# Szkolenia R

Pakiet R jest wszechstronnym narzędziem do analizy statystycznej. Do najczęściej spotykanych zastosowań R należy standardowa analiza statystyczna, analiza ekonometryczna symulacje stochastyczne czy data mining. Oprogramowanie R oparte jest na licencji GPL co oznacza że jest całkowicie darmowy. Wśród zalet tego narzędzia należy wskazać ponadto dynamiczny i nieustanny rozwój. Nowe osiągnięcia w dziedzinie nauki z zakresu analizy danych są w R najszybciej na rynku upowszechniane za pomocą ogólnodostępnych pakietów. Pakiet R umożliwia nie tylko analizę danych ale również profesjonalne sposoby prezentacji wyników. Oferowane szkolenia są dla osób które dopiero zaczynają swoją pracę z R jak i osób znających to środowisko i chcących poszerzyć swoje umiejętności z wybranego obszaru analizy danych.

## Opinie uczestników

## Plany Szkoleń R

Kod | Nazwa | Czas trwania | Charakterystyka kursu |
---|---|---|---|

mlbankingr | Machine Learning for Banking (with R) | 28 godz. | 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 |

rforfinance | R Programming for Finance | 28 godz. | 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. |

mrkanar | Analiza Marketingowa w R | 21 godz. | 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. |

dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 godz. | 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 |

danagr | Data and Analytics - from the ground up | 42 godz. | 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. |

dataminr | Data Mining z wykorzystaniem R | 14 godz. | 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 godz. | 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 |

dmmlr | Data Mining & Machine Learning with R | 14 godz. | 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. |

bigdatar | Programming with Big Data in R | 21 godz. | |

mlfinancer | Machine Learning for Finance (with R) | 28 godz. | 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 |

bigddbsysfun | Big Data & Database Systems Fundamentals | 14 godz. | The course is part of the Data Scientist skill set (Domain: Data and Technology). |

rlang | R | 21 godz. | 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 godz. | 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 |

kdd | Knowledge Discover in Databases (KDD) | 21 godz. | 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. |

webappsr | Building Web Applications in R with Shiny | 7 godz. | 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. |

intermediaterforfinance | Intermediate R for Finance | 21 godz. | 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 |

datamodeling | Pattern Recognition | 35 godz. | 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 |

rintrob | Introductory R for Biologists | 28 godz. | 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. |

shinyrhtml | Shiny, R and HTML: Merging Data Science and Web Development | 7 godz. | 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 |

dsbda | Data Science for Big Data Analytics | 35 godz. | 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. |

dataar | Data Analytics With R | 21 godz. | 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 |

67795 | Numerical Methods | 14 godz. | 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. |

rprogda | R Programming for Data Analysis | 14 godz. | This course is part of the Data Scientist skill set (Domain: Data and Technology) |

MLFWR1 | Machine Learning Fundamentals with R | 14 godz. | 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. |

predmodr | Predictive Modelling with R | 14 godz. | 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. |

rneuralnet | Sieci Neuronowe w R | 14 godz. | Szkolenie jest wprowadzeniem do wdrożenia sieci neuronowych w życiu codziennym wykorzystując oprogramowanie R-project. |

intror | Introduction to R with Time Series Analysis | 21 godz. | |

mrkfct | Prognozowanie Rynku | 14 godz. | Kurs został przygotowany dla menadżerów, analityków biznesowych, przedsiębiorców, którzy chcieliby usprawnić wykorzystywane metody prognozowania, jak również dla tych, którzy dopiero rozważają ich wprowadzenie. Omówione na kursie narzędzia oraz metody mogą zostać później zostosowane do : prognozowania sprzedaży, ustalania planów sprzedażowych, zarządzania kanałami sprzedaży prognozowania zachowania rynku, ryzyka ekonomicznego, zmian ekonomicznych prognozowania zmian technologicznych, prognozowania zapotrzebowania produktowego, zarządzania łańcuchem dostaw Kurs ma za zadanie pokazanie uczestnikom serii narzędzi, frameworków, metodologii oraz algorytmów, przydatnych przy próbach przewidywania przyszłości opartych o analizę danych. Podczas kursu, uczestnicy nauczą się również zastosowania omówionych metod w standardowych narzędziach takich jak MS Excel czy oprogramowaniu OpenSource' wym - R- pakiet statystyczny. Metody oraz zasady przedstawione na kursie mogą być bez problemu zaimplementowane do każdego innego oprogramowania (np. SAS, SPSS, Statistica, MINITAB ...itp.) |

advr | Advanced R | 7 godz. | This course covers advanced topics in R programming. |

datascience | Data Science Training | 21 godz. | Data Science Training Aim: Obtaining the required knowledge for application of Data Science methods and also getting consultancy for establishing a Data Science team in an insurance company |

nlpwithr | NLP: Natural Language Processing with R | 21 godz. | 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 | Advanced R Programming | 7 godz. | This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills. The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience |

BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 godz. | 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. |

tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 godz. | 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 |

rintro | Wprowadzenie do R | 21 godz. | 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. This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (http://www.r-project.org) both on a command line and in a graphical user interface (GUI). |

rdataana | R dla analityków danych i naukowców | 7 godz. | Audience managers developers scientists students Format of the course on-line instruction and discussion OR face-to-face workshops |

frcr | Prognozowanie w R | 14 godz. | This course allows delegate to fully automate the process of forecasting with R |

## Najbliższe szkolenia

Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
---|---|---|

Data Mining & Machine Learning with R - Toruń, ul. Żeglarska 10/14 | czw., 2018-04-05 09:00 | 3330PLN / 4080PLN |

A Practical Introduction to Data Analysis and Big Data - Kraków, ul. Rzemieślnicza 1 | pon., 2018-04-09 09:00 | 8320PLN / 9820PLN |

R - Szczecin, ul. Sienna 9 | wt., 2018-04-10 09:00 | 2990PLN / 3990PLN |

Sieci Neuronowe w R - Gliwice ul. Karola Marksa 11 | czw., 2018-04-12 09:00 | 3330PLN / 4080PLN |

Big Data & Database Systems Fundamentals - Bydgoszcz, ul. Dworcowa 94 | pon., 2018-04-16 09:00 | 3330PLN / 4080PLN |