Many office work processes can be automated as part of the digitalization process, reducing employees’ workloads and increasing productivity at the same time. Basic programming knowledge can often be enough to automate processes in your day-to-day work. StackFuel has developed a training course in which the basics of programming with Python are taught, thus providing participants with tools to automate their own work processes.
Target group
The course is suitable for anyone who's work is predominantly digital, but who did not study IT and a related subject. You should have an interest in technology.
Learning object
The goal of this hands-on course is to learn the basics of the free programming language Python and commonly-used libraries for importing, processing and storing local data.
Conditions for participation
No previous programming knowledge is required for the Python Programming course. You will develop these skills in the course. However, you should be a frequent computer user, and be familiar with common software.
Next start dates
Standard
Contact and consultation
E-Mail:
Tel: +49 (0) 30 6800 9503
Training formats


Hands-on learning environment
Participants learn using current technologies and the latest Python libraries.
Advanced technology stack
Our trainings make use of real data sets as well as industry business cases to create hands-on Learning scenarios.
Browser-based
Participants have access to all the computing power they need to complete the course.
Innovative Data Lab
The course takes place in your browser, you don’t need to install any special software.
Modules
Module 1
Data types
Represent data as numbers or text in Python
Save data in variables and lists
Create, read and change lists
Read error messages and use them productively
Module 2
Data manipulation
Use standard Python functions like len() or sum()
Methods of text and list data types, e.g. str.format () or list.append()
Represent truth values as bool and define them with comparison operators
Two-hour mini-project with car data to consolidate the learning content
Module 3
Flow control
Write for loops and link them to conditions
Write functions yourself and check how they work
Import python modules
Read data in as simple text and export it again
four-hour final project for workplace automationOne-hour mini project with telephone data to consolidate the learning content
Module 4
Deepening of the concepts and final project
Sets
List comprehension
Complex data structures
Numpy arrays
Four-hour final project for workplace automation
Starting dates
Curriculum
Request the course curriculum for more detail about the course material!


Flexible course formats
StackFuel has developed various training formats to meet the diverse requirements of a modern working environment. Our regular part-time courses are designed specifically with company employees in mind, and combine professional development and in-depth skill development with day-to-day work. Our FastTrack courses are tailored specifically toward our participants and customers who would like to take part in our courses more quickly on a part-time or full-time basis. The time spent on our courses each week as part of our FastTrack formats is therefore more concentrated, and you will ready for the job role of Data Analyst or Data Scientist more quickly.
Certification
StackFuel's specialist courses follow a practical approach that trains employees for the job roles of the future and gives them the necessary skills to create real added value from data. This is also reflected in the final project that the participants carry out and which our mentoring team, made up of educational data scientists, analyzes and evaluates. Participants receive a certificate based on an independent final project at the end of the course, which they can use in various social networks as well as privately, as proof of their newly acquired knowledge and skills. Our specialist Data Analyst (73598) Data Scientist (73597) courses are ZFU* tested and certified. *The ZFU checks whether a course meets the requirements of the German Distance Learning Protection Act (Fernunterrichtschutzgesetz) and whether courses are suitable for distance learning in terms of their subject matter and didactics.

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