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We had a wide range of Python experience in our group and each person gained something valuable to take away....

Dr. Ryan Woodard, Chair of Entrepreneurial Risks, ETH ETH Zurich, Switzerland more...


Very good introduction to the programming language.

Matthias Enderle, freelancer programmer about the German version of the course "Python for Programmers" more...


[The trainer] knows well what scientists need, so his hints are very practical and valuable. The hands-on course [..] covers a wide range of examples and will be very helpful in my daily work. ..

Dorota Jarecka, University of Warsaw, Poland about the course "Python for Scientists and Engineers" more...


Very good course, excellent content. Does require a strong python background which I lacked so perhaps my experience was not typical. Still I learned a very great deal.

Keith Bord, Oracle about the course "Advanced Python" more...


I enjoyed the course very much and learned a lot. My interest for quite few of topics was ignited during the course and I will into more details. I understood many principles. All in all: Very good training! Thank you very much all the best.

Dominik Schwinn, German Aerospace Stuttgart about the course "Python für Programmierer und Python für Wissenschaftler und Ingenieure" more...


Foundations of Data Sciences with Python

Dates for Open Courses

Course only available as in-house training. Please ask us at info@python-academy.de

Intended Audience

Data scientists with good knowledge of Python and of of basic Python data science libraries.

It is strongly recommended participants attend our course Foundations of Data Science with Python or have have inequivalent knowledge. This course can be combined with introductory courses (see Recommended Module Combinations) to achieve appropriate Python skills.

Motivation

Machine learning and natural language processing are important methods for data science. Python is commonly used in data science. This course provides an overview of important Python libraries for these data science fields.

Course Content

Machine Learning with Python

  • preprocessing
  • dimensional reduction
  • kernel transformations
  • regression and classification
  • model selection
  • cross-validation
  • grid-search

Natural Language Processing with Python

  • NLP
  • text modeling and classification
  • topic modeling
  • webscraping with bs4 and scrapy

Parallel Programming

  • multiprocessing
  • multithreading
  • MPI
  • introduction to hadoop streaming
  • mrjob
  • luigi
  • pyspark

Course Duration

5 days

Exercises

The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.

Course Material

Every participant receives comprehensive printed materials that cover the whole course content as wells as all source codes and used software.

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