Helyi, oktatott élő R (R nyelv) tanfolyamok demonstrálják handson gyakorlat különböző aspektusait az R nyelv, beleértve az alapjait R programozás, fejlett R programozás és R az adatok elemzése és az adatok vizualizáció Képzési gyakorlataink érintik a valós világ problémáit és megoldásait olyan területeken, mint a pénzügyek, bankok és biztosítások A NobleProg R tanfolyamok kezdettől kezdődően haladó tanfolyamokig terjednek, és népszerűek azoknál a vállalatoknál, akik R-ot akarnak alkalmazni a gépi tanulási és mély tanítási alkalmazások fejlesztésére R edzés elérhető "helyszíni élő képzés" vagy "távoli élő képzés" formájában A helyszíni élő képzés helyi szinten valósulhat meg az ügyfél telephelyén Magyarország vagy a NobleProg vállalati oktatóközpontjaiban Magyarország A távoli élőképzés interaktív, távoli asztal segítségével történik NobleProg Az Ön Helyi Képzési Szolgáltatója.
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Vélemények
Nagyon informatív és hasznos volt.
Pratheep Ravy
Kurzus: Predictive Modelling with R
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Minden kérdésemre választ kapok.
Natalia Gladii
Kurzus: Data Analytics With R
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Sok olyan kérdés, amelyet a képzés után lehet feltárni Kl
Klaudia Kłębek
Kurzus: Data Mining z wykorzystaniem R
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A tréner annyira jól ismertté vált, és olyan területeket érintett, amelyekbe érdekelt.
Mohamed Salama
Kurzus: Data Mining & Machine Learning with R
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Nagyon igényeihez igazítva.
Yashan Wang
Kurzus: Data Mining with R
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Igazán élveztem a Gunner 1: 1-es munkáját.
Bryant Ives
Kurzus: Introduction to R
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Tetszett a mély gépi tanulás új betekintése.
Josip Arneric
Kurzus: Neural Network in R
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Némi tudást szereztünk az NN-ről általában, és ami számomra a legérdekesebb volt, az új NN típusok ma népszerűek.
Tea Poklepovic
Kurzus: Neural Network in R
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Leginkább a R :) grafikonokat élveztem)).
Faculty of Economics and Business Zagreb
Kurzus: Neural Network in R
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A rugalmas és barátságos stílus. Megtanulva pontosan mi volt hasznos és releváns számomra.
Jenny Tickner
Kurzus: Advanced R
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Nagyon élveztem az Excel-lapokat, feltéve, hogy a gyakorlatok példákat mutattak. Ez azt jelentette, hogy ha a tamil másokat segített volna, akkor a következő részekkel fel tudtam szakítani.
Luke Pontin
Kurzus: Data and Analytics - from the ground up
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Megtanulják az excel megfelelő használatát.
Torin Mitchell
Kurzus: Data and Analytics - from the ground up
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Az, hogy az oktató könnyen érthetővé tette az összetett témákat.
Adam Drewry
Kurzus: Data and Analytics - from the ground up
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Részletes és átfogó oktatás, melyet tapasztalt és jól ismerő szakértő adott meg a témában.
Justin Roche
Kurzus: Data and Analytics - from the ground up
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A tamil nagyon jól ismeretes és kedves ember, sokat tanultam tőle.
Aleksandra Szubert
Kurzus: Data and Analytics - from the ground up
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Tetszett az első ülés. Nagyon intenzív és gyors.
Digital Jersey
Kurzus: Data and Analytics - from the ground up
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Leginkább a tamil türelmét szerettem.
Laszlo Maros
Kurzus: Data and Analytics - from the ground up
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Nagyon hasznot vettem a valós gyakorlati példákból.
Wioleta (Vicky) Celinska-Drozd
Kurzus: Data and Analytics - from the ground up
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Megválaszolta az összes kérést, és sok időt vett igénybe. HSH
HSH Nordbank AG
Kurzus: Prognosen mit R
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A szabad gyakorlatok.
Sabine Stammberger
Kurzus: Prognosen mit R
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Sok tudás - elméleti és gyakorlatias.
Anna Alechno
Kurzus: Forecasting with R
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Igazán szerettem tudását és gyakorlati példáit.
Irina Tulgara
Kurzus: Forecasting with R
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Áttekintés és megértés, hogy mekkora a téma.
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Kurzus: Forecasting with R
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jó interakció az oktatóval, a tudás dinamikus cseréje N
NIIT Limited
Kurzus: Data Analytics With R
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A példák kezében a leghasznosabbak voltak.
