Data Science Training in Delft

This instructor-led, live training in Delft (online or onsite) is tailored for beginner-level professionals seeking to understand pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

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

This instructor-led, live training in Delft (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 Delft (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 Delft (online or on-site) 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 become immensely popular within the financial sector. Utilized by major investment banks and hedge funds, it is employed to construct a diverse array of financial applications, ranging from core trading systems to risk management frameworks.

In this instructor-led live training, participants will learn how to leverage Python to develop practical solutions for various specific finance-related challenges.

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

• Grasp the fundamentals of the Python programming language
• Download, install, and maintain the optimal development tools for creating financial applications in Python
• Select and employ the most appropriate Python packages and techniques to organize, visualize, and analyze financial data from multiple sources (CSV, Excel, databases, web, etc.)
• Construct applications that address issues 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

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

Note

• This training focuses on providing solutions for key problems encountered by finance professionals. If you have a specific topic, tool, or technique you wish to add or expand upon, please contact us to arrange this.

This course explores practical methodologies for Data Science and AI via Python, empowering professionals with the expertise to analyze data, construct machine learning models, and implement AI-driven applications within business settings. It encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data technologies using Spark, and data-driven storytelling. This program is ideal for beginners aiming to obtain a Python data science certification and receive career-focused analytics training.

The KNIME Analytics Platform stands as a premier open-source solution for driving data-centric innovation. It empowers you to uncover the hidden potential within your data, extract new insights, or forecast future trends. Featuring over 1,000 modules, numerous ready-to-execute examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, the KNIME Analytics Platform serves as the ideal toolkit for data scientists and business analysts alike.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with the platform, learn how to utilize it more effectively, and create clear, comprehensive reports based on KNIME workflows.

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

It is tailored for an audience with no programming background who intend to use cutting-edge tools to implement analytical scenarios.

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

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

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical 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 make arrangements.

This instructor-led live training in Delft (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who want to leverage machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.