
Online or onsite, instructor-led Data Mining training courses demonstrate through hands-on practice the fundamentals of Data Mining, its sources of methods including Artificial intelligence, Machine learning, Statistics and Database systems, and its use and applications.
Data Mining 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 Mining training can be carried out locally on customer premises in Finland or in NobleProg corporate training centers in Finland.
NobleProg -- Your Local Training Provider
Testimonials
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
The information given was interesting and the best part was towards the end when we were provided with Data from Murex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course: Data Mining and Analysis
The hands on exercise and the trainer capacity to explain complex topics in simple terms
youssef chamoun
Course: Data Mining and Analysis
I like the exercices done
Nour Assaf
Course: Data Mining and Analysis
I really enjoyed learning new and interesting things.
SIVECO Romania SA
Course: Data Mining
very tailored to needs
Yashan Wang
Course: Data Mining with R
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Learning about all the chart types and what they are used for. Learning the value of decluttering. Learning about the methods to show time data.
Susan Williams
Course: Data Visualization
I really appreciated that Jeff utilized data and examples that were applicable to education data. He made it interesting and interactive.
Carol Wells Bazzichi
Course: Data Visualization
I thought that the information was interesting.
Allison May
Course: Data Visualization
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Course: Data Mining & Machine Learning with R
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course: Data Science for Big Data Analytics
The example and training material were sufficient and made it easy to understand what you are doing
Teboho Makenete
Course: Data Science for Big Data Analytics
The trainer was very concern about individual understanding.
Muhammad Surajo Sanusi - Birmingham City University
Course: Foundation R
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course: Foundation R
I was benefit from the good examples and opportunity to follow along.
Environmental and Climate Change Canada
Course: Foundation R
Very useful in because it helps me understand what we can do with the data in our context. It will also help me
Nicolas NEMORIN - Adecco Groupe France
Course: KNIME Analytics Platform for BI
The way it was conducted, the way trainer keeps contact with audience, materials, everything was really good!
Marcin Prewo - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
A lot of exercises, trainer was always helping us and giving the solution, he was always answering our questions & explaining our doubts. The trainer was also always checking with us about the break we would like to take etc.
GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Data Mining Subcategories in Finland
Data Mining Course Outlines in Finland
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
- Understand the fundamentals of data mining.
- Learn how to import and assess data quality with the Modeler.
- Develop, deploy, and evaluate data models efficiently.
- Data analysts or anyone interested in learning how to interpret data to solve problems
- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.
- Use cluster analysis for data mining
- Master R syntax for clustering solutions.
- Implement hierarchical and non-hierarchical clustering.
- Make data-driven decisions to help to improve business operations.
- Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection.
- Compare and implement various strategies for solving real-world data mining problems.
- Understand and interpret the results.
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
- To request a customized training for this course, please contact us to arrange.
- Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
- Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
- Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
- Build and deploy highly scalable and repeatable warehouses.
- Understand MonetDB and its features
- Install and get started with MonetDB
- Explore and perform different functions and tasks in MonetDB
- Accelerate the delivery of their project by maximizing MonetDB capabilities
- Developers
- Technical experts
- Part lecture, part discussion, exercises and heavy hands-on practice
- The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework.
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