
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 Indonesia or in NobleProg corporate training centers in Indonesia.
NobleProg -- Your Local Training Provider
Testimonials
possibility to check code, to ask and to verify what has been done, vast knowledge of trainer
Adrian Zieliński, Mid Ocean Logistics Poland Sp. z.o.o
Course: Python Programming - 4 days
Opisy funkcji, przedstawione przykłady, alternatywne wersje. Przesył przydatnych do pracy w Pythonie linków.
Agnieszka Jurołajć, VOLKSWAGEN POZNAŃ SP. Z O.O.
Course: Python Programming - 4 days
The notes given
Bertrand Chen, MINDEF
Course: Python Programming - 4 days
The virtual machines worked very well and make playing around with the code very easy. I also particularly liked having copies of all the examples being put together by the trainer to following with so I could see the end result in advance. Made it easier for me to ask more specific questions.
Stefan Kotze - Samantha Campbell, ACC
Course: Python Programming - 4 days
Trainer covered more in depth in every topic within the time given and also gave us questions to do and explained it whenever we had queries.
Praveent Thamil Mani - Bertrand Chen, MINDEF
Course: Python Programming - 4 days
Well-paced, sufficient break time so us to absorb the content
Bertrand Chen, MINDEF
Course: Python Programming - 4 days
It generated good discussion from the group
Samantha Campbell, ACC
Course: Python Programming - 4 days
Days 2 and 3. There was an absurd amount of content but Abhi handled it well, so I got real value there.
Michael Clews - Samantha Campbell, ACC
Course: Python Programming - 4 days
The communication with the Mr. Khobeib
Aref AlHosani - Ali Aljneibi, beamtrail
Course: Python Programming - 4 days
trainer was very helpful, patient, and friendly
Ali Aljneibi, beamtrail
Course: Python Programming - 4 days
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Course: Machine Learning with Python – 2 Days
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Course: Machine Learning with Python – 2 Days
I did like the exercises
Office for National Statistics
Course: Natural Language Processing with Python
The trainer gave a clear and systematic teaching. He usually gave the reasoning and fundamental knowledge behind the commands. He also gave us time to do the exercises and practice.
Felicia Rezanda - Ong Lee Chiau, HP Singapore (Private) Ltd.
Course: Advanced Python - 4 Days
The trainer will illustrate the theory behind certain data structure behavior using Jamboard.
Ong Lee Chiau, HP Singapore (Private) Ltd.
Course: Advanced Python - 4 Days
Learned lots of things of course.
Jonathan Rico, Nordic Semiconductor ASA
Course: Advanced Python - 4 Days
The first 2 days were very informative. it gets messy when you get into frameworks because every projects has its own goals and requirements and sometimes the 'popular' framework isn't suitable.
Raphael Treccani-Chinelli - Jonathan Rico, Nordic Semiconductor ASA
Course: Advanced Python - 4 Days
How flexible Gunnar was with what he taught. He seemed well prepared and of god knowledge in the topic.
Jonathan Rico, Nordic Semiconductor ASA
Course: Advanced Python - 4 Days
Very good overview about python on a lot of area of usage.
János Dóra - Robert Bosch Kft.
Course: Advanced Python
The early examples was to build up for the later coming complex topics like machine learning or data analyzing.
Robert Bosch Kft.
Course: Advanced Python
The prepared Jupiter Notebook examples were really good. Plenty of explanations for later, offline use, and we didn't have to spend half of the training copying the examples.
Csongor Miklos - Robert Bosch Kft.
Course: Advanced Python
The trainer was friendly and had a very good way of explaining the topics to us
Thames Water Utilities Ltd
Course: Python: Automate the Boring Stuff
Lots of things; good explanations of the underlying concepts and how they work, good practical exercises to demonstrate the concepts etc
Thames Water Utilities Ltd
Course: Python: Automate the Boring Stuff
1:1 very intensive but learnt a lot.
Karen Dyke - BT
Course: Python: Automate the Boring Stuff
the hands-on exercise and the instructor seem very knowledgeable.
Ashok Nair, City of Calgary
Course: Machine Learning with Python – 4 Days
The trainer was a practitioner with a lot of experience and had a very good knowledge of the material.
Witold Iwaniec - Ashok Nair, City of Calgary
Course: Machine Learning with Python – 4 Days
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Course: Machine Learning with Python – 4 Days
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Course: Machine Learning with Python – 4 Days
The trainers depth of knowledge & explanations, he could explain difficult concepts quite intuitively!
