Data Science Training in Évora

This instructor-led, live training in Évora (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 Évora (online or onsite) targets intermediate-level data scientists and analysts who wish to use AWS Cloud9 to streamline their data science workflows.

Upon completing this training, participants will be able to:

• Establish a data science environment in AWS Cloud9.
• Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9.
• Integrate AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Utilize AWS Cloud9 for developing and deploying machine learning models.
• Optimize cloud-based workflows for efficient data analysis and processing.

This instructor-led live training in Évora (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.

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

• Configuring Apache Airflow to orchestrate machine learning workflows.
• Automating tasks related to data preprocessing, model training, and validation.
• Integrating Airflow with various machine learning frameworks and tools.
• Deploying machine learning models through automated pipelines.
• Monitoring and optimising machine learning workflows in production environments.

This trainer-led, live training in Évora (online or in-person) is designed for beginner-level data scientists and IT professionals who wish to learn the fundamentals 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 established itself 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, spanning from core trading systems to comprehensive risk management solutions.

During this instructor-led live training, participants will learn how to leverage Python to create practical applications that address specific challenges in the finance industry.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain the most effective development tools for building financial applications using Python
• Choose and employ the most appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (such as CSV, Excel, databases, and web interfaces)
• Develop applications that solve problems related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Format of the course

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

Note

• This training is designed to provide solutions for some of the key challenges faced by finance professionals. However, if you have a specific topic, tool, or technique you wish to include or elaborate on, please contact us to make arrangements.

Explores practical methodologies for Data Science and Artificial Intelligence (AI) using Python. This course empowers professionals with the expertise to analyse data, develop machine learning models, and implement AI-driven solutions within business environments. Topics include CRISP-DM workflows, 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 gain career-focused analytics training.

The KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover latent potential within their data, extract novel insights, or forecast future trends. Boasting over 1,000 modules, numerous pre-built examples, a robust suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for any data scientist or business analyst.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME specialists alike to become familiar with KNIME, enhance their proficiency, and learn how 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 targeted at participants with no programming background who wish to utilize cutting-edge tools to implement analytics scenarios.

By the conclusion 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 of data science models.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Évora (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.