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. Medellín onsite live Data Mining trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Medellín - San Fernando Plaza
06th Floor Torre Proteccion, Medellín, colombia
The center occupies the sixth floor of the complex. As well as being steps away from an excellent food court and other amenities, the center is convenient for the Zona Rosa tourist district. The building is convenient for public transport and offers easy access to the main bus routes, metro stations Poblado and Aguacatala, and taxis.
This instructor-led, live training in Medellín (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:
This instructor-led, live training in Medellín (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.
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.
This instructor-led, live training in Medellín (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.
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.
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.
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.
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, please contact us to arrange.
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.
In this instructor-led, live training in Medellín, 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.
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.
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.
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.
This instructor-led, live training in Medellín (online or onsite) is aimed at beginner-level professionals who wish to gain a mastery of the fundamentals of R and how to work with data.By the end of this training, participants will be able to:
Understand the R programming environment and RStudio interface.
Import, manipulate, and explore datasets using R commands and packages.
Perform basic statistical analysis and data summarization.
Generate visualizations using both base R and ggplot2.
Manage workspaces, scripts, and packages effectively.
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
The "Analytics Platform KNIME" training offers a comprehensive overview of this free data analytics platform. The program includes an introduction to data processing and analysis, installation and configuration KNIME, building workflow, methodology for creating business models and data modeling. The course also covers advanced data analysis tools, workflow import and export, tool integration, ETL processes, data mining, visualization, extensions and integrations with tools such as R, Java, Python, Gephi, Neo4j. The conclusion includes an overview of reporting, integration with BIRT and KNIME WebPortal.
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.
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.
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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Testimonials (10)
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse
Open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course - Process Mining
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course - Foundation R
The tool's capacity
Gerardo Avila - Reckitt Benckizer
Course - KNIME Analytics Platform for BI
Machine Translated
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 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
I thought that the information was interesting.
Allison May
Course - Data Visualization
I like the exercises done.
Nour Assaf
Course - Data Mining and Analysis
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The trainer was so knowledgeable and included areas I was interested in.
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