Online or onsite, instructor-led live Python training courses demonstrate through hands-on practice various aspects of the Python programming language. Some of the topics covered include the fundamentals of Python programming, advanced Python programming, Python for test automation, Python scripting and automation, and Python for Data Analysis and Big Data applications in areas such as Finance, Banking and Insurance.
NobleProg Python training courses also cover beginning and advanced courses in the use of Python libraries and frameworks for Machine Learning and Deep Learning.
Python training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Python training can be carried out locally on customer premises in Berlin or in NobleProg corporate training centers in Berlin.
Our training facilities are located at Dianastrasse 46 in Potsdam-Babelsberg. Our spacious training rooms are located directly opposite the Filmstudios Babelsberg and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Medienstadt Babelsberg railway station and the A115 motorway is also easily accessible.
Our training facilities are located at Brückenstr. 4 in Berlin. Located on the fourth floor of a well-kept office building, our premises offer enough space for successful training courses in the heart of Berlin, within walking distance of the Jannowitzbrücke station.
Directions
The NobleProg training facilities are located in the heart of Berlin's Mitte district, just one underground station from...
As I was the only participant the training could be adapted to my needs.
Kevin THIERRY
Course: Web Development with Web2Py
Joey has an infectious enthusiasm about programming. And he was very good at adapting to our needs and interests on the fly.
Randy Enkin
Course: Python Programming
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
I liked the helpful and very kind.
Natalia Machrowicz
Course: Python Programming
I liked the customized, in-house file processing and data analysis.
Glycom A/S
Course: Data Analysis in Python using Pandas and Numpy
Fact that customization was taken seriously.
jurgen linsen
Course: Python Programming
We did practical exercises (the scripts we wrote can be used in our everyday work). It made the course very interesting.
I also liked the way the trainer shared his knowledge. He did it in a very accessible way.
Malwina Sawa
Course: Python Programming
I enjoyed the felixibility to add specific topics into the course / lessons.
Marc Ammann
Course: Python Programming
I did like the exercises.
Office for National Statistics
Course: Natural Language Processing with Python
I enjoyed the that we have used our own data as examples.
Glycom A/S
Course: Data Analysis in Python using Pandas and Numpy
Very good approach to memorize/repeat the key topics. Very nice “warm-up” exercises.
Course: Python Programming
* Enjoyable exercises.
* Quickly moved into more advanced topics.
* Trainer was friendly and easy to get on with.
* Customized course for needs of team.
Matthew Lucas
Course: Python Programming
Many examples made me easy to understand.
Lingmin Cao
Course: Python Programming
I preferred the exercise and learning about the nooks and crannies of Python.
Connor Brierley-Green
Course: Python Programming
Very good approach to memorize/repeat the key topics. Very nice “warm-up” exercises.
This instructor-led, live training in Berlin (online or onsite) is aimed at data analysts and anyone who is interested to learn how to use and integrate Tableau, Python, R, and SQL for data visualization and analysis.
By the end of this training, participants will be able to:
Perform data analysis using Python, R, and SQL.
Create insights through data visualization with Tableau.
Make data-driven decisions for business operations.
This instructor-led, live training in Berlin (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.
By the end of this training, participants will be able to:
Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
Understand the features, core components, and architecture of Spark and Hadoop.
Learn how to integrate Spark, Hadoop, and Python for big data processing.
Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
Use Apache Mahout to scale machine learning algorithms.
This instructor-led, live training in Berlin (online or onsite) is aimed at data scientists and developers who wish to use and integrate SQL, Python, and Tableau to perform complex data analysis, processing, and visualization.
By the end of this training, participants will be able to:
Set up the necessary environment to perform data analysis with SQL, Python, and Tableau.
Understand the key concepts of software integration (data, servers, clients, APIs, endpoints, etc.).
Get a refresher on the fundamentals of Python and SQL.
Perform data pre-processing techniques in Python.
Learn how to connect Python and SQL for data analysis.
Create insightful data visualizations and charts with Tableau.
This instructor-led, live training in Berlin (online or onsite) is aimed at developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.
By the end of this training, participants will be able to:
Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
Understand the key concepts, features, and benefits of the FARM stack.
Learn how to build REST APIs with FastAPI.
Learn how to design interactive applications with React.
Develop, test, and deploy applications (front end and back end) using the FARM stack.
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
In this instructor-led, live training in Berlin, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting.
In this instructor-led, live training in Berlin, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
Learn how to use Spark with Python to analyze Big Data.
Work on exercises that mimic real world cases.
Use different tools and techniques for big data analysis using PySpark.
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
The training course will help the participants prepare for Web Application Development using Python Programming with Data Analytics. Such data visualization is a great tool for Top Management in decision making.
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
This instructor-led, live training in Berlin is based on the popular book, "Automate the Boring Stuff with Python", by Al Sweigart. It is aimed at beginners and covers essential Python programming concepts through practical, hands-on exercises and discussions. The focus is on learning to write code to dramatically increase office productivity.
By the end of this training, participants will know how to program in Python and apply this new skill for:
Automating tasks by writing simple Python programs.
Writing programs that can do text pattern recognition with "regular expressions".
Programmatically generating and updating Excel spreadsheets.
Parsing PDFs and Word documents.
