Local, 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 "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Saudi Arabia or in NobleProg corporate training centers in Saudi Arabia. Remote live training is carried out by way of an interactive, remote desktop. 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 generally was benefit from the examples.

peter coleman

Course: Data Visualization

I enjoyed the good real world examples, reviews of existing reports.

Ronald Parrish

Course: Data Visualization

I enjoyed the interaction and facts gained / learn.

Monna Liza Mengullo

Course: Data Protection

I was benefit from the discussion and exchange of ideas.

RAYMOND JACKSON PAJARILLO

Course: Data Protection

I generally liked all.

Marjorie Pepito

Course: Data Protection

He was interactive.

Suraj

Course: Semantic Web Overview

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

the scope of material

Maciej Jonczyk

Course: From Data to Decision with Big Data and Predictive Analytics

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systematizing knowledge in the field of ML

Orange Polska

Course: From Data to Decision with Big Data and Predictive Analytics

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A lot of issues that can be explored after the training

Klaudia Kłębek

Course: Data Mining z wykorzystaniem R

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The use of dataintegration

Waldemar Wisniewski

Course: Pentaho Business Intelligence (PBI) - moduły raportowe

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trainer's knowledge, available equipment

Mariusz Moskal

Course: Pentaho Business Intelligence (PBI) - moduły raportowe

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Examples enriched with real data and scenarios useful in everyday practice

Krzysztof Świątczak

Course: Pentaho Business Intelligence (PBI) - moduły raportowe

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Example exercises; Practical work experience sharing

澳新银行

Course: Data Vault: Building a Scalable Data Warehouse

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The teacher's knowledge of the data warehouse is comprehensive, and he praises it!

澳新银行

Course: Data Vault: Building a Scalable Data Warehouse

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The teacher explained in detail and discussed the atmosphere

澳新银行

Course: Data Vault: Building a Scalable Data Warehouse

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Data Mining Subcategories

Data Mining Course Outlines

Code	Name	Duration	Overview
psr	Introduction to Recommendation Systems	7 hours	Audience Marketing department employees, IT strategists and other people involved in decisions related to the design and implementation of recommender systems. Format Short theoretical background follow by analysing working examples and short, simple exercises.
datavis1	Data Visualization	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.
pentaho	Pentaho Open Source BI Suite Community Edition (CE)	28 hours	Pentaho Open Source BI Suite Community Edition (CE) is a business intelligence package that provides data integration, reporting, dashboards, and load capabilities. In this instructor-led, live training, participants will learn how to maximize the features of Pentaho Open Source BI Suite Community Edition (CE). By the end of this training, participants will be able to: - Install and configure Pentaho Open Source BI Suite Community Edition (CE) - Understand the fundamentals of Pentaho CE tools and their features - Build reports using Pentaho CE - Integrate third party data into Pentaho CE - Work with big data and analytics in Pentaho CE Audience - Programmers - BI Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - To request a customized training for this course, please contact us to arrange.
PentahoDI	Pentaho Data Integration Fundamentals	21 hours	Pentaho Data Integration is an open-source data integration tool for defining jobs and data transformations. In this instructor-led, live training, participants will learn how to use Pentaho Data Integration's powerful ETL capabilities and rich GUI to manage an entire big data lifecycle, maximizing the value of data to the organization. By the end of this training, participants will be able to: - Create, preview, and run basic data transformations containing steps and hops - Configure and secure the Pentaho Enterprise Repository - Harness disparate sources of data and generate a single, unified version of the truth in an analytics-ready format. - Provide results to third-part applications for further processing Audience - Data Analyst - ETL developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice
smtwebint	Semantic Web Overview	7 hours	The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
foundr	Foundation R	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.
monetdb	MonetDB	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 Audience - Developers - Technical experts Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice
datavault	Data Vault: Building a Scalable Data Warehouse	28 hours	Data vault modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all of the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema. In this instructor-led, live training, 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 Audience - Data modelers - Data warehousing specialist - Business Intelligence specialists - Data engineers - Database administrators Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice
processmining	Process Mining	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. Audience Data science professionals Anyone interested in understanding and applying process modeling and data mining
dsbda	Data Science for Big Data Analytics	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.
dmmlr	Data Mining & Machine Learning with R	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.
mdlmrah	Model MapReduce and Apache Hadoop	14 hours	The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers.
rintrob	Introductory R for Biologists	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.
datama	Data Mining and Analysis	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.
osqlide	Oracle SQL Intermediate - Data Extraction	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.
datashrinkgov	Data Shrinkage for Government	14 hours	The objective of the course is to enable participants to gain a mastery of the fundamentals of data shrinkage for government.
pmml	Predictive Models with PMML	7 hours	The course is created to scientific, developers, analysts or any other people who want to standardize or exchange their models with Predictive Model Markup Language (PMML) file format.
dataminr	Data Mining with R	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.
d2dbdpa	From Data to Decision with Big Data and Predictive Analytics	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.
datamin	Data Mining	21 hours	Course can be provided with any tools, including free open-source data mining software and applications
sspsspas	Statistics with SPSS Predictive Analytics Software	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.
kdd	Knowledge Discovery in Databases (KDD)	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 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 Format of the course 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.

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