Local, instructor-led live Statistics training courses demonstrate through interactive discussion and hands-on practice how to apply Statistic principles to the solving of real-world problems.
Statistics training is available as "onsite live training" or "remote live training". Onsite live Statistics trainings in Lithuania can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Very clear and information packed course, would definitely recommend it.
Elsa Mitchell - Babcock International
Course: Excel For Statistical Data Analysis
He (the trainer) is excellent. Very patient, can listen well to our questions. We liked his patience and pace.
Ellen Lentz- Genentech Inc
Course: Introduction to R
I really liked the hands-on learning by doing.
Guihong Chen - TCF
Course: Introduction to R
Trainer was flexible -- he tried to address our issues. The course was well organized and the trainer did have the breadth of knowledge, both in the R program and finance industry, necessary to help us.
George Rezek - TCF
Course: Introduction to R
I really appreciated that he (the trainer) was a practitioner and had that perspective.
Bradley Olson - TCF
Course: Introduction to R
The training was perfect. The trainer was very good and was very capable to deliver the information, share his knowledge and use different methods to explain difficult statistical methodologies, he is very knowledgeable and kind person. I am very happy that I attended such course and looking forward to joining more courses in the future.
Sabha Al Ameri - Abu Dhabi Tourism & Cultural Authority
Course: Statistics Level 2
A lot of exercises, good explanation.
Katarzyna Rompala - UBS Kraków Sp. z o.o.
Course: Excel For Statistical Data Analysis
Ref material to use later was very good.
PAUL BEALES- Seagate Technology.
Course: Applied Machine Learning
He (the trainer) was excellent. I liked the pace and the focus on principles at the beginning, not skipping over the detail.
Geoff Copps - Mediabrands
Course: Introduction to R
What did you like the most about the training?: Trainer attitude and knowledge.
Maria Ożóg - Stefko - Amway Business Center Europe
Course: Excel For Statistical Data Analysis
What did you like the most about the training?: How easily he explains and all the exercises.
JOanna Tomzik - Amway Business Center Europe
Course: Excel For Statistical Data Analysis
What did you like the most about the training?: A lot of examples and exercises.
Agnieszka Krasuska - Citibank Europe PLC
Course: Excel For Statistical Data Analysis
What did you like the most about the training?: The examples given for each excel function and also many useful shortcuts.
Izabela Niziołek - Citibank Europe PLC
Course: Excel For Statistical Data Analysis
What did you like the most about the training?: Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away. This was an excellent course. One of the best I have had.
Matthew Thomas - British Telecom
Course: Applied Machine Learning
What did you like the most about the training?: Able to do hands-on and get help from trainer on how to go about working on difficult questions.
Faeiza Ab Rahman - Seagate Singapore International Headquarters Pte. Ltd
Course: Introduction to R
What did you like the most about the training?: Trainer's subject knowledge was such that complex subjects seemed easy, and he genuinely inspired me with enthusiasm for the topic. My session worked out as 1:1, and it was a real privilege to get the benefit of such a high caliber of trainer in the context of personal tuition.
Harry Frost - Babcock International
Course: Market Forecasting
I genuinely enjoyed the trainer's helping.
Urszula Kuza
Course: Tableau Advanced
I get answers on all my questions.
Natalia Gladii
Course: Data Analytics With R
The training was adaptable and personalized to our needs.
Dominique Soulie
Course: Minitab for Statistical Data Analysis
I liked the exercises as it's the only way to learn, by repetition.
David Rushe
Course: Tableau Advanced
I genuinely enjoyed working 1:1 with Gunner.
Bryant Ives
Course: Introduction to R
I like the exercises done.
Nour Assaf
Course: Data Mining and Analysis
The hands-on exercise and the trainer capacity to explain complex topics in simple terms.
youssef chamoun
Course: Data Mining and Analysis
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course: Data Mining and Analysis
I mostly liked the trainer giving real live Examples.
Simon Hahn
Course: Administrator Training for Apache Hadoop
I genuinely enjoyed the big competences of Trainer.
Grzegorz Gorski
Course: Administrator Training for Apache Hadoop
I genuinely enjoyed the many hands-on sessions.
Jacek Pieczątka
Course: Administrator Training for Apache Hadoop
I genuinely was benefit from the flexibility of the trainer.
Irina Ostapenko
Course: Statistics Level 2
A lot of knowledge - theoretical and practical.
Anna Alechno
Course: Forecasting with R
I genuinely liked his knowledge and practical examples.
Irina Tulgara
Course: Forecasting with R
Overview and understanding how big the topic is.