Crawling web sites and pulling information from online sources.
Writing programs that send out email notifications.
Use Python's debugging tools to quickly resolve bugs.
Programmatically controlling the mouse and keyboard to click and type for you.
In this instructor-led, live training in Berlin, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
Implement machine learning algorithms and techniques for solving complex problems.
Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
Push Python algorithms to their maximum potential.
Use libraries and packages such as NumPy and Theano.
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
In this instructor-led, live training in Berlin, participants will learn how to use PyTest to write short, maintainable tests that are elegant, expressive and readable.
By the end of this training, participants will be able to:
Write readable and maintainable tests without the need for boilerplate code.
Use the fixture model to write small tests.
Scale tests up to complex functional testing for applications, packages, and libraries.
Understand and apply PyTest features such as hooks, assert rewriting and plug-ins.
Reduce test times by running tests in parallel and across multiple processors.
Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium.
This instructor-led, live training in Berlin (online or onsite) is aimed at data analysts who wish to build analytic applications using Python with Plotly and Dash.
By the end of this training, participants will be able to:
Set up a real-time interactive dashboard for streaming live updating data.
Build interactive dashboards using Python for data science solutions.
Secure interactive dashboards with advanced authentication methods.
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
Understand the fundamentals of the Python programming language
Download, install and maintain the best development tools for creating financial applications in Python
Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
Troubleshoot, integrate, deploy, and optimize a Python application
Audience
Developers
Analysts
Quants
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Note
This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.
The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.
This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
Apply advanced Reinforcement Learning algorithms to solve real-world problems
Build a Deep Learning Agent
Audience
Developers
Data Scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training in Berlin participants combine the power of Python with Selenium to automate the testing of a sample web application. By combining theory with practice in a live lab environment, participants will gain the knowledge and practice needed to automate their own web testing projects using Python and Selenium.
In this instructor-led, live training in Berlin (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.
By the end of this training, participants will be able to:
Install and configure packages for integrating Python and Excel.
Read, write, and manipulate Excel files using Python.
In this instructor-led, live training in Berlin, participants will learn three different approaches for accessing, analyzing and visualizing data. We start with an introduction to RDMS databases; the focus will be on accessing and querying an Oracle database using the SQL language. Then we look at strategies for accessing an RDMS database programmatically using the Python language. Finally, we look at how to visualize and present data graphically using TIBCO Spotfire.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
This instructor-led, live training in Berlin begins with a discussion of BDD and how the Behave framework can be used to carry out BDD testing for web applications. Participants are given ample opportunity to interact with the instructor and peers while implementing the concepts and tactics learned in this hands-on, practice-based lab environment.
By the end of this training, participants will have a firm understanding of BDD and Behave, as well as the necessary practice to implement these techniques and tools in real-world test scenarios.
This instructor-led, live training in Berlin (online or onsite) is aimed at persons who wish to program a visually attractive software application using Python and the Qt UI framework.
By the end of this training, participants will be able to:
Set up a development environment that includes all needed libraries, packages and frameworks.
Create a desktop or server application whose user interface functions smoothly and is visually appealing.
Implement various UI elements and effects, including widgets, charts, layers, etc. to achieve maximum effect in usability.
Implement good UI design and code organization during the design and development phase.
This instructor-led, live training in Berlin (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.
By the end of this training, participants will be able to:
Install and configure a Python development environment.
Understand the differences and similarities between Matlab and Python syntax.
Use Python to obtain insights from various datasets.
Object-Oriented Programming (OOP) is a programming paradigm based around the concept of objects. OOP is more data-focused rather than logic-focused. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to get started with Object-Oriented Programming using Python.
By the end of this training, participants will be able to:
Understand the fundamental concepts of Object-Oriented Programming
Understand the OOP syntax in Python
Write their own object-oriented program in Python
Audience
Beginners who would like to learn about Object-Oriented Programming
Developers interested in learning OOP in Python
Python programmers interested in learning OOP
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training in Berlin, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
Understand the fundamental concepts of deep learning.
Learn the applications and uses of deep learning in telecom.
Use Python, Keras, and TensorFlow to create deep learning models for telecom.
Build their own deep learning customer churn prediction model using Python.
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Course Discounts
Capture the Flag (CTF)
2023-04-03 09:30
14 hours
Blockchain for Telecom
2023-04-12 09:30
7 hours
Big Data - Data Science
2023-06-19 09:30
14 hours
Course Discounts Newsletter
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Some of our clients
is growing fast!
We are looking for a good mixture of IT and soft skills in Germany!
As a NobleProg Trainer you will be responsible for:
delivering training and consultancy Worldwide
preparing training materials
creating new courses outlines
delivering consultancy
quality management
At the moment we are focusing on the following areas:
Statistic, Forecasting, Big Data Analysis, Data Mining, Evolution Alogrithm, Natural Language Processing, Machine Learning (recommender system, neural networks .etc...)
SOA, BPM, BPMN
Hibernate/Spring, Scala, Spark, jBPM, Drools
R, Python
Mobile Development (iOS, Android)
LAMP, Drupal, Mediawiki, Symfony, MEAN, jQuery
You need to have patience and ability to explain to non-technical people
To apply, please create your trainer-profile by going to the link below:
