Cursurile de formare locală, instruite live R (R Language), demonstrează prin practicarea manuală diferite aspecte ale limbajului R, inclusiv fundamentele programării R, programarea avansată R și R pentru analiza datelor și vizualizarea datelor Exercițiile noastre de instruire se referă la problemele și soluțiile din lumea reală în domenii precum Finanțe, Bancare și Asigurări Cursurile de formare NobleProg R variază de la cursuri începător până la cursuri avansate și sunt populare în rândul companiilor care doresc să adopte R pentru dezvoltarea aplicațiilor Machine Learning și Deep Learning Formarea R este disponibilă ca "formare live la fața locului" sau "formare live la distanță" Training-ul live la fața locului poate fi efectuat la fața locului la sediul clientului Moldova sau în centrele de formare corporativa NobleProg din Moldova Instruirea live la distanță este realizată printr-un desktop interactiv, la distanță NobleProg Furnizorul dvs de formare locală.
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Mărturii
El a fost foarte informativ și de ajutor.
Pratheep Ravy
Curs: Predictive Modelling with R
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Am răspunsuri la toate întrebările mele.
Natalia Gladii
Curs: Data Analytics With R
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O mulțime de probleme care pot fi explorate după formare
Klaudia Kłębek
Curs: Data Mining z wykorzystaniem R
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Formatorul a fost atât de informat și a inclus zonele în care mă interesează.
Mohamed Salama
Curs: Data Mining & Machine Learning with R
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Foarte adaptate nevoilor.
Yashan Wang
Curs: Data Mining with R
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M-am bucurat cu adevărat să lucrez la 1: 1 cu Gunner.
Bryant Ives
Curs: Introduction to R
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Mi-au plăcut noile cunoștințe despre învățarea mașinilor adânci.
Josip Arneric
Curs: Neural Network in R
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Am câștigat câteva cunoștințe despre NN în general, și ceea ce a fost cel mai interesant pentru mine au fost noile tipuri de NN care sunt populare în zilele noastre.
Tea Poklepovic
Curs: Neural Network in R
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M-am bucurat cel mai mult de graficele din R :))).
Faculty of Economics and Business Zagreb
Curs: Neural Network in R
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Stilul flexibil și prietenos. Aflați exact ce a fost util și relevant pentru mine.
Jenny Tickner
Curs: Advanced R
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Mi-a plăcut foile Excel oferind exerciții cu exemple. Aceasta a însemnat că, dacă Tamil ar fi fost răzbunat cu ajutorul altor persoane, aș putea să mă descurc cu următoarele părți.
Luke Pontin
Curs: Data and Analytics - from the ground up
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Învățați cum să utilizați Excel în mod corespunzător.
Torin Mitchell
Curs: Data and Analytics - from the ground up
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Modul în care antrenorul a făcut subiecte complexe ușor de înțeles.
Adam Drewry
Curs: Data and Analytics - from the ground up
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Instrucțiune detaliată și cuprinzătoare oferită de un expert cu experiență și cu o cunoaștere clară pe această temă.
Justin Roche
Curs: Data and Analytics - from the ground up
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Tamil este o persoană foarte cunoștință și drăguță, am învățat de la el foarte mult.
Aleksandra Szubert
Curs: Data and Analytics - from the ground up
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Mi-a plăcut prima sesiune. Foarte intens și rapid.
Digital Jersey
Curs: Data and Analytics - from the ground up
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Mai mult mi-a plăcut răbdarea Tamilului.
Laszlo Maros
Curs: Data and Analytics - from the ground up
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Chiar am beneficiat de exemplele practice din viața reală.
Wioleta (Vicky) Celinska-Drozd
Curs: Data and Analytics - from the ground up
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A răspuns la toate solicitările de subiecte și a luat mult timp să răspundă. HS
HSH Nordbank AG
Curs: Prognosen mit R
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Exercițiile libere.
Sabine Stammberger
Curs: Prognosen mit R
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Multe cunoștințe - teoretice și practice.
Anna Alechno
Curs: Forecasting with R
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Mi-a plăcut cu adevărat cunoștințele și exemplele practice.
Irina Tulgara
Curs: Forecasting with R
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Prezentare generală și înțelegere cât de mare este subiectul.
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Curs: Forecasting with R
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interacțiunea bună cu formatorul, schimbul dinamic al cunoștințelor
NIIT Limited
Curs: Data Analytics With R
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Mâinile pe exemple au fost cele mai utile.
Sean Kaukas
Curs: Introduction to R
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Formatorul Michael este foarte priceput și priceput în ceea ce privește subiectul Big Data și R. El este foarte flexibil și personalizează rapid nevoile clienților de formare. De asemenea, este foarte capabil să rezolve problemele tehnice și de subiect în mișcare. Antrenament fantastic și profesional !.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curs: Programming with Big Data in R
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Mi-a plăcut cu adevărat introducerea de pachete noi.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curs: Programming with Big Data in R
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Profesorul, domnul Michael Yan, a interacționat cu publicul foarte bine, instrucțiunea a fost clară. De asemenea, tutorele adaugă mai multe informații pe baza solicităților din partea cursanților în timpul pregătirii.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curs: Programming with Big Data in R
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Subiectul și ritmul au fost perfecte.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curs: Programming with Big Data in R
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O privire de ansamblu asupra R și o gamă bună de subiecte. Formatorul a fost bucuros să răspundă la toate întrebările.
Symphony EYC
Curs: R
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Mi-a plăcut cu adevărat cunoștințele formatorului.
