The training starts by briefly describing Python’s position in the landscape of programming languages. The trainees start by installing the Anaconda manager and learn how to create a new Python environment for their project. They will also learn how to install Python packages which is necessary for the rest of the training. At this point in the lesson, everybody is guaranteed to have a working installation of Python and Anaconda, so we are ready for the second part: programming in Python.

The second part of the training introduces the Python programming language. It alternates short explanations and short exercises that cover basic data types, how to work with lists and dicts, functions, classes, objects, and list comprehensions. It ends with longer exercises in which the trainee can practice and digest what they’ve just learned.

Finally, the training goes in depth on list comprehensions; list comprehensions are useful in data science because they transform collections of data. They are suitable for an introductory class because they use simple expressions and their expressive power allows a ramp up to more advanced exercises.

After this training learners will:

• Understand what makes Python versatile for DS/DE work
• Understand what makes Python a powerful programming language
• Understand the differences between conda and pip – Know what pipenv, direnv and pyenv do
• Have a working configuration that allows for easy switching between Python versions and virtual environments
• Have installed Anaconda
• Know how to use Anaconda to create and work with isolated Python environments
• Know how to install Python packages
• Know how to launch a Jupyter Notebook server in its own conda environment
• Know how to create and work with Jupyter Notebooks
• Know how to work with markdown and code cells
• Python base data types
• Python collection types such as lists and dicts
• Functions, calling, and return values
• How to use classes, including methods
• List, dict, and generator comprehensions.

Regardless of your OS of choice, knowing how to deal with Linux through the command line is a valuable skill to have for any engineer or scientist. To look under the hood of the application your deployed, to debug that job you had running on one of those nodes that keep crashing, or to simply prepare this dataset that will take longer to download, transform and upload again, being able to utilize the power of Bash will not only often save you, it will actually speed your work up!

As with any power tool, it is of course also very easy to cut off your own foot, so join us on this journey towards getting to know Bash and unlocking its power.

The training includes theory, demos, and hands-on exercises.

After this training you will have gained knowledge about:

• Some concepts behind Linux
• Everyday Bash tools
• Tricks that will make Bash use easier
• Basic Bash scripting

As Data Scientists and Machine Learning experts spend a decent amount of time preprocessing, this topic is a necessary part in their toolkit.

In this training we specifically focus on the pandas library, which has grown into one of the main tools for data preprocessing and exploration in Python, with many capabilities.

We start off with an introduction to preprocessing, the concept of tidy data and some useful techniques such as pivoting and missing value imputation. Then, we go into the pandas library, its background, data structures, and basic features. In a demo we get to see concrete ways to handle data sets, from loading, subsetting, merging, etc. to (re)sampling, applying grouped transformations and saving results.

The training includes theory, demos, and hands-on exercises.

After this training you have gained knowledge about:

• The pandas library
• Data structures: dataframes, series
• Tidy data
• Loading and saving data
• Data exploration
• Plotting time series
• Useful transformation techniques
• Merging, selecting, sorting, sampling
• Missing value imputation
• Grouped operations
• Long/wide conversions
• Advantages and limitations of pandas