VA, Stafford - Quantico Corporate

800 Corporate Drive, Suite 301, Stafford, united states, 22554

The venue is located between interstate 95 and the Jefferson Davis Highway, in the vicinity of the Courtyard by Mario...

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Tidyverse

Explore Our Courses

Data Analytics with Tableau, Python, R, and SQL

 35 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data analysts and anyone who is interested to learn how to use and integrate Tableau, Python, R, and SQL for data visualization and analysis.By the end of this training, participants will be able to:
  • Perform data analysis using Python, R, and SQL.
  • Create insights through data visualization with Tableau.
  • Make data-driven decisions for business operations.
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Data Analytics With R

 21 Hours
R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students.  It covers language fundamentals, libraries and advanced concepts.  Advanced data analytics and graphing with real world data. Audience Developers / data analytics Duration 3 days Format Lectures and Hands-on
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Data and Analytics - from the ground up

42 Hours
Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions: What has happened?
  • processing and analyzing data
  • producing informative data visualizations
What will happen?
  • forecasting future performance
  • evaluating forecasts
What should happen?
  • turning data into evidence-based business decisions
  • optimizing processes
The course itself can be delivered either as a 6 day classroom course or remotely over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
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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 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
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R Programming for Excel

 21 Hours
This instructor-led, live training in Virginia (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.
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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.
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Advanced R

 14 Hours
This course covers advanced topics in R programming.
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Algorithmic Trading with Python and R

 14 Hours
This instructor-led, live training in Virginia (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.
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Anomaly Detection with Python and R

 14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists and data analysts who wish to program in R and Python for outlier detection. By the end of this training, participants will be able to:
  • Identify whether data is an anomaly or is an expected value.
  • Implement algorithms for anomaly detection.
  • Use various techniques and methods to detect anomalies.
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Programming with Big Data in R

 21 Hours
Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.
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Cluster Analysis with R and SAS

 14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis. By the end of this training, participants will be able to:
  • Use cluster analysis for data mining
  • Master R syntax for clustering solutions.
  • Implement hierarchical and non-hierarchical clustering.
  • Make data-driven decisions to help to improve business operations.
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Data Mining with R

 14 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
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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
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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
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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.
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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.
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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.
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KNIME with Python and R for Machine Learning

 14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.By the end of this training, participants will be able to:
  • Plan, build, and deploy machine learning models in KNIME.
  • Make data driven decisions for operations.
  • Implement end to end data science projects.
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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
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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
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Marketing Analytics using 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.
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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.
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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:
  • 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.
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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.
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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
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R

 21 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.
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Neural Network in R

 14 Hours
This course is an introduction to applying neural networks in real world problems using R-project software.
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Advanced R Programming

 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
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R Programming for Data Analysis

 14 Hours
This course is part of the Data Scientist skill set (Domain: Data and Technology)
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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
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Statistical Analysis with Stata and R

 35 Hours
This instructor-led, live training in (online or onsite) is aimed at data analysts who wish to use Stata and R to analyze big data for statistical analysis.By the end of this training, participants will be able to:
  • Create statistic models for predicting key interest variables and events.
  • Generate descriptive visualizations, summary tables, frequencies, and more.
  • Manage and structure large databases to preapare for data analysis.
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Data Science with Tableau and R Programming

 14 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at data scientists who wish to program in R for Tableau. By the end of this training, participants will be able to:
  • Implement Tableau analytics with R.
  • Return values to Tableau with learning algorithms in R.
  • Structure and visualize R functions in Tableau.
  • Make data driven decisions for business operations.
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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.
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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
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Forecasting with R

 14 Hours
This course allows delegate to fully automate the process of forecasting with R
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R for Data Analysis and Research

7 Hours
Audience
  • managers
  • developers
  • scientists
  • students
Format of the course on-line instruction and discussion OR face-to-face workshops
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Introduction to R

 21 Hours
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (https://www.r-project.org) both on a command line and in a graphical user interface (GUI).
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Testimonials (16)

Many examples and exercises related to the topic of the training.

Tomasz - Ministerstwo Zdrowia

Course - Advanced R Programming

I genuinely enjoyed the hands passed exercises.

Yunfa Zhu - Environmental and Climate Change Canada

Course - Foundation R

The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.

Rhian Hughes - Public Health Wales NHS Trust

Course - Introduction to Data Visualization with Tidyverse and R

the matter was well presented and in an orderly manner.

Marylin Houle - Ivanhoe Cambridge

Course - Introduction to R with Time Series Analysis

It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.

Sonja Steiner - BearingPoint GmbH

Course - R Programming for Data Analysis

I really enjoyed the knowledge of the trainer.

Stephanie Seiermann

Course - R

Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.

Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada

Course - Programming with Big Data in R

A lot of knowledge - theoretical and practical.

Anna Alechno

Course - Forecasting with R

I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.

Luke Pontin

Course - Data and Analytics - from the ground up

The flexible and friendly style. Learning exactly what was useful and relevant for me.

Jenny

Course - Advanced R

I liked the new insights in deep machine learning.

Josip Arneric

Course - Neural Network in R

I genuinely enjoyed working 1:1 with Gunner.

Bryant Ives

Course - Introduction to R

Very tailored to needs.

Yashan Wang

Course - Data Mining with R

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama

Course - Data Mining & Machine Learning with R

I get answers on all my questions.

Natalia Gladii

Course - Data Analytics With R

He was very informative and helpful.

Pratheep Ravy

Course - Predictive Modelling with R

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