Delivered either online or on-site, our instructor-led live Data Science training courses utilize hands-on exercises to demonstrate how to derive meaningful knowledge from data in various formats.
Data Science training is offered as either "online live training" or "onsite live training." Online live training, also referred to as "remote live training," is conducted through an interactive remote desktop. Onsite live training takes place either at your customer premises in Copenhagen or within NobleProg’s corporate training centers in Copenhagen.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This live, instructor-led training in Copenhagen (offered online or onsite) is designed for entry-level professionals aiming to master the concept of pre-trained models. It provides the skills necessary to apply these models to solve real-world issues without the requirement of building them from scratch.
By the conclusion of this training, participants will be able to:
Understand the nature and benefits of pre-trained models.
Explore different pre-trained model architectures and their practical uses.
Adjust a pre-trained model to suit specific tasks.
Apply pre-trained models in simple machine learning projects.
This live, instructor-led training in Copenhagen (available online or onsite) is tailored for intermediate-level data scientists and analysts aiming to optimize their data science workflows with AWS Cloud9.
By the conclusion of this program, participants will have the capability to:
Configure a comprehensive data science environment in AWS Cloud9.
Conduct data analysis utilizing Python, R, and Jupyter Notebook within Cloud9.
Integrate AWS Cloud9 with essential AWS data services such as S3, RDS, and Redshift.
Apply AWS Cloud9 for the creation and release of machine learning models.
Refine cloud-based workflows to enhance data analysis and processing performance.
This instructor-led live training in Copenhagen (available online or onsite) is designed for intermediate-level professionals aiming to automate and oversee machine learning workflows, encompassing model training, validation, and deployment using Apache Airflow.
Upon completion of this training, participants will be equipped to:
Configure Apache Airflow for the orchestration of machine learning workflows.
Automate processes for data preprocessing, model training, and validation.
Integrate Airflow with diverse machine learning frameworks and tools.
Deploy machine learning models via automated pipelines.
Monitor and optimize machine learning workflows within production environments.
This live, instructor-led training in Copenhagen (online or onsite) is designed for beginner-level data scientists and IT professionals seeking to learn the basics of data science with Google Colab.
Upon completing this training, participants will be equipped to:
Configure and navigate the Google Colab interface.
Develop and execute fundamental Python code.
Import and process datasets.
Generate visualizations using various Python libraries.
This 35-hour instructor-led course in Copenhagen guides participants through using Python to create practical financial applications. It covers data analysis, asset allocation, and risk management via a hands-on approach, merging lectures with extensive practical exercises.
This 35-hour program in Copenhagen offers a practical exploration of Data Science and AI using Python. Participants will engage with CRISP-DM workflows, machine learning through TensorFlow, NLP, and Big Data processing via Spark. It is an ideal starting point for beginners aiming to develop job-ready analytics capabilities and achieve Python data science certification for business applications.
This instructor-led training in Copenhagen introduces the KNIME Analytics Platform as a driver for data-driven innovation. Participants will gain the skills to build data science scenarios, train and validate models, and implement end-to-end data value chains through hands-on labs and practical exercises.
This professionally guided, live training in Copenhagen (offered online or on-site) is tailored for intermediate-level data analysts, developers, and prospective data scientists seeking to utilize Python for machine learning to extract insights, generate predictions, and automate data-based decisions.
By the conclusion of this course, participants will be capable of:
Understanding and differentiating key machine learning paradigms.
Examining data preprocessing techniques and model assessment metrics.
Applying machine learning algorithms to resolve real-world data issues.
Utilizing Python libraries and Jupyter notebooks for practical development.
Developing models for forecasting, categorization, recommendation, and clustering.
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Testimonials (2)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
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