Veit Schiele Communications

Veit Schiele Communications
HomeDienstleistungenSchulungenSeminarkalenderSeminar »Data Processing with NumPy« from May 22 to May 23, 2017

Seminar »Data Processing with NumPy« from May 22 to May 23, 2017

After this course you will be able to write your own programs for processing data with NumPy and optimize them.
by Veit Schiele last modified Feb 17, 2017 10:18 AM
When May 22, 2017 09:00 AM to
May 23, 2017 05:00 PM
Where Veit Schiele Communications GmbH, Mansteinstr. 7, D-10783 Berlin
Contact Name
Contact Phone +49 30 8185667-1
Add event to calendarvCal
iCal
../../images/numpy.png/image_mini../../images/heatmap.png/image_mini../../images/histogramm.png/image_mini

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 1Day 2
Introduction to NumPyBroadcasting
Functions / ufuncsOptimization with NumPy
IndexingThe Scipy Library
Typical ApplicationsRelated 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