R Language Schulungen
Lokale, instruktorierte Live-R (R Language) -Trainings demonstrieren durch praktische Übungen verschiedene Aspekte der R-Sprache, einschließlich der Grundlagen der R-Programmierung, fortgeschrittenen R-Programmierung und R für Datenanalyse und Datenvisualisierung Unsere Trainings beschäftigen sich mit realen Problemen und Lösungen in Bereichen wie Finanzen, Bankwesen und Versicherungen NobleProg R Trainingskurse reichen von Anfängerkursen bis hin zu Fortgeschrittenenkursen und sind beliebt bei Unternehmen, die R für die Entwicklung von Machine Learning und Deep Learning Anwendungen einsetzen möchten R-Training ist als "Live-Training vor Ort" oder "Fern-Live-Training" verfügbar Onsite Live Training kann vor Ort bei Kunden durchgeführt werden Schweiz oder in NobleProg Corporate Trainingszentren in Schweiz Remote-Live-Training wird über einen interaktiven Remote-Desktop durchgeführt NobleProg Ihr lokaler Trainingsanbieter.
Erfahrungsberichte
Auf alle Themenwünsche eingegangen und viel Zeit für die Beantwortung genommen.
HSH Nordbank AG
Course Name: Prognosen mit R
Die freien Übungen.
Sabine Stammberger
Course Name: Prognosen mit R
He (the trainer) is excellent. Very patient, can listen well to our questions. We liked his patience and pace..
Ellen Lentz- Genentech Inc
Course Name: Introduction to R
Hands-on learning by doing
Guihong Chen - TCF
Course Name: Introduction to R
Trainer was flexible -- he tried to address our issues. The course was well organized and the trainer did have the breadth of knowledge, both in the R program and finance industry, necessary to help us.
George Rezek - TCF
Course Name: Introduction to R
I really appreciated that he (the trainer) was a practitioner and had that perspective.
Bradley Olson - TCF
Course Name: Introduction to R
He (the trainer) was excellent. I liked the pace and the focus on principles at the beginning, not skipping over the detail.
Geoff Copps - Mediabrands
Course Name: Introduction to R
What did you like the most about the training?: Able to do hands-on and get help from trainer on how to go about working on difficult questions. Faeiza Ab Rahman - Seagate Singapore International Headquarters Pte. Ltd
Course Name: Introduction to R
What did you like the most about the training?: Trainer's subject knowledge was such that complex subjects seemed easy, and he genuinely inspired me with enthusiasm for the topic. My session worked out as 1:1, and it was a real privilege to get the benefit of such a high calibre of trainer in the context of personal tuition. Harry Frost - Babcock International
Course Name: Market Forecasting
What did you like the most about the training?: Trainer's subject knowledge was such that complex subjects seemed easy, and he genuinely inspired me with enthusiasm for the topic. My session worked out as 1:1, and it was a real privilege to get the benefit of such a high calibre of trainer in the context of personal tuition. Harry Frost - Babcock International
Course Name: Market Forecasting
He was very informative and helpful.
Pratheep Ravy
Course Name: Predictive Modelling with R
Ich bekomme Antworten auf alle meine Fragen.
Natalia Gladii
Course Name: Data Analytics With R
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Viele Probleme, die nach dem Training erforscht werden können
Klaudia Kłębek
Course Name: Data Mining z wykorzystaniem R
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The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Course Name: Data Mining & Machine Learning with R
very tailored to needs
Yashan Wang
Course Name: Data Mining with R
Working with 1:1 with Gunnar.
Bryant Ives
Course Name: Introduction to R
neue Einblicke in tiefes maschinelles Lernen
Josip Arneric
Course Name: Neural Network in R
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Wir haben etwas über NN im Allgemeinen gelernt, und was für mich am interessantesten war, waren die neuen Typen von NN, die heutzutage populär sind
Tea Poklepovic
Course Name: Neural Network in R
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Graphen in R :)))
Faculty of Economics and Business Zagreb
Course Name: Neural Network in R
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Der flexible und freundliche Stil. Ich lernte genau, was für mich nützlich und relevant war
Jenny Tickner
Course Name: Advanced R
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Ich genoss die bereitgestellten Excel-Tabellen mit den Übungen mit Beispielen. Das bedeutete, dass, wenn Kamil anderen Leuten helfen würde, ich mit den nächsten Teilen weitermachen könnte
Luke Pontin
Course Name: Data and Analytics - from the ground up
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lernen, wie man Excel richtig einsetzt
Torin Mitchell
Course Name: Data and Analytics - from the ground up
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Die Art und Weise, wie der Trainer komplexe Themen verständlich machte
Adam Drewry
Course Name: Data and Analytics - from the ground up
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Detaillierte und umfassende Anleitung von erfahrenen und klar kenntnisreichen Experten zum Thema
Justin Roche
Course Name: Data and Analytics - from the ground up
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Kamil ist sehr kenntnisreich und nette Person, ich habe viel von ihm gelernt.
