Online or onsite, instructor-led live Data Science training courses demonstrate through hands-on practice how to extract knowledge from data in different forms.
Data Science training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Data Science training can be carried out locally on customer premises in Graz or in NobleProg corporate training centers in Graz.
NobleProg -- Your Local Training Provider
NobleProg Graz
Waagner-Biro-Strasse 47, Graz, Austria, 8020
Overview
Our training facilities are located at Waagner-Biro-Strasse 47 in Graz. Our spacious training rooms are located directly in the old town and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are best reached via the A9 motorway and the federal highway 67.
Parking spaces
There are parking spaces in the streets around our training rooms as well as the ContiPark multi-storey car park.
Local infrastructure
There are numerous restaurants in the downtown area and hotels are also within walking distance.
This instructor-led, live training in Graz (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 Graz (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 Graz (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 Graz (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:
This 35-hour instructor-led course in Graz teaches participants to use Python for building practical financial applications. It covers data analysis, asset allocation, and risk management through a hands-on approach, combining lecture and heavy practical exercises.
This 35-hour training in Graz covers practical Data Science and AI using Python. Learn CRISP-DM workflows, machine learning with TensorFlow, NLP, and Big Data with Spark. Ideal for beginners seeking career-ready analytics skills and Python data science certification for business contexts.
This instructor-led training in Graz introduces KNIME Analytics Platform for data-driven innovation. Participants will learn 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 instructor-led, live training in Graz (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.
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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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