Python Training Courses

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 Python 2.7.x or 3.x, with practical exercises making use of the full power of both versions of the language. 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.

This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives.

The course is designed and aimed for people without computer science background who want to learn programming.

This course is suited for:

• Researchers dealing with biological data.
• Scientists who would like to learn how to automate everyday tasks and analyse data.
• Managers who want to learn how programming improves workflows and conducting projects.

By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.

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.

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

Audience

This course is directed at developers and engineers seeking to incorporate Django in their projects

web2py is a python based free open source full-stack framework for rapid development of fast, scalable, secure and portable database-driven web-based applications.

Audience

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.

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala 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.

Selenium is an open source library for automating web application testing across multiple browsers. Selenium interacts with a browser as people do: by clicking links, filling out forms and validating text. It is the most popular tool for web application test automation. Selenium is built on the WebDriver framework and has excellent bindings for numerous scripting languages, including Python.

In this training 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.

Audience

• Testers and Developers

Format of the course

• Part lecture, part discussion, heavy hands-on practice

In this instructor-led training, 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.

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Notes

• If you wish to add, remove or customize any section or topic within this course, please contact us to arrange.

This instructor-led training 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.

Audience

• Non-programmers wishing to learn programming with Python
• Professionals and company teams wishing to optimize their office productivity
• Managers wishing to automate tedious processes and workflows

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Kivy is an open-source cross-platform graphical user interface library written in Python, which allows multi-touch application development for a wide selection of devices.

In this instructor-led, live training participants will learn how to install and deploy Kivy on different platforms, customize and manipulate widgets, schedule, trigger and respond to events, modify graphics with multi-touching, resize the screen, package apps for Android, and more.

By the end of this training, participants will be able to

• Relate the Python code and the Kivy language
• Have a solid understanding of how Kivy works and makes use of its most important elements such as, widgets, events, properties, graphics, etc.
• Seamlessly develop and deploy Android apps based on different business and design requirements

Audience

• Programmers or developers with Python knowledge who want to develop multi-touch Android apps using the Kivy framework
• Android developers with Python knowledge

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

APIs (Application Programming Interface) allow for your application to connect with other applications.

In this instructor-led, live training, participants will learn how to write high-quality APIs as they build and secure a backend API server.

By the end of this training, participants will be able to:

• Choose from a number of frameworks for building APIs
• Understand and model the APIs published by companies such as Google and Facebook
• Create and publish their own Restful APIs for public consumption
• Secure their APIs through token-based authentication

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To customize this course for other languages, such as PHP, Javascript, etc., please contact us to arrange

In this instructor-led, live training, 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

Audience

• Developers
• Analysts
• 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 how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.

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

Natural language generation (NLG) refers to the production of natural language text or speech by a computer.

In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.

By the end of this training, participants will be able to:

• Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
• Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
• Understand the NLG pipeline and apply the right techniques at each stage
• Understand the architecture of a Natural Language Generation (NLG) system
• Implement the most suitable algorithms and models for analysis and ordering
• Pull data from publicly available data sources as well as curated databases to use as material for generated text
• Replace manual and laborious writing processes with computer-generated, automated content creation

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Unit testing is a testing approach that tests individual units of source code by modifying their properties or triggering an event to confirm whether the outcome is as expected. PyTest is a full-featured, API-independent, flexible, and extensible testing framework with an advanced, full-bodied fixture model.

In this instructor-led, live training, 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
• Use Python to test non-Python applications

Audience

• Software testers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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.

In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.

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

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

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

By the end of this training, participants will be able to:

• Understand the fundamental concepts in machine learning
• Learn the applications and uses of machine learning in finance
• Develop their own algorithmic trading strategy using machine learning with Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.

In this instructor-led, live training, 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 circumstances
• Use different tools and techniques for big data analysis using PySpark

Audience

• Developers
• IT Professionals
• Data Scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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 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

Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.

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

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking 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 banking
• Use Python, Keras, and TensorFlow to create deep learning models for banking
• Build their own deep learning credit risk model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction 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 finance
• Use Python, Keras, and TensorFlow to create deep learning models for finance
• Build their own deep learning stock price prediction model using Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices.

In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python.

By the end of this training, participants will be able to:

• Understand the basics of building microservices
• Learn how to use Python to build microservices
• Learn how to use Docker to deploy Python based microservices

Audience

• Developers
• Programmers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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.