Aleksandra Szubert
Course Name: Data and Analytics - from the ground up
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Erste Session. Sehr intensiv und schnell
Digital Jersey
Course Name: Data and Analytics - from the ground up
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Die Geduld von Kamil
Laszlo Maros
Course Name: Data and Analytics - from the ground up
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praktische Beispiele aus dem wirklichen Leben
Wioleta (Vicky) Celinska-Drozd
Course Name: Data and Analytics - from the ground up
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Eine Menge Wissen theoretisch und praktisch
Anna Alechno
Course Name: Forecasting with R
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sein Wissen und praktische Beispiele
Irina Tulgara
Course Name: Forecasting with R
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Überblick und Verständnis, wie groß das Thema ist
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Course Name: Forecasting with R
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gute Interaktion mit dem Trainer, dynamischer Wissensaustausch
NIIT Limited
Course Name: Data Analytics With R
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Praktische Beispiele waren am hilfreichsten
Sean Kaukas
Course Name: Introduction to R
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Michael der Trainer ist sehr sachkundig und geschickt in Bezug auf das Thema Big Data und R. Er ist sehr flexibel und passt das Training schnell an die Bedürfnisse der Kunden an. Er ist auch sehr in der Lage, technische und fachliche Probleme unterwegs zu lösen. Fantastisches und professionelles Training!
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
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die Einführung neuer Pakete
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
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Der Lehrer, Herr Michael Yan, interagierte sehr gut mit dem Publikum, die Anweisung war klar. Der Tutor wird auch in der Lage sein, weitere Informationen basierend auf den Anfragen der Schüler während des Trainings hinzuzufügen
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
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Das Thema und das Tempo waren perfekt
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
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Guter Überblick über R und gute Themenvielfalt. Trainer war glücklich, alle Fragen zu beantworten
Symphony EYC
Course Name: R
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Kenntnis des Trainers
Stephanie Seiermann
Course Name: R
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Das Beispiel und das Schulungsmaterial waren ausreichend und machten es leicht zu verstehen, was Sie tun
Teboho Makenete
Course Name: Data Science for Big Data Analytics
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Es war sehr informativ und professionell gehalten. Wojteks Wissensniveau war so fortgeschritten, dass er grundsätzlich jede Frage beantworten konnte und er bereit war, sich zu bemühen, das Training auf meine persönlichen Bedürfnisse abzustimmen
Sonja Steiner - BearingPoint GmbH
Course Name: R Programming for Data Analysis
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viele Übungen, die ich direkt in meiner Arbeit anwenden kann.
Alior Bank S.A.
Course Name: Sieci Neuronowe w R
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Beispiele für echte Daten.
Alior Bank S.A.
Course Name: Sieci Neuronowe w R
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neuralnet, pROC in einer Schleife.
Alior Bank S.A.
Course Name: Sieci Neuronowe w R
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Übungen zur Zeitreihenmodellierung.
Teleperformance
Course Name: Data Analytics With R
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Schulungsübersicht
| Code | Name | Dauer | Übersicht |
|---|---|---|---|
| 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. |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in banking Use R to create deep learning models for banking Build their own deep learning credit risk model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| rprogda | R Programming for Data Analysis | 14 hours | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| shinyrhtml | Shiny, R and HTML: Merging Data Science and Web Development | 7 hours | Shiny is an open source R package that provides a web framework for building interactive web applications using R. In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML. By the end of this training, participants will be able to: Build interactive web applications with R using Shiny Audience Data scientists Web developers Statisticians Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mrkfct | Marktprognose | 14 hours | Audience This course has been created for analysts, forecasters wanting to introduce or improve forecasting which can be related to sale forecasting, economic forecasting, technology forecasting, supply chain management and demand or supply forecasting. Description This course guides delegates through series of methodologies, frameworks and algorithms which are useful when choosing how to predict the future based on historical data. It uses standard tools like Microsoft Excel or some Open Source programs (notably R project). The principles covered in this course can be implemented by any software (e.g. SAS, SPSS, Statistica, MINITAB ...) |
| bigddbsysfun | Big Data & Database Systems Fundamentals | 14 hours | The course is part of the Data Scientist skill set (Domain: Data and Technology). |
| intror | Introduction to R with Time Series Analysis | 21 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. |
