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 Estonia or in NobleProg corporate training centers in Estonia.
NobleProg -- Your Local Training Provider
Testimonials
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course: Data Mining & Machine Learning with R
Very tailored to needs.
Yashan Wang
Course: Data Mining with R
I like the exercises done.
Nour Assaf
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
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course: Data Mining and Analysis
I thought that the information was interesting.
Allison May
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
Learning about all the chart types and what they are used for. Learning the value of cluttering. Learning about the methods to show time data.
Susan Williams
Course: Data Visualization
Trainer was enthusiastic.
Diane Lucas
Course: Data Visualization
I really liked the content / Instructor.
Craig Roberson
Course: Data Visualization
I am a hands-on learner and this was something that he did a lot of.
Lisa Comfort
Course: Data Visualization
I liked the examples.
Peter Coleman
Course: Data Visualization
I liked the examples.
Peter Coleman
Course: Data Visualization
I enjoyed the good real world examples, reviews of existing reports.
Ronald Parrish
Course: Data Visualization
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
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 content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
Course: From Data to Decision with Big Data and Predictive Analytics
I really enjoyed the all the best.
Halil polat - Amazon Development Center Poland Sp. z o.o.
Course: Data Mining and Analysis
The trainer concentrated on the key topics.
Amazon Development Center Poland Sp. z o.o.
Course: Data Mining and Analysis
Expertise and huge knowledge of the trainer.
Amazon Development Center Poland Sp. z o.o.
Course: Data Mining and Analysis
I was benefit from the guidance and sharing life examples + answering all questions.
Marta Melloch - Amazon Development Center Poland Sp. z o.o.
Course: Data Mining and Analysis
I really enjoyed learning new and interesting things.
SIVECO Romania SA
Course: Data Mining
I was benefit from the good examples and opportunity to follow along.
Environmental and Climate Change Canada
Course: Foundation R
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course: Foundation R
The trainer was very concern about individual understanding.
Muhammad Surajo Sanusi - Birmingham City University
Course: Foundation R
Excellent presentation and it gives me confidence to build on knowledge gained.
Birmingham City University
Course: Foundation R
Background knowledge and 'provenance' of trainer.
Francis McGonigal - Birmingham City University
Course: Foundation R
Resources
Hafiz Rana - Birmingham City University
Course: Foundation R
Good explanations on how we do things
Birmingham City University
Course: Foundation R
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna Yartseva - Birmingham City University
Course: Foundation R
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course: Data Science for Big Data Analytics
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
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
open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
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
a lot of execises
GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Lots of practice during the training and quizzes were useful and made it more interactive
Valter Pinho - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
There was a lot of exercises - I like it.
GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Data Mining Subcategories in Estonia
Data Mining Course Outlines in Estonia
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Estonia (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.
By the end of this training, participants will be able to:
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
14 hours
This instructor-led, live training in Estonia (online or onsite) is aimed at data analysts or anyone who wishes to use SPSS Modeler to perform data mining activities.
By the end of this training, participants will be able to:
- Understand the fundamentals of data mining.
- Learn how to import and assess data quality with the Modeler.
- Develop, deploy, and evaluate data models efficiently.
21 hours
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
- 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.
14 hours
This instructor-led, live training in Estonia (online or onsite) is aimed at data scientists who wish to use Excel for data mining.
- 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.
14 hours
The objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level.
14 hours
Goal:
Learning to work with SPSS at the level of independence
The addressees:
Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.
21 hours
Course can be provided with any tools, including free open-source data mining software and applications
21 hours
Audience
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run, so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
14 hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
28 hours
Objective:
Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results.
28 hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.
14 hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
28 hours
This course is intended for engineers and decision makers working in data mining and knoweldge discovery.
You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.
35 hours
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
21 hours
MATLAB is a numerical computing environment and programming language developed by MathWorks.
28 hours
In this instructor-led, live training in Estonia, participants will learn how to build a Data Vault.
By the end of this training, participants will be able to:
- 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.
28 hours
MonetDB is an open-source database that pioneered the column-store technology approach.
In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it.
By the end of this training, participants will be able to:
- 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
7 hours
The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.
21 hours
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
14 hours
This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python.
By the end of this training, participants will be able to:
- 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.
35 hours
KNIME is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with only minimal, programming. To some extent as advanced analytics tool KNIME can be considered as a SAS alternative.
Since 2006, KNIME has been used in pharmaceutical research, it also used in other areas like CRM customer data analysis, business intelligence and financial data analysis.
14 hours
This instructor-led, live training in Estonia (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.
By the end of this training, participants will be able to:
- 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.
21 hours
Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency.
Format of the Course
- 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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