Data Science Training in Ostrava

This instructor-led, live training in Ostrava (online or on-site) is designed for beginner-level professionals who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without the need to build models from the ground up.

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

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

This instructor-led, live training in Ostrava (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 Ostrava (online or onsite) is designed for intermediate-level participants seeking 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:

• Configure Apache Airflow for orchestrating machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in a production environment.

This instructor-led, live training session in Ostrava (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 become immensely popular within the financial sector. It is widely adopted by leading investment banks and hedge funds to develop a diverse array of financial applications, from core trading platforms to risk management systems.

During this instructor-led live training, participants will learn to leverage Python to create practical solutions for various specific finance challenges.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of the Python programming language
• Download, install, and configure the optimal development tools for building financial applications in Python
• Choose and apply appropriate Python libraries and techniques to organize, visualize, and analyze financial data from multiple sources (CSV, Excel, databases, web APIs, etc.)
• Develop applications that address issues such as asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Target Audience

• Developers
• Analysts
• Quants

Course Format

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

Note

• This training focuses on providing solutions to key problems faced by finance professionals. If you have a specific topic, tool, or technique you wish to include or expand upon, please contact us to arrange a custom agenda.

This course explores practical applications of Data Science and AI through Python, equipping professionals with the ability to analyze data, construct machine learning models, and implement AI-powered solutions in business environments. It covers the CRISP-DM methodology, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data handling with Spark, and the art of data-driven storytelling. Designed for beginners seeking Python data science certification and career-focused analytics training.

The KNIME Analytics Platform stands as a premier open-source solution for driving data innovation. It empowers users to uncover hidden potential within their data, extract new insights, or forecast future trends. Boasting over 1,000 modules, numerous ready-to-use examples, a comprehensive suite of integrated tools, and the most extensive selection of advanced algorithms available, KNIME Analytics Platform serves as the ideal toolbox for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to become familiar with the platform, learn how to utilize 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.

The course targets individuals who may not have programming knowledge but wish to utilize cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be capable of:

• Installing and configuring KNIME.
• Developing Data Science scenarios.
• Training, testing, and validating models.
• Implementing the end-to-end value chain for data science models.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation within a live laboratory environment.

Customization Options

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

This instructor-led, live training in Ostrava (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply 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.