Data Science Training in Thessaloniki

This instructor-led, live training in Thessaloniki (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of 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 Thessaloniki (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 Thessaloniki (online or onsite) is designed for 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 Thessaloniki (online or in-person) is designed for novice data scientists and IT specialists seeking to grasp the fundamentals of data science via Google Colab.

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

• Configuring and navigating Google Colab.
• Writing and running fundamental Python code.
• Importing and managing datasets.
• Generating visualisations using Python libraries.

Python has emerged as a highly popular programming language within the financial sector. Widely adopted by major investment banks and hedge funds, it is utilized to develop a diverse array of financial applications, ranging from core trading systems to sophisticated risk management tools.

Through this instructor-led live training, participants will gain the skills necessary to use Python for developing practical applications that address specific financial challenges.

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

• Grasp the fundamental concepts of Python programming
• Download, install, and maintain optimal development tools for building financial applications in Python
• Choose and apply appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
• Construct applications that solve 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 mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training is designed to offer solutions to key problems faced by finance professionals. If there is a specific topic, tool, or technique you wish to include or expand upon, please contact us to arrange.

This course delves into practical methodologies for Data Science and AI leveraging Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Key topics include the CRISP-DM workflow, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is particularly suitable for beginners looking to obtain a Python data science certification and receive career-focused analytics training.

KNIME Analytics Platform stands as a premier open-source solution for driving data-based innovation, enabling you to uncover hidden potential within your data, extract new insights, or forecast future trends. Boasting over 1,000 modules, numerous ready-to-run examples, a broad suite of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for data scientists and business analysts alike.

This course offers an excellent entry point for beginners, advanced users, and KNIME experts to familiarize themselves with KNIME, enhance their proficiency, and learn to generate clear, comprehensive reports using 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 specifically tailored for participants without programming experience who wish to utilize cutting-edge tools to implement analytics scenarios.

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

• Install and configure KNIME.
• Develop 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 practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• For requests regarding customized training for this course or to learn more about the program, please contact us to arrange.

This instructor-led, live training in Thessaloniki (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists looking 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.