Local, 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 "onsite live training" or "remote live training". Ireland onsite live Python trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Testimonials
practice tasks
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
Course: Python and Spark for Big Data (PySpark)
Recap of previous day, trainer very knowledgable in answering questions
Mateusz Jaros - GE Medical Systems Polska Sp. Zoo
Course: Python Programming
It gave me a broad overview of the possibilites
GE Medical Systems Polska Sp. Zoo
Course: Python Programming
really kind, good approach to trainees, helpful
GE Medical Systems Polska Sp. Zoo
Course: Python Programming
I like pace of the training. It was good and we were able to cover many aspects of programming language. Trainer was able to show many applications of Python in very informative way. Trainer sent to us many scripts and micro-programs for furher reference which is very useful. I like, that we started training with some technical remarks and setting up virtual environment.
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Python Programming
I thought John was very knowledgeable and able to diseminate information in a very understandable way.
Crux Product Design
Course: Python Programming Fundamentals
John was a very friendly and knowledgeable trainer and was keen to adapt the course to our requests.
Crux Product Design
Course: Python Programming Fundamentals
Gaining a better understanding of object oriented programming as this is a key difference to programming in Matlab (which I am much more familiar with). The training should hopefully be very useful!
Crux Product Design
Course: Python Programming Fundamentals
knew his subject well
Albert JACOB - Proximus
Course: Python Programming
The exercises combined with the experienced help of the trainer
Proximus
Course: Python Programming
The fact that we could practice a lot. Even though for me being a newbe the pace was to fast and explanation too few. However, probably due to the mixed knowkedge level of the students attending the class.
Proximus
Course: Python Programming
Trainer obviously had a great holistic understanding of programming.
Crux Product Design
Course: Python Programming Fundamentals
the last day. generation part
Accenture Inc
Course: Python for Natural Language Generation
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
I enjoyed the sentinal analysis/ data science aspect of the course.
Jake Hamilton - Scottish Government
Course: Python Programming
pace and explanations
Centric IT Solutions Lithuania
Course: Advanced Python
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The trainer was great! If he would have more time I think we could have learned a lot more.
Zarim Jei Serrano - Cloudstaff Philippines, Inc.
Course: Python Programming Fundamentals
Exercises
Vince Christian Henson - Cloudstaff Philippines, Inc.
Course: Python Programming Fundamentals
It makes the trick. A good introduction (and more) to python.
jean-christophe GOLDBERG - Proximus
Course: Python Programming
* Organization * Trainer's expertise with the subject
ENGIE- 101 Arch Street
Course: Python and Spark for Big Data (PySpark)
Teaching style and ability of the trainer to overcome unforeseen obstacles and adopt to circumstances. Broad knowledge and experience of the trainer
ASML
Course: Python for Matlab Users
Overall good intro to Python. The format of using Jupyter notebook and live examples on the projector was good for following along with the exercises.
ASML
Course: Python for Matlab Users
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
The flexibilty and clear information
WAFEYA AlMadhoob - Tatweer Petroleum
Course: Advanced Python
The content.
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
Pictures
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
Willingness of Krzysztof to answer all questions.
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
Live coding, helping with code and different bugs, explanation with examples
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
Good interaction with audience, a lot of questions
Kinga Kalinowska - HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
The course has good proportion between theory and practice, knowledgeable trainer, a lot of training materials and user in practice.
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
It covered many algorithms of ML and is useful to provide a track
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
It was cery consistent
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
All the exercises have been discussed
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
Overall I liked course a lot. Good discussions. Sometimes to overall, but I understand that we were short of time.
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
It covered systematically all the main topics of machine earning: both the theory and implementation. It gave me great background for further work. It also answered most of the questions about machine learning that I had up to this point.
HSBC Service Delivery (Polska) Sp. z o.o
Course: Python Programming
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
Trainer develops training based on participant's pace
Farris Chua
Course: Python Programming Fundamentals
Trainer develops training based on participant's pace
Farris Chua
Course: Data Analysis in Python using Pandas and Numpy
The notebooks were well-prepared and the examples were on point.
