R Language Schulungen
R (R Language) ist eine Open Source-Programmiersprache für statistische Berechnungen, Datenanalyse und Grafik. R wird von einer wachsenden Zahl von Managern und Datenanalysten in Unternehmen und Hochschulen genutzt. R hat auch eine starke Anhängerschaft unter Statistikern, Ingenieuren und Wissenschaftlern ohne Programmierkenntnisse, die es einfach zu bedienen finden. NobleProg-Vor-Ort-Live-R-Schulungen demonstrieren durch praktisches Üben verschiedener 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 in verschiedenen Formaten verfügbar, einschließlich Live-Training vor Ort und Live-Training mit einem interaktiven Remote-Desktop-Setup. Das lokale R-Training kann live in den Räumlichkeiten des Kunden oder in lokalen Trainingszentren von NobleProg durchgeführt werden.
Erfahrungsberichte
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. |
| 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 |
| rprogda | R Programming for Data Analysis | 14 hours | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 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. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python 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 Python, Keras, and TensorFlow to create deep learning models for banking Build their own deep learning credit risk model using Python Audience Developers Data scientists 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). |
| 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 |
| 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. |
| 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 |
| 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. |
| datamodeling | Pattern Recognition | 35 hours | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| 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 |
| kdd | Knowledge Discover 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. |
| 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. |
| 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 |
| 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. |
| BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 hours | Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress |
| 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. |
| 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 |
| 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 |
| 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 |
| 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. |
| 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. |
| 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. |
| 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. |
| 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. |
| 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 |
| 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. |
| 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 |
| rintro | Einführung in R | 21 hours | Forecasters, statisticians, managers, analysts who want to use R software http://www.r-project.org/. It shows you how to use the software in available GUI's and command lines. |
| rdataana | R für Datenanalyse und Forschung | 7 hours | Audience managers developers scientists students Format of the course on-line instruction and discussion OR face-to-face workshops |
| frcr | Prognosen mit R | 14 hours | This course allows delegate to fully automate the process of forecasting with R |
Upcoming Courses
| Course | Schulungsdatum | Kurspreis (Fernkurs / Schulungsraum) |
|---|---|---|
| NLP: Natural Language Processing with R - Nürnberg | Mi, 2018-06-06 09:30 | 5250EUR / 6050EUR |
| Prognosen mit R - Potsdam | Do, 2018-06-07 09:30 | 3500EUR / 4100EUR |
| Machine Learning Fundamentals with R - Hannover | Do, 2018-06-07 09:30 | 3500EUR / 4100EUR |
| Pattern Recognition - München | Mo, 2018-06-11 09:30 | 8750EUR / 9950EUR |
| R - Hamburg | Mo, 2018-06-11 09:30 | 5250EUR / 6050EUR |
Statistik Schulung, Statistik boot camp, Statistik Abendkurse, Statistik Wochenendkurse
, Statistik Seminar, Statistik Coaching, Statistik Training, Statistik Seminare,Statistik Kurs, Statistik Lehrer