Data Science Training in Radom

This instructor-led, live training in Radom (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 Radom (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 Radom (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 Radom (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 is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.

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

• Understand the fundamentals of the Python programming language
• Download, install and maintain the best development tools for creating financial applications in Python
• Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
• Build applications that solve problems 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

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.

 Dives into practical approaches to Data Science and AI using Python — equips professionals with the skills to explore data, build machine learning models, and deploy AI-driven applications in business contexts; Covers CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data with Spark, and data-driven storytelling; Ideal for beginners seeking a Python data science certification and career-ready analytics training.

KNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist and business analyst.

This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows

This instructor-led, live training (online or onsite) is aimed at data professionals who wish to use KNIME to solve complex business needs.

It is targeted for the audience that doesn't know programming and intends to use cutting edge tools to implement analytics scenarios

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

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

Format of the Course

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

Course Customization Options

• To request a customized training for this course or to know more on this program, please contact us to arrange.

This instructor-led, live training in Radom (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.