KnowledgePool
Course: Python for Advanced Machine Learning
The trainer was very knowledgeable, he was able to answer every question, was able to bug fix coding issues, and could tie a lot of the topics into his real life experiences. The trainer's knowledge applied to a different approach to coding (see above) would have been perfect.
Premier Partnership
Course: Python for Advanced Machine Learning
Seeing the practical examples
Premier Partnership
Course: Python for Advanced Machine Learning
Trainer knowledge and experience on subject matter is very deep
Premier Partnership
Course: Python for Advanced Machine Learning
The trainers knowledge of the topics he was teaching.
Premier Partnership
Course: Python for Advanced Machine Learning
Having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine Learning
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.
Accenture Inc
Course: Python for Natural Language Generation
the last day. generation part
Accenture Inc
Course: Python for Natural Language Generation
I loved summaries
Martyna - Robert Kwiatkowski, Orange Szkolenia Sp. z o.o.
Course: Unit Testing with Python
Materials Trainer
Zakar Abid - Gehan Hisham Elraey, TII
Course: Unit Testing with Python
Did hands on exercise. Walked through the code. Explained everything very well
Steve Thomas - Gehan Hisham Elraey, TII
Course: Unit Testing with Python
monkeypatch, fixtures, proper writing tests
Alicja Regulska, ArcelorMittal Business Center of Excellence Poland Sp. z o.o. Sp. k.
Course: Unit Testing with Python
No rushing things, though a bit too slow sometimes. Checking excercises with group and comparing solutions
Piotr - Alicja Regulska, ArcelorMittal Business Center of Excellence Poland Sp. z o.o. Sp. k.
Course: Unit Testing with Python
możliwość dopytania w każdej chwili
Alicja Regulska, ArcelorMittal Business Center of Excellence Poland Sp. z o.o. Sp. k.
Course: Unit Testing with Python
good is a previous day material repeat
Alicja Regulska, ArcelorMittal Business Center of Excellence Poland Sp. z o.o. Sp. k.
Course: Unit Testing with Python
The trainer is interactive with the audience. He is able to reply the questions easily and gives the accurate examples and illustrations in real life. The theoritical and practical rythm are smooth. The exercices give the user a better experience to think and structure his/ her way of testing and developping. Numpy and Pandas may be useful in order to better exploit data, such as performance results, statistics, image treatement, calculating the correlation for biological set images. The Django framework would be helpful for building web API. All this knowledge is an asset. However, I am not sure this would be fruitful for other contexts, since we need unit and Integration tests of Java apps in Python.
Soumaya ELALOUANI - Stéphanie Vander Straeten, Telemis
Course: Unit Testing with Python
I liked the "bottom up" approach (i.e. "let's say we want to do this and then discover how we can improve it using design patterns") instead of "top down" (i.e. this is pattern X and this is how you can use it)
Ewa Dusza, Red Embedded Consulting Sp. z o.o.
Course: Unit Testing with Python
That we started from a simple implementation, adding functionalities/features until we need to update the design to keep having a maintainable software. Definitely real life job problems I see value in this. Also liked the use of VideoUpload project as we can relate to this working in Consult Red. Very open to questions, driving the class in a way asked by the audience.
Thibault Marechal - Ewa Dusza, Red Embedded Consulting Sp. z o.o.
Course: Unit Testing with Python
A lot of knowledge servred in easy to take way.
Paweł Piszcz - Ewa Dusza, Red Embedded Consulting Sp. z o.o.
Course: Unit Testing with Python
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Provided a very broad overview on object-oriented programming.
Course: Learn Object-Oriented Programming with Python
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
Provided a very broad overview on object-oriented programming.
Course: Learn Object-Oriented Programming with Python
Python Course Outlines in Indonesia
- Perform data analysis using Python, R, and SQL.
- Create insights through data visualization with Tableau.
- Make data-driven decisions for business operations.
- Use Python programming for defensive cybersecurity.
- Understand and use Python for ethical offensive techniques and digital forensics tasks.
- Recognize legal and ethical considerations surrounding offensive cybersecurity and vulnerability disclosure.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Use Python to test non-Python applications.
- 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.
- 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
- Developers
- Analysts
- Quants
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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.
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
- 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.
- Test and debug the application.
- 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.
- Convert existing Matlab applications to Python.
- Integrate Matlab and Python applications.
- Understand the fundamental concepts of Object-Oriented Programming
- Understand the OOP syntax in Python
- Write their own object-oriented program in Python
- Beginners who would like to learn about Object-Oriented Programming
- Developers interested in learning OOP in Python
- Python programmers interested in learning OOP
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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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