| mrkanar | Marketinganalytik mit R | 21 hours | Audience: Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals. Overview: The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech. Format: Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course. Trainer: The Instructor has gained 19 years experience in customer insights and customer relationship management after originally graduating in experimental particle physics and working at the CERN laboratory. He has worked with large corporations across Europe and North America to transform the way they look at their customers and derive value from data, and has held interim management roles at leading Dutch and Irish mobile phone operators building their Insights and Customer Base Management teams. He is one of the founders of The PCA Group, which helps large corporations in Europe and South America transform their approach to marketing, and of CYBAEA, which provides analytics-as-a-service across the globe with a strong focus on commercial results. His teaching style focuses on practical example and emphasizes results over theoretical sophistication: his courses are for practitioners who need to deliver value to their organizations and while he covers just enough theory to make sure his students are on a firm footing his teaching is not geared to more theoretical students. Expect much hands-on work and very few formula. |
| advr | Advanced R | 7 hours | This course covers advanced topics in R programming. |
| rneuralnet | Training Neural Network in R | 14 hours | This course is an introduction to applying neural networks in real world problems using R-project software. |
| 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. |
| 67795 | Numerical Methods | 14 hours | This course is for data scientists and statisticians that have some familiarity with numerical methods and have at least one programming language from R, Python, Octave, and some C++ options. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose of this course is to give a practical introduction in numerical methods to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience. |
| nlpwithr | NLP: Natural Language Processing with R | 21 hours | It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| rprogadv | Fortgeschrittene "R"-Programmierung | 7 hours | Dieser Kurs ist ausgelegt für Data Scientists and Statistiker die breits Grundkenntnisse in "R & C++ coding skills und R-Code haben und fortgeschrittene "R-coding-skills" benötigen. Es handelt sich um einen praxisorientierten Fortgeschrittenen-Kurs in der Programmiersprache "R" für alle diejenigen, die die Methoden für die Arbeit benötigen. Bereichsspezifische Beispiele erhöhen die Relevanz der Schulung für die Teilnehmer |
| radvml | Advanced Machine Learning with R | 21 hours | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: Use techniques as hyper-parameter tuning and deep learning Understand and implement unsupervised learning techniques Put a model into production for use in a larger application Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 hours | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: Perform data analysis and create appealing visualizations Draw useful conclusions from various datasets of sample data Filter, sort and summarize data to answer exploratory questions Turn processed data into informative line plots, bar plots, histograms Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience Beginners to the R language Beginners to data analysis and data visualization Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bigdatar | Programming with Big Data in R | 21 hours | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| mlbankingr | Machine Learning for Banking (with R) | 28 hours | In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience Developers Data scientists Banking professionals with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| MLFWR1 | Machine Learning Fundamentals with R | 14 hours | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications. |
| danagr | Data and Analytics - from the ground up | 42 hours | Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: What has happened? processing and analyzing data producing informative data visualizations What will happen? forecasting future performance evaluating forecasts What should happen? turning data into evidence-based business decisions optimizing processes The course itself can be delivered either as a 6 day classroom course or remotely over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs. |
| dataar | Data Analytics With R | 21 hours | R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on |
| rforfinance | R Programming for Finance | 28 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: Understand the fundamentals of the R programming language Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) Build applications that solve problems related to asset allocation, risk analysis, investment performance and more Troubleshoot, integrate deploy and optimize an R application Audience Developers Analysts Quants Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| 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. |
| intermediaterforfinance | Intermediate R for Finance | 21 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples. By the end of this training, participants will be able to: Implement advanced R programming techniques Use R to manipulate their data to perform more advanced financial operations Audience Programmers Finance professionals IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| webappsr | Building Web Applications in R with Shiny | 7 hours | Description: This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS. Objective: Covers the basics of how Shiny apps work. Covers all commonly used input/output/rendering/paneling functions from the Shiny library. |
| mlfinancer | Machine Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: Understand the fundamental concepts in machine learning Learn the applications and uses of machine learning in finance Develop their own algorithmic trading strategy using machine learning with R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| rlang | R | 21 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. |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: Understand the fundamental concepts of deep learning Learn the applications and uses of deep learning in finance Use R to create deep learning models for finance Build their own deep learning stock price prediction model using R Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| predmodr | Predictive Modelling 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. |
Upcoming Courses
| Course | Schulungsdatum | Kurspreis (Fernkurs / Schulungsraum) |
|---|---|---|
| R Programming for Data Analysis - Zürich | Mi, 2018-07-11 09:30 | 3500EUR / 4000EUR |
| Big Data & Database Systems Fundamentals - Bern | Di, 2018-07-17 09:30 | 3000EUR / 3500EUR |
| Training Neural Network in R - Basel | Do, 2018-07-26 09:30 | 3500EUR / 4000EUR |