ILIAS Universität Bern
  • Login
Show Sidebar

Breadcrumb Navigation

Icon Course

2020-06-10 Data analytics (with NumPy and Pandas) (09:00-17:00)

Exploit the data science capabilities of Python using NumPy and Pandas.

Tabs

About this training
Data analytics with Python (NumPy and Pandas)

While Python provides a large variety of modules for various areas (file handling, regular expressions, audio files etc.) it lacks a complete solution for numerical calculations and data science. Those two capabilites are provided by the Numpy and Pandas packages, which together underly a large fraction of the Python scientific software stack. Numpy mainly provides structures called Arrays which allow for fast computation on any type of data taking the form of a list or matrix. Pandas provides tabular structures called Dataframes, implementing typical tabular operations such as joins, groups etc. and from which statistical information can be easily extracted. In this course you will discover these two packages through series of interactive Jupyter notebooks as well as exercises with real world data.

Learning outcomes - participants can/know
  • Numpy
    • Understand Numpy arrays
    • Create, modify, combine etc. Numpy arrays
    • Mathematics with Numpy arrays: linear algebra, statistics
    • Plotting Numpy arras using Matplotlib
  • Pands
    • Understand Pandas data structures: series and dataframes
    • Create, modify, combine etc. dataframes
    • Extract statistics from dataframes
    • Plotting dataframes using grammar of graphics (Altair, plotnine)
Target group
  • UNIBE staff members, students and potential users involved in scientific research
Prerequisites 
  • Participants must bring own laptops. They need to be experienced with basic Python programming.
Methods
  • Students have access to the Notebooks used during the lecture and can live experiment with them during the presentation and in pauses between presentation units.
Certificate 
  • A certificate will be delivered to participants who have attended the whole training.
  • Participants attending  DSF 1-3 are offered to make a project (30 hours). Upon a successful presentation 2 ECTS credit points are given. 
Coaches
  • The coaches are local or external experts
Practical information (time, location ...)
Time : 2020-06-10 09:00 - 17:00 
Location : Online

Training language: English
Participants : Max 25
Registraion : Mandatory
Coaches : Dr. Guillaume Witz
Prerequisites : Laptop, experience with basic Python
Certificate : Certificate for full training attendance 

Course material : You don't have to prepare anything for the course. You will be given access to a Jupyter environment that runs in your default browser and that is pre-loaded with the course material.
About ScITS
The Science IT Support is there to boost your research by supporting you solving computing challenges. 
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - scits.math.unibe.ch. 

Calendar (Block)

09. Jun 2020, 15:05
10. Jun 2020
10. Jun 2020
16. Jul 2020, 15:05