Advanced Python - 1 Day Training Course

This intensive, hands-on course covers advanced Python techniques, engineering best practices, and commonly used design patterns to build maintainable, testable, and high-performance Python applications. It emphasises modern tooling, typing, concurrency models, architecture patterns, and deployment-ready workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level Python developers who wish to adopt professional practices and patterns for production-grade Python systems.

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

• Apply Python typing, dataclasses, and type-checking to increase code reliability.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Build well-tested code with pytest, property-based testing, and CI pipelines.
• Profile, optimise, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Course Format

• Interactive lectures and short demos.
• Hands-on labs and coding exercises each day.
• Capstone mini-project integrating patterns, testing, and deployment.

Course Customisation Options

• To request a customised training or focus area (data, web, or infra), please contact us to arrange.

This course teaches practical engineering techniques to design, build, test, and deploy agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.

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

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to extend agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

This is a five-day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered using Python, with practical examples and hands-on exercises.

Artificial Intelligence with Python involves building intelligent systems by leveraging Python’s rich ecosystem of AI and machine learning libraries.

This instructor-led, live training (available online or on-site) is designed for intermediate-level Python programmers who want to design, implement, and deploy AI solutions using Python.

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

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimise for accuracy and efficiency.

Course Format

• Interactive lectures and discussions.
• Abundant exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or onsite) is aimed at business analysts who wish to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to buy and sell securities rapidly in specialised increments.
• Reduce trading-related costs through the application of algorithmic trading.
• Automatically monitor stock prices and execute trades.

This is a four-day course introducing AI and its application using the Python programming language. There is an option to undertake an additional day of an AI project upon completion of this course.

This instructor-led, live training in New Zealand (online or onsite) is designed for intermediate-level Python developers who wish to enhance their Python development skills using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for Python development.
• Understand the AWS Cloud9 IDE interface and its features.
• Write, debug, and deploy Python applications within AWS Cloud9.
• Collaborate with other developers using the AWS Cloud9 platform.
• Integrate AWS Cloud9 with other AWS services for advanced deployment scenarios.

Chatbots are computer programmes that automatically simulate human responses via chat interfaces. Chatbots help organisations maximise operational efficiency by providing simpler and faster options for user interactions.

In this instructor-led, live training, participants will learn how to build chatbots in Python.

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

• Understand the fundamentals of building chatbots
• Build, test, deploy, and troubleshoot various chatbots using Python

Audience

• Developers

Format of the course

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

Note

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.

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

• Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
• Create, compile and launch custom CUDA kernels.
• Manage GPU memory.
• Convert a CPU-based application into a GPU-accelerated application.

Course Overview

This practical training is tailored for professionals with a data engineering background who wish to develop hands-on expertise in artificial intelligence, Python, and large language models. The curriculum emphasises real-world applications, encompassing model utilisation, prompt engineering, and the creation of AI-driven solutions. Participants will navigate through progressive exercises that transition from foundational concepts to the development of deployable AI workflows.

Training Format

• In-person classroom-based training

• Instructor-led sessions featuring guided practice

• Interactive discussions and real-world case studies

• Daily hands-on exercises

Course Objectives

• Comprehend core AI and machine learning concepts pertinent to contemporary applications

• Enhance Python skills for AI development and data workflows

• Gain an understanding of how large language models function and how to utilise them effectively

• Design and optimise prompts to ensure reliable outputs

• Construct end-to-end AI solutions using APIs and frameworks

• Integrate AI capabilities into data engineering pipelines

This instructor-led, live training in New Zealand (delivered online or on-site) is aimed at data scientists and software engineers who wish to leverage Dask within the Python ecosystem to build, scale, and analyse large datasets.

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

• Set up the environment to begin building big data processing solutions with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (NumPy, SciPy, and Pandas) using Dask.
• Optimise the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level Python developers and data analysts seeking to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment including Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyse time series data.
• Visualise data using Matplotlib and other visualisation libraries.
• Debug and optimise their data analysis code.

This instructor-led, live training (available online or on-site) is designed for developers eager to harness the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Establish the essential development environment integrating FastAPI, React, and MongoDB.
• Grasp the core concepts, features, and advantages of the FARM stack.
• Learn how to construct REST APIs using FastAPI.
• Master the art of designing interactive applications with React.
• Develop, test, and deploy both front-end and back-end applications using the FARM stack.

This instructor-led, live training in New Zealand (online or on-site) is designed for developers who wish to leverage FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly.

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

• Set up the necessary development environment to build APIs using Python and FastAPI.
• Create APIs more quickly and easily using the FastAPI library.
• Learn how to define data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication mechanisms in APIs using FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in New Zealand (available online or on-site) is tailored for data scientists and developers who wish to utilise Modin to build and implement parallel computations with Pandas, enabling faster data analysis.

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

• Set up the necessary environment to begin developing scalable Pandas workflows with Modin.
• Understand the features, architecture, and advantages of Modin.
• Identify the key differences between Modin, Dask, and Ray.
• Execute Pandas operations more efficiently using Modin.
• Implement the full Pandas API and its associated functions.