Data Scientist with Python
Machine learning algorithms with Python
The profession of data scientist is one of the most sought-after in the 21st century. This part time online course from StackFuel will teach you how to use supervised and unsupervised machine learning algorithms, different data visualization methods and data storytelling so that you are able to take on the role of data scientist after you finish the course. You will develop the skills you need to work as a data scientist. You can then apply the knowledge you gained in your department and implement machine learning algorithms by yourself. During the course, you will work in our browser-based, interactive learning environment, the Data Lab. This is a full programming environment where you can execute code you write yourself.
The Data Scientist course is suitable for anyone who wants to analyze data and make predictions based on this data in order to make data-driven decisions. You should be also be interested in machine learning. A good knowledge of Python and common modules (pandas, matplotlib) is required to participate in the Data Scientist Course.
The objective of the course is to understand and use performance metrics and assumptions from supervised and unsupervised learning models with sklearn. You will also learn
data storytelling principles as well as best practices for informative visualization design with bokeh algorithms from supervised and unsupervised learning, such as decision trees and random forests.
Conditions for participation
Solid Python skills and proficiency in common modules (pandas, matplotlib) are required for success in the Data Scientist Course.
Next start dates
Contact and consultation
Tel: +49 (0) 30 6800 9503
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.
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.
Data Wrangling: Gaining New Insights
Refresh skills in data processing with pandas
Gain new insights from structured, semistructured and unstructured data
Use relational and NoSQL databases
Machine Learning: Algorithms at Work
Create forecasts using Scikit-learn
Differentiate between supervised and unsupervised learning and apply each type competently
Implement typical algorithms, such as Random Forest, Bayes Classification, Clustering
Inferential Statistics for Data Scientists
Run data queries and resolve them with inferential statistics
Perform A/B testing with the help of classical and Bayesian statistics
Using Big Data the Smart Way
Use data within a Hadoop infrastructure
Accelerate data processing with the help of Spark
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.
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.
The Data Lab really offers added value, you notice instantly how the practical exercises are relevant to your work. The tasks were always clearly described and presented. So I always knew what to do. The course itself was a great experience!
The greatest added value for me was the courses practical relevance. Thanks to StackFuel, I can quickly put what I have learned into practice and adapt it for myself. This is the real learning success behind the online course.
The online StackFuel course content was very hands on. There were a great deal of good examples and projects. I found it very interesting and instructive. My professional life has changed significantly since completing the course: Im now a data analysis specialist in my department.
The user-friendly and flexible Python Programming course has entirely changed my view on complex data structures. Thanks to the sustainable and well-designed learning approach, and the seamless use of learning content in the coding environment, I can now apply my new skills in my daily work of testing automation and process data more easily and efficiently.
Our Portfolio of Training and Seminars
A seminar that covers the basics of data analytics to gain an understanding of data-driven work and decision-making and to synchronise team work.
Entry into the topics of data strategy, data management and data thinking to identify the entrepreneurial potential of data.
Many office work processes can be automated as part of the digitalization process, reducing employees' workloads and increasing productivity at the same time.