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Advanced Python Programming for Data Scientist

Training for object-oriented programming (OOP) with Python
Certificate of attendance
(Cross) boarding
Full time/part time
German, English
Free of charge with education voucher
Course description

The goal of this course is to learn object-oriented programming (OOP) with Python. In addition, you will learn the basics in Bash and Git to use and create code collaboratively in a team.

The automation of digital processes and the analysis of large amounts of data often require customized solutions for use in the company. For this reason, Data Scientists should be able to generate production-ready code collaboratively in a team.

In this training you will learn
Advanced Python Basics
OOP Basics
Advanced OOP
  • Create and customize modules, classes and objects
    in the Python framework
  • Independent processing and presentation of software projects
  • Use of Git and Bash for collaborative work on software projects

Target group

The advanced training is suitable for you and your career aspirations if you have a degree, ideally in the fields of mathematics, computer science, natural sciences, technology, business administration, (business) information technology or have a comparable qualification or previous experience.

Requirements for participation

Solid knowledge of Python basics (use of basic data types and flow control concepts)




  • Using the command line to navigate folder structures
  • Viewing and searching text documents in the command line
  • Execution of scripts and installation of programs
  • Writing clean code according to recognized standards


  • Introduction
    • Getting to know each other
    • Training procedure and insight into the building blocks
    • Introduction to the learning environment
  • Bash Basics
    • Command line
    • Navigation in folder structures
    • Create, copy and delete folders and files
    • Filtering text files and scripts
    • Concatenating commands with pipe operator
    • Editor Nano
    • Programs installation
    • Execute Python scripts
    • Environment variables
    • Rights management
    • Bash Script
  • Advanced Python Basics
    • Function definition
    • Flow Control
    • List and Dict Comprehensions
    • Clean Code & PEP 8

Introduction to Git and object-oriented programming


  • Creating and updating projects with Git
  • Collaborative use of Git


  • Introduction to Git
    • Clarification of terms: version control
    • How Git works
    • Create and clone projects
    • Git workflow
    • Branching & Merging
    • Resolve merge conflicts
  • Introduction to object-oriented programming
    • Principles of object-oriented programming
    • Classes and instances
    • Attributes
    • Methods

Repetition of OOP, inheritance and composition in Python, unit testing


  • Defining and using classes and assumptions about assertions
  • Create and use unit tests


  • Repetition: Introduction to object-oriented programming
  • Inheritance and composition in Python
    • Simple inheritance
    • Multiple inheritance
    • Composition
    • Inheritance hierarchy
  • Unit Testing
    • Clarification of terms: Unit Test
    • Test naming conventions
    • Test Assertions
    • Set-up methods

Advanced object-oriented programming with Python


  • Use and define Decorator
  • Selecting and using external modules for typical tasks
  • Presentation of results and discussion under technical language
  • Writing clean code according to recognized standards


  • Advanced object-oriented programming with Python
    • Operator Overloading
    • Decorators
    • Special methods
  • Modules of the Python Standard Library
    • os
    • pickle
    • json
    • zipfile
    • collections
    • difflib
  • Project: Adapt Transformer in the Machine Learning Pipeline
  • Final exam
The StackFuel Data Lab offers me real added value. Here you can feel the practical relevance particularly well. The tasks were always clearly described and presented. So I always knew what I had to do. The training itself was a great experience!
Alexander Gross
Data Analyst at AIC Portaltechnik
The greatest added value for me is the practical relevance. Thanks to StackFuel, I can quickly implement what I've learned and adapt it for myself. That is the real learning success behind the online trainings.
Lutz Schneider
Strategic IT Buyer at Axel Springer SE
The content of StackFuel's online training was very practical. There were many good examples and projects. I found that very interesting and instructive. Since the training, my everyday professional life has changed significantly: I am now a data analytics specialist in my department.
Jaroslaw Wojciech Sulak
Specialist for data analysis at IAV GmbH
The user-friendly and flexible Python programming training has completely changed my view of complex data structures. Thanks to the sustainable and well thought-out learning concept as well as the seamless application of the learning content in the development environment, I can now implement the newly learned knowledge in my everyday job in test automation in greater depth and process data more easily and efficiently since then.
Jenny Lindenau
Technical Manager Test Management at Bank Deutsches Kraftfahrzeuggewerbe GmbH

Let's start with a consultation.

Our consultants will be happy to help you and answer all your questions. Free of charge and without obligation. We look forward to meeting you.
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