Stephanie Seiermann
Curs: R
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A fost foarte informativ și a avut loc profesional. Nivelul de cunoștințe al lui Wojteks a fost atât de avansat încât a putut să răspundă la orice întrebare și a fost dispus să depună eforturi pentru a adapta formarea la nevoile mele personale.
Sonja Steiner - BearingPoint GmbH
Curs: R Programming for Data Analysis
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o mulțime de exerciții pe care le pot folosi direct în munca mea.
Alior Bank S.A.
Curs: Sieci Neuronowe w R
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Exemple privind date reale.
Alior Bank S.A.
Curs: Sieci Neuronowe w R
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neuralnet, pROC într-o buclă.
Alior Bank S.A.
Curs: Sieci Neuronowe w R
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Mi-a plăcut foarte mult exercițiile pe modelarea seriilor de timp.
Teleperformance
Curs: Data Analytics With R
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Un nou instrument care este "R" și mi se pare interesant să știu existența unui astfel de instrument pentru analiza datelor.
Michael Lopez - Teleperformance
Curs: Data Analytics With R
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Instrumentul a fost interesant și văd folosirea. Aș dori să aflu mai multe despre asta.
Teleperformance
Curs: Data Analytics With R
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O mulțime de practică.
Centrum Innowacji ProLearning Agnieszka Kołodziejczyk
Curs: R
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Cunoașterea substanțială a formatorului și experiența, diverse exemple
Anna Wysocka - Centrum Innowacji ProLearning Agnieszka Kołodziejczyk
Curs: R
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problema a fost bine prezentată și într-o manieră ordonată.
Marylin Houle - Ivanhoe Cambridge
Curs: Introduction to R with Time Series Analysis
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El a explicat cu adevărat totul bine și a folosit exemple.
royal bank of Canada
Curs: R
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M-am bucurat de auto-învățare prin exerciții și sfaturi și comenzi rapide împărtășite.
Competition Bureau
Curs: R for Data Analysis and Research
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Am fost beneficia de notele detaliate pentru a păstra și de a lucra prin intermediul după curs.
Public Health Wales NHS Trust
Curs: Introduction to Data Visualization with Tidyverse and R
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Ritmul a fost doar dreapta și atmosfera relaxată făcut candidații simt la usurinta de a pune întrebări.
Rhian Hughes - Public Health Wales NHS Trust
Curs: Introduction to Data Visualization with Tidyverse and R
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Am fost beneficia de exemple bune și posibilitatea de a urmări de-a lungul.
Environmental and Climate Change Canada
Curs: Foundation R
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M-am bucurat cu adevărat mâinile trecut exerciții.
Yunfa Zhu - Environmental and Climate Change Canada
Curs: Foundation R
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Antrenorul, mâncarea și spațiul au fost grozave.
Canada Revenue Agency
Curs: R
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Îmi place de fapt, scrierea de cod cu date eșantion și adnotating script-ul pentru referință viitoare.
Canada Revenue Agency
Curs: R
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Script-urile pre-Made utilizate pentru materialul de formare a fost foarte util. Antrenamentul interactiv a permis o înțelegere clară a fiecărui subiect.
Canada Revenue Agency
Curs: R
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R Language Subcategories
R Language Course Outlines
Numele cursului
Durata
Sinoptic
Numele cursului
Durata
Sinoptic
21 hours
Sinoptic
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.
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.
14 hours
Sinoptic
This course is an introduction to applying neural networks in real world problems using R-project software.
7 hours
Sinoptic
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
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
14 hours
Sinoptic
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.
21 hours
Sinoptic
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.
14 hours
Sinoptic
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.
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.
21 hours
Sinoptic
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.
21 hours
Sinoptic
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.
7 hours
Sinoptic
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.
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.
28 hours
Sinoptic
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.
21 hours
Sinoptic
[R](http://www.r-project.org/) 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
Audience
Developers / data analytics
Duration
3 days
Format
Lectures and Hands-on
14 hours
Sinoptic
This course is part of the Data Scientist skill set (Domain: Data and Technology)
14 hours
Sinoptic
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.
14 hours
Sinoptic
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.
21 hours
Sinoptic
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.
21 hours
Sinoptic
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 instructor-led, live 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.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live 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.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
42 hours
Sinoptic
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](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
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](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
21 hours
Sinoptic
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:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
28 hours
Sinoptic
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
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
28 hours
Sinoptic
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.
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.
21 hours
Sinoptic
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 the fundamentals of R programming as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Understand the basics of R programming
- Use R to manipulate their data to perform basic financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of R programming as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Understand the basics of R programming
- Use R to manipulate their data to perform basic financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Sinoptic
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 the basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in trading
- Create and implement their first trading strategy using R
- Analyze the performance of their strategy using R
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in trading
- Create and implement their first trading strategy using R
- Analyze the performance of their strategy using R
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Sinoptic
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
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
28 hours
Sinoptic
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
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
28 hours
Sinoptic
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
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
28 hours
Sinoptic
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
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
7 hours
Sinoptic
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
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
7 hours
Sinoptic
The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.
14 hours
Sinoptic
This instructor-led, live training in Moldova (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.
By the end of this training, participants will be able to:
- Employ algorithms to buy and sell securities at specialized increments rapidly.
- Reduce costs associated with trade using algorithmic trading.
- Automatically monitor stock prices and place trades.
By the end of this training, participants will be able to:
- Employ algorithms to buy and sell securities at specialized increments rapidly.
- Reduce costs associated with trade using algorithmic trading.
- Automatically monitor stock prices and place trades.
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