Data Science Training in Adelaide

This instructor-led, live training in Adelaide (online or onsite) is designed for professional beginners who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without constructing models from the ground up.

Upon completion of this training, participants will be able to:

• Comprehend the concept and advantages of pre-trained models.
• Explore various pre-trained model architectures and their specific use cases.
• Fine-tune a pre-trained model for particular tasks.
• Implement pre-trained models in straightforward machine learning projects.

This instructor-led, live training in Adelaide (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

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

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led, live training in Adelaide (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

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

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in Adelaide (online or onsite) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

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

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

Python has surged in popularity within the financial sector, adopted by major investment banks and hedge funds to build diverse financial applications, from core trading systems to risk management tools.

Through this instructor-led live training, participants will learn to leverage Python to develop practical applications that address specific finance-related challenges.

Upon completing this course, participants will be able to:

• Grasp the fundamental concepts of the Python programming language
• Download, install, and manage the optimal development tools for building financial applications in Python
• Choose and employ the most appropriate Python packages and programming techniques to organise, visualise, and analyse financial data from various sources (such as CSV, Excel, databases, and web interfaces)
• Develop applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimise Python applications

Audience

• Developers
• Analysts
• Quantitative analysts (Quants)

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training aims to provide solutions for key problems faced by finance professionals. If you have a specific topic, tool, or technique you would like to add or explore further, please contact us to arrange accordingly.

This course explores practical applications of Data Science and Artificial Intelligence leveraging Python. It empowers professionals with the necessary skills to analyse data, construct machine learning models, and implement AI-driven solutions within business environments. Key topics include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data management using Spark, and data-driven storytelling. It is an ideal choice for beginners pursuing a Python data science certification and career-focused analytics training.

The KNIME Analytics Platform is a leading open-source solution for driving data-based innovation. It empowers you to uncover hidden potential within your data, extract fresh insights, and forecast future trends. Featuring over 1,000 modules, hundreds of ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course offers an excellent opportunity for beginners, advanced users, and KNIME experts to become familiar with KNIME, learn to utilise it more effectively, and develop clear, comprehensive reports based on KNIME workflows.

This instructor-led live training (available online or onsite) is designed for data professionals aiming to leverage KNIME to address complex business requirements.

It targets participants who may not have programming experience but wish to utilise cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be able to:

• Install and configure KNIME.
• Build Data Science scenarios
• Train, test and validate models
• Implement the end-to-end value chain for data science models

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course or to learn more about this program, please contact us to arrange.

This instructor-led, live training in Adelaide (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists keen on leveraging machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

Upon completing this course, participants will be able to:

• Grasp and distinguish between key machine learning paradigms.
• Investigate data preprocessing techniques and model evaluation metrics.
• Implement machine learning algorithms to address real-world data challenges.
• Utilise Python libraries and Jupyter notebooks for practical development.
• Construct models for prediction, classification, recommendation, and clustering.