Sean Kaukas
Kurzus: Introduction to R
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Michael az edző nagyon ismerős és ügyes a Big Data és R. témájáról. Nagyon rugalmas és gyorsan testreszabja a képzést az ügyfelek igényeinek megfelelően. Ő is nagyon képes megoldani a technikai és tárgyi problémákat útközben. Fantasztikus és szakmai képzés !.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Kurzus: Programming with Big Data in R
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Nagyon élveztem az új csomagok bevezetését.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Kurzus: Programming with Big Data in R
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Az oktató, Michael Yan, nagyon jól kommunikált a közönséggel, az utasítás világos volt. Az oktató szintén megy ahhoz, hogy további információkat adjon a hallgatók kérése alapján az oktatás során.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Kurzus: Programming with Big Data in R
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A tárgy és a tempó tökéletes volt.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Kurzus: Programming with Big Data in R
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Jó áttekintés az R és a jó témakörökről. A tréner boldogan válaszolt minden kérdésre.
Symphony EYC
Kurzus: R
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Nagyon élveztem az edző ismeretét.
Stephanie Seiermann
Kurzus: R
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Nagyon informatív és professzionális módon tartották. A Wojteks tudás szintje annyira fejlett volt, hogy alapvetően bármilyen kérdést megválaszolhatott, és hajlandó volt erőfeszítéseket tenni arra, hogy a képzést saját szükségleteimhez igazítsa.
Sonja Steiner - BearingPoint GmbH
Kurzus: R Programming for Data Analysis
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sok olyan gyakorlatot, amit közvetlenül a munkámban felhasználhatok.
Alior Bank S.A.
Kurzus: Sieci Neuronowe w R
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Példák a valós adatokra.
Alior Bank S.A.
Kurzus: Sieci Neuronowe w R
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neuralnet, pROC egy hurokban.
Alior Bank S.A.
Kurzus: Sieci Neuronowe w R
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Nagyon tetszett az idősor-modellezés gyakorlata.
Teleperformance
Kurzus: Data Analytics With R
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Új eszköz, ami "R", és érdekesnek találom az ilyen elemek létezését az adatok elemzéséhez.
Michael Lopez - Teleperformance
Kurzus: Data Analytics With R
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Az eszköz érdekes volt és látom a felhasználást. Szeretnék többet megtudni róla.
Teleperformance
Kurzus: Data Analytics With R
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Sok gyakorlat.
Centrum Innowacji ProLearning Agnieszka Kołodziejczyk
Kurzus: R
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A tréner és tapasztalat anyagi ismerete, különböző példák
Anna Wysocka - Centrum Innowacji ProLearning Agnieszka Kołodziejczyk
Kurzus: R
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az ügyet jól bemutatták és rendezett módon.
Marylin Houle - Ivanhoe Cambridge
Kurzus: Introduction to R with Time Series Analysis
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Igazán jól magyarázott, és példákat használt.
royal bank of Canada
Kurzus: R
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Élveztem az önképzést gyakorlatokon keresztül, valamint a megosztott tippeket és gyorsbillentyűket.
Competition Bureau
Kurzus: R for Data Analysis and Research
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Én részesültem a részletes jegyzeteket tartani és dolgozni a tanfolyam után.
Public Health Wales NHS Trust
Kurzus: Introduction to Data Visualization with Tidyverse and R
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A tempó csak jobb volt, és a nyugodt légkör a jelentkezőknek jól érzik magukat, hogy kérdéseket feltenni.
Rhian Hughes - Public Health Wales NHS Trust
Kurzus: Introduction to Data Visualization with Tidyverse and R
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Én is profitáltam a jó példákat és lehetőséget, hogy kövessék végig.
Environmental and Climate Change Canada
Kurzus: Foundation R
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Én valóban élveztem a kezek telt gyakorlatokat.
Yunfa Zhu - Environmental and Climate Change Canada
Kurzus: Foundation R
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A tréner, az élelmiszer-, és a tér mind nagy.
Canada Revenue Agency
Kurzus: R
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Nekem tetszik valójában írás kód-val minta adat és magyarázó jegyzet a kézírás a jövő számára ajánlás.
Canada Revenue Agency
Kurzus: R
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Az előre elkészített scriptek használt képzési anyag nagyon hasznos. Az interaktív tréning lehetővé tette a világos megértését az egyes témának.
Canada Revenue Agency
Kurzus: R
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R Language Subcategories
R Language Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
21 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at data analysts who wish to program in R for Excel.
By the end of this training, participants will be able to:
- Toggle and move data between Excel and R.
- Use R Tidyverse and R features for data analytic solutions in Excel.
- Extend their data analytical skills by learning R.
By the end of this training, participants will be able to:
- Toggle and move data between Excel and R.
- Use R Tidyverse and R features for data analytic solutions in Excel.
- Extend their data analytical skills by learning 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.
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
This course is an introduction to applying neural networks in real world problems using R-project software.
7 hours
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
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
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
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
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
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
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
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
[R](https://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
This course is part of the Data Scientist skill set (Domain: Data and Technology)
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.
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.
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.
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 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
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
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
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
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
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
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
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
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
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
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
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
The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.
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Upcoming R Language Courses
Machine Learning Fundamentals with R
07-02-2022 09:30
14 hours
R Fundamentals
07-02-2022 09:30
21 hours
Building Web Applications in R with Shiny
17-02-2022 09:30
7 hours
R
09-03-2022 09:30
21 hours
Programming with Big Data in R
30-03-2022 09:30
21 hours
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