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Course: Forecasting with R
Hands on examples were the most helpful.
Sean Kaukas
Course: Introduction to R
The practical exercises were extremely beneficial.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
The use of examples, though even these were demonstrated at some considerable speed.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
I enjoyed the 2nd day we did lots of examples of gauge R&R.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
I genuinely liked the exercises - use of Minicab.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
Good overview of R and good range of topics. Trainer was happy to answer all questions.
Symphony EYC
Course: R
I really enjoyed the knowledge of the trainer.
Stephanie Seiermann
Course: R
It was very hands-on, we spent half the time actually doing things in Clouded/Hardtop, running different commands, checking the system, and so on. The extra materials (books, websites, etc. .) were really appreciated, we will have to continue to learn. The installations were quite fun, and very handy, the cluster setup from scratch was really good.
Ericsson
Course: Administrator Training for Apache Hadoop
I really liked the exercises on time series modeling.
Teleperformance
Course: Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
Michael Lopez - Teleperformance
Course: Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
Teleperformance
Course: Data Analytics With R
The training felt very personal and customised to what I wanted out of it. It was so non judgemental that I felt comfortable in asking whatever questions I needed to, and the trainer was able to competently answer all my questions. It was a very good overview of useful statistical techniques and there was a good balance between statistical theory and how to use minitab.
Cambridge Consultants
Course: Minitab for Statistical Data Analysis
Lot of hands-on exercises.
Ericsson
Course: Administrator Training for Apache Hadoop
Ambari management tool. Ability to discuss practical Hadoop experiences from other business case than telecom.
Ericsson
Course: Administrator Training for Apache Hadoop
I generally liked the trainer Knowledge.
SAP Business Objects
Course: Statistics Level 1
I mostly was benefit from learning about Gantt charts..
Sarah Drummond - Siemens Gamesa c/o Hemsley Fraser
Course: Tableau Advanced
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course: Introduction to R with Time Series Analysis
The fact that we had the time to cover some useful extras..
Alina Vishniakova - TUI Business Services GmbH
Course: Statistics Level 1
He really explained everything well and used examples.
royal bank of Canada
Course: R
| Code | Name | Duration | Overview |
|---|---|---|---|
| foundr | Foundation R | 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. |
| scilab | Scilab | 14 hours | Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems. In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing. By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge. Audience - Data scientists and engineers, especially with interest in image processing and facial recognition Format of the course - Part lecture, part discussion, exercises and intensive hands-on practice, with a final project |
| dmmlr | Data Mining & Machine Learning with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| predmodr | Predictive Modelling with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| spssanal | Statistical Analysis using SPSS | 21 hours | SPSS is software for editing and analyzing data. |
| intror | Introduction to R with Time Series Analysis | 21 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| tbladv | Tableau Advanced | 14 hours | Tableau helps people see and understand data. |
| datavisR1 | Introduction to Data Visualization with R | 28 hours | This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions |
| advr | Advanced R | 7 hours | This course covers advanced topics in R programming. |
| nlpwithr | NLP: Natural Language Processing with R | 21 hours | It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data. This course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements. By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance. Audience Linguists and programmers Format of the course Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding |
| mathematica1 | Mathematica - Elementary Programming, Visualizations and Data Presentation | 14 hours | Mathematica consists of 20 years of creating the most powerful specialized mathematical software engine in the world. Its versatility makes it useful not only for doing basic academic calculations but also completing complicated calculations, like programming or numerical data presentations. Mathematica integrates software engines doing numerical andsymbolic computation, as well as graph analysis software, programming language, document formats and the possibility of publishing your work results. Thanks to multiplicity of its functions it’s a priceless tool for mathematicians, physicists, biologists, chemists, financial analysts, sociologists and many more professions that deal with data. Participants will gain skills to - perform calculations efficiently - understanding program commands - creating text documents - building charts and graphs - data presentations |
| danagr | Data and Analytics - from the ground up | 42 hours | Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: What has happened? - processing and analyzing data - producing informative data visualizations What will happen? - forecasting future performance - evaluating forecasts What should happen? - turning data into evidence-based business decisions - optimizing processes The course itself can be delivered either as a 6 day classroom course or [remotely](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. |
| radvml | Advanced Machine Learning with R | 21 hours | In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application. By the end of this training, participants will be able to: - Use techniques as hyper-parameter tuning and deep learning - Understand and implement unsupervised learning techniques - Put a model into production for use in a larger application Audience - Developers - Analysts - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dataar | Data Analytics With R | 21 hours | [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 |