Course: Python Programming Fundamentals
The notebooks and examples were on point.
Course: Data Analysis in Python using Pandas and Numpy
The hands on
Course: Python Programming Fundamentals
The explanation provided is clear.
Course: Data Analysis in Python using Pandas and Numpy
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
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
Flow.
Embassy of Canada
Course: Python: Automate the Boring Stuff
all
Albert JACOB - Proximus
Course: Python Programming
Flask, dataclasses
Atos Global Delivery Center Polska Sp. Z o.o. Sp. K.,
Course: Advanced Python
The lessons were taught in a Jupyter notebook. The topics were structured with a logical sequence and naturally helped develop the session from the easier parts to the more complex. I'm already an advanced user of Python with background in Machine Learning, so found the course easier to follow than, possibly, some of my classmates that took the training course. I appreciate that some of the most elementary concepts were skipped and that he focused on the most substantial matters.
Angela DeLaMora - ADT, LLC
Course: Python and Spark for Big Data (PySpark)
NA
DBS
Course: Python and Spark for Big Data (PySpark)
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The notebooks were well-prepared and the examples were on point.
Course: Python Programming Fundamentals
The notebooks and examples were on point.
Course: Data Analysis in Python using Pandas and Numpy
The hands on
Course: Python Programming Fundamentals
The explanation provided is clear.
Course: Data Analysis in Python using Pandas and Numpy
Python Course Outlines in Ireland
The course is delivered with examples and exercises using Python
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
- Test and debug the application.
By the end of this training, participants will be able to:
- Automate repetitive tasks with SikuliX scripts.
- Identify text in content with text recognition.
- Find and control GUI components with image recognition.
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.
- Use Python to test non-Python applications.
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.
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, participants will learn how to use Python for quantitative finance.
By the end of this training, participants will be able to:
- Understand the fundamentals of Python programming
- Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics
- Implement financial algorithms using performance Python
Audience
- Developers
- Quantitative analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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.
- Call Python functions from Excel.
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.
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.
- Convert existing Matlab applications to Python.
- Integrate Matlab and Python applications.
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.
By the end of this training, participants will be able to:
- Install and configure Python and all relevant packages.
- Retrieve and parse data stored across many different websites.
- Understand how websites work and how their HTML is structured.
- Construct spiders to crawl the web at scale.
- Use Selenium to crawl AJAX-driven web pages.
By the end of this training, participants will be able to:
- Employ algorithms to buy and sell securities at specialized increments rapidly.
- Reduce costs associated with trade using algorithmic trading.
- Automatically monitor stock prices and place trades.
After Completing the course students will be able to demonstrate knowledge and understanding of Python Security Principles.
By the end of this training, participants will be able to:
- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
By the end of this training, participants will be able to:
- Create a self documenting REST API.
- Deploy REST APIs onto a cloud based server.
- Implement APIs for application authentication.
- Build a reusable backend for future Python projects.
By the end of this training, participants will be able to:
- Implement a REST API to allow a Flask web application to read and write to a database in the backend.
- Develop advanced authentication features like refresh tokens.
- Build a reusable backend for future Python projects.
- Simplify storage of data with SQLAlchemy.
- Deploy REST APIs onto a cloud based server.
By the end of this training, participants will be able to:
- Install and configure spaCy.
- Understand spaCy's approach to Natural Language Processing (NLP).
- Extract patterns and obtain business insights from large-scale data sources.
- Integrate the spaCy library with existing web and legacy applications.
- Deploy spaCy to live production environments to predict human behavior.
- Use spaCy to pre-process text for Deep Learning
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about spaCy, please visit: https://spacy.io/
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 circumstances.
- Use different tools and techniques for big data analysis using PySpark.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API.
By the end of this training, participants will be able to:
- Integrate Tableau and Python using TabPy API
- Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Use geography managers to lay out the GUI.
- Organize widgets inside of frames.
- Build a GUI application with Python Tkinter.
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