 Berlin |

Contact Name | Veit Schiele |

Contact Phone | +49 30 8185667-1 |

Add event to calendar | vCal iCal |

### Prerequisites

Basic knowledge of Python

### Target Audience

Analysts, researchers and engineers who would like to carry out numerical calculations on their data.

### Course Description

NumPy is the most frequently used Python library for processing numerical data. It combines a programmer-friendly Python interface with the speed of an implementation in pure C. NumPy allows to implement calculations with large data series and matrices with few lines of code. Therefore, NumPy is a perfect fit to optimize the runtime of Python programs.

In this course you will get a hands-on introduction of NumPys essentials, featuring many practical examples. To build upon the basic functionality, the SciPy package featuring a plethora of mathematical tools that make the best use of NumPy will be covered as well.

### Duration

2 days

### Course Outline

Day 1 | Day 2 |
---|---|

Introduction to NumPy | Broadcasting |

Functions / ufuncs | Optimization with NumPy |

Indexing | The Scipy Library |

Typical Applications | Related Python Libraries |

#### Introduction to NumPy

- overview of the functionality in NumPy
- arrays
- dtypes
- reshape
- creating arrays
- loading/saving data

#### Indexing

- indexing arrays
- views
- fancy indexing
- sorting
- set operations

#### Functions

- built-in functions
- ufuncs
- matrix operations
- rotating coordinates

#### Optimization

- eliminating Python loops with NumPy
- sparse matrices
- identifying bottlenecks with cProfile

#### Typical Applications

- recommender systems
- Eigenvectors
- the PageRank algorithm
- implementing a neural network in NumPy

#### Broadcasting

- broadcasting
- stacking
- raveling

#### The SciPy Library

- finding zeroes
- fitting polynomial functions
- Fourier transformation
- data visualization