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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 audience

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)

Modules

1
Introduction
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Goals:

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

Contents:

  • Introduction
    • Get to know each other
    • Training process and insight into the modules
    • Introduction to the learning environment
  • Bash basics
    • Command line
    • Navigation in folder structures
    • Creating, copying and deleting folders and files
    • Filtering text files and scripts
    • Chaining commands with pipe operator
    • Editor Nano
    • Installation of programs
    • Executing Python scripts
    • Environment variables
    • Rights management
    • Bash script
  • Advanced Python basics
    • Function definition
    • Flow control
    • List and dict comprehensions
    • Clean Code & PEP 8

2
Introduction to Git and object-oriented programming
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Goals:

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

Contents:

  • Introduction to Git
    • Definition of the term: version control
    • How Git works
    • Creating and cloning projects
    • Git workflow
    • Branching & merging
    • Solving merge conflicts
  • Introduction to object-oriented programming
    • Principles of object-oriented programming
    • Classes and instances
    • Attributes
    • Methods

3
Repetition of OOP, inheritance and composition in Python, unit testing
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Goals:

  • Defining and using classes and assumptions about assertions
  • Creating and using unit tests

Contents:

  • Repetition: Introduction to object-oriented programming
  • Inheritance and composition in Python
    • Simple inheritance
    • Multiple inheritance
    • Composition
    • Inheritance hierarchy
  • Unit testing
    • Definition of terms: unit test
    • Conventions for test naming
    • Test assertions
    • Set-up methods

4
Advanced object-oriented programming with Python
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Goals:

  • Using and defining Decorator
  • Select and use external modules for typical tasks
  • Presenting results and discussing them using technical language
  • Writing clean code according to recognized standards

Contents:

  • Advanced object-oriented programming with Python
    • Operator overloading
    • Decorators
    • Special methods
  • Modules of the Python standard library
    • os
    • pickle
    • json
    • zipfile
    • collections
    • difflib
  • Project: Customizing transformers in the machine learning pipeline
  • Final test
FAQ

The demand for data experts is high. Around 4 million data experts will be needed in Europe by 2025. In Germany alone, 149,000 IT jobs are currently vacant. The demand for data and AI experts in particular continues to grow enormously.

But a decision for a data career is so much more than just a safe decision for the future! As a data expert, you deal with powerful, socially relevant topics, are a tech professional, and are communicative and creative at the same time. The profession is varied, can be combined with most other professions and offers an attractive salary. And most importantly, with us it can be learned unerringly!

Yes, after successful completion of the training, you will receive a certificate of completion from us that you can show in your job applications. Data Analysts and Data Scientists are desperately sought after in many business sectors. Even without relevant work experience, your chances of finding an entry-level job are good. In addition, there are analysts in almost every industry who have different job titles, but the skills you need are the same as those of a data analyst or data scientist.
Yes, our online training courses are designed to offer you the greatest possible flexibility. In general, we recommend that you plan six to eight hours per week for learning. When you want to schedule this time is up to you and is not prescribed by us. In our career paths, the Data Analyst and Data Scientist course, we offer live webinars where you can ask our mentors questions, but you don't have to attend if it doesn't fit into your schedule.
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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
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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
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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
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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
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