| tidyverse | Introduction to Data Visualization with Tidyverse and R | 7 hours | The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble. In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse. By the end of this training, participants will be able to: - Perform data analysis and create appealing visualizations - Draw useful conclusions from various datasets of sample data - Filter, sort and summarize data to answer exploratory questions - Turn processed data into informative line plots, bar plots, histograms - Import and filter data from diverse data sources, including Excel, CSV, and SPSS files Audience - Beginners to the R language - Beginners to data analysis and data visualization Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlbankingr | Machine Learning for Banking (with R) | 28 hours | In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects. Audience - Developers - Data scientists - Banking professionals with a technical background Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| rforfinance | R Programming for Finance | 28 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems. By the end of this training, participants will be able to: - Understand the fundamentals of the R programming language - Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.) - Build applications that solve problems related to asset allocation, risk analysis, investment performance and more - Troubleshoot, integrate deploy and optimize an R application Audience - Developers - Analysts - Quants Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange. |
| rintrofinance | Introduction to R for Finance | 21 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn 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 |
| rfintrading | Financial Trading with R | 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 |
| intermediaterforfinance | Intermediate R for Finance | 21 hours | R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples. By the end of this training, participants will be able to: - Implement advanced R programming techniques - Use R to manipulate their data to perform more advanced financial operations Audience - Programmers - Finance professionals - IT Professionals Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| mlfinancer | Machine Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: - Understand the fundamental concepts in machine learning - Learn the applications and uses of machine learning in finance - Develop their own algorithmic trading strategy using machine learning with R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlfinancewithr | Deep Learning for Finance (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use R to create deep learning models for finance - Build their own deep learning stock price prediction model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithr | Deep Learning for Banking (with R) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use R to create deep learning models for banking - Build their own deep learning credit risk model using R Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| shinyrhtml | Shiny, R and HTML: Merging Data Science and Web Development | 7 hours | Shiny is an open source R package that provides a web framework for building interactive web applications using R. In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML. By the end of this training, participants will be able to: - Build interactive web applications with R using Shiny Audience - Data scientists - Web developers - Statisticians Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| rsttatan | R for Statistical Analysis | 14 hours | Organization – Lab work and practical examples on real data |
| rprogda | R Programming for Data Analysis | 14 hours | This course is part of the Data Scientist skill set (Domain: Data and Technology) |
| rintrob | Introductory R for Biologists | 28 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. |
| excelafd | Analysing Financial Data in Excel | 14 hours | Audience Financial or market analysts, managers, accountants Course Objectives Facilitate and automate all kinds of financial analysis with Microsoft Excel |
| webappsr | Building Web Applications in R with Shiny | 7 hours | Description: This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS. Objective: Covers the basics of how Shiny apps work. Covers all commonly used input/output/rendering/paneling functions from the Shiny library. |
| excelstatsda | Excel For Statistical Data Analysis | 14 hours | Audience Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel. Course Objectives This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research. This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them. |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Excel For Statistical Data Analysis - Kaunas, City Center | Thu, 2019-05-16 09:30 | 3000EUR / 3600EUR |
| Excel For Statistical Data Analysis - Kaunas, City Center | Mon, 2019-06-10 09:30 | 3000EUR / 3600EUR |
| Excel For Statistical Data Analysis - Kaunas, City Center | Tue, 2019-06-11 09:30 | 3000EUR / 3600EUR |
| Excel For Statistical Data Analysis - Kaunas, City Center | Wed, 2019-06-12 09:30 | 3000EUR / 3600EUR |
| Excel For Statistical Data Analysis - Kaunas, City Center | Thu, 2019-06-13 09:30 | 3000EUR / 3600EUR |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Combined C/C++/C#, ASP.NET and Web Application Security | Kaunas, City Center | Mon, 2019-05-13 09:30 | 6300EUR / 7300EUR |
| Simulink® for Automotive System Design Advanced Level | Kaunas, City Center | Tue, 2019-06-11 09:30 | 3150EUR / 3750EUR |
| OpenSpan: Robotic Automation and Intelligence | Kaunas, City Center | Tue, 2019-06-11 09:30 | 6300EUR / 7300EUR |
| Business Process Modeling using BPMN and UML | Kaunas, City Center | Tue, 2019-07-02 09:30 | 3150EUR / 3750EUR |
| Mastering Deeplearning4j | Kaunas, City Center | Wed, 2019-07-03 09:30 | 4725EUR / 5525EUR |
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.jpg "abu dhabi company for onshore oil operations (adco)")
.jpg "european southern observatory (eso)")
.jpg "aeronautical radio, incorporated (arinc)")
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