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Intro to Data Visualization with R & ggplot2
 
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The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code. In this webinar Dave Langer will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business. Attendees will learn how to: • Craft ggplot visualizations, including customization of rendered output. • Choose optimal visualizations for the type of data and the nature of the analysis at hand. • Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”. • Export ggplot2 visualizations from RStudio for use in documents and presentations. Github: https://github.com/datasciencedojo/IntroDataVisualizationWithRAndGgplot2 -- Learn more about Data Science Dojo here: https://hubs.ly/H0dTtFq0 See what our past attendees are saying here: https://hubs.ly/H0dTtFw0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 85969 Data Science Dojo
Data Visualization Using R
 
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How to create histograms, density plots, boxplots, box and whisker plots, scatterplots, scatterplots matrices, fancy scatterplot matrices, conditional scatterplots, dotplots, qqplots, and access plot components.
Views: 36308 How To R
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 858177 David Langer
Data Manipulation and Visualization with R | Examples using dplyr & ggplot2 Packages
 
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Data Manipulation and Visualization with R R File link: https://goo.gl/nRmkwr Data file: https://goo.gl/UMYMZR For fiftystater and colorplaner, run following lines: devtools::install_github("wmurphyrd/fiftystater") devtools::install_github("wmurphyrd/colorplaner") R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 4984 Bharatendra Rai
ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This "ggplot2 Tutorial" by Edureka is a comprehensive session on the ggplot2 in R. This tutorial will not only get you started with the ggplot2 package, but also make you an expert in visualizing data with the help of this package. This tutorial will comprise of these topics: 1) Base R Graphics 2) Grammar of Graphics 3) GGPLOT2 package Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #ggplot2 #ggplotinr How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 25051 edureka!
R Tutorial: Data Visualization in R
 
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Introduction video to Data Visualization in R course by Ron Pearson. Learn more about the course here: https://www.datacamp.com/courses/data-visualization-in-r R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset. Take Ron's course here: https://www.datacamp.com/courses/data-visualization-in-r
Views: 5414 DataCamp
Data Visualization In R | Data Science Tutorial | Simplilearn
 
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This video will teach you how to visualise your data using. R has several systems for making graphs, but ggplot2 is one of the most elegant and most versatile. It implements the grammar of graphics, a coherent system for describing and building graphs. With this, you can do more faster by learning one system and applying it in many places. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Visualization-_WyUme_H2ZQ&utm_medium=SC&utm_source=youtube What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 2144 Simplilearn
Basic Data Analysis in RStudio
 
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This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4
Views: 117241 Ralf Becker
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming in which I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
Data visualization in R | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) The nuances in data can be both analyzed and visualized using R language. R helps in creating data visualizations that provide insights about the data metrics. Following are the contents that have been covered in this video: 1.Data Visualization 2.Library (Corrgram) 3.Graphs in R 4.Spatial Analysis 5.Spatial Analysis: Example 6.Spatial Analysis using Deducer GUI 7.Data Visualization: Facets 8.Interactive Graphs in R (Demo) 9.Tableau Software – Public Related Posts: http://www.edureka.co/blog/core-data-scientist-skills/ http://www.edureka.co/blog/r-for-marketing-professionals/ Edureka is a New Age e-learning platform that provides Instructor-Led Live Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics, related to Data Visualization, have been widely covered in our course ‘Business Analytics with R’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 14207 edureka!
Data Visualization Tutorial For Beginners | Big Data Analytics Tutorial | Simplilearn
 
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This Data Visualization Tutorial will start by explain what Data Visualization is, Why we use Data Visualization, major considerations for Data Visualization and the basics of different types of graphs. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Visualization-MiiANxRHSv4&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Hadoop, check our Big Data Hadoop and Spark Developer Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training?utm_campaign=BigData-Visualization-MiiANxRHSv4&utm_medium=Tutorials&utm_source=youtube #bigdata #bigdatatutorialforbeginners #bigdataanalytics #bigdatahadooptutorialforbeginners #bigdatacertification #HadoopTutorial - - - - - - - - - About Simplilearn's Big Data and Hadoop Certification Training Course: The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form. As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E-commerce. This Big Data course also prepares you for the Cloudera CCA175 certification. - - - - - - - - What are the course objectives of this Big Data and Hadoop Certification Training Course? This course will enable you to: 1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames - - - - - - - - - - - Who should take up this Big Data and Hadoop Certification Training Course? Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals: 1. Software Developers and Architects 2. Analytics Professionals 3. Senior IT professionals 4. Testing and Mainframe professionals 5. Data Management Professionals 6. Business Intelligence Professionals 7. Project Managers 8. Aspiring Data Scientists - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 2053 Simplilearn
Data Visualization in R Part-1
 
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https://sites.google.com/site/raibharatendra/home/data-visualization Shows how to make graphs and charts in R. Examples include histogram, pie chart, bar chart, scatter plot, box plot, etc. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 6709 Bharatendra Rai
Dashboards with R | Interactive Data Visualization including  Plots, Data Tables and Pivot Charts
 
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Interactive Dashboards with multiple pages in R. Data File: https://goo.gl/7PLsYx R Markdown File: https://goo.gl/a1bHYK Includes, - Interactive plots - Interactive data tables - Interactive pivot tables - Interactive geographic plots R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 6242 Bharatendra Rai
Vinayak Hegde - Data analysis and Visualization using R
 
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Vinayak gives a brief talk about what he is going to cover at this workshop for fifthelephant.in. The Fifth Elephant is a conference on Big Data organized by HasGeek. http://hasgeek.com/
Views: 95 HasGeek TV
How to Perform K-Means Clustering in R Statistical Computing
 
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In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 185931 Influxity
Getting Started with Spatial Data Analysis in R
 
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Spatial and spatial-temporal data have become pervasive nowadays. We are constantly generating spatial data from route planners, sensors, mobile devices, and computers in different fields like Transportation, Agriculture, Social Media. These data need to be analyzed to generate hidden insights that can improve business processes, help fight crime in cities, and much more. Simply creating static maps from these data is not enough. In this webinar we shall look at techniques of importing and exporting spatial data into R; understanding the foundation classes for spatial data; manipulation of spatial data; and techniques for spatial visualization. This webinar is meant to give you introductory knowledge of spatial data analysis in R needed to understand more complex spatial data modeling techniques. In this webinar, we will cover the following topics: -Why use R for spatial analysis -Packages for spatial data analysis -Types of spatial data -Classes and methods in R for spatial data analysis -Importing and exporting spatial data -Visualizing spatial data in R
Views: 42684 Domino Data Lab
Basic Data Visualisation Techniques
 
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Learn basic data visualization techniques in this tutorials. For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected] Find all free videos & study packs available with us here: http://analyticsuniversityblog.blogspot.in/ SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop
Views: 10555 Analytics University
Ashton Drew  | Interactive Data Visualization Tools in R
 
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PyData Carolinas 2016 An introduction to the R packages that generate interactive table and graphics. The focus will be on R Shiny, but I will also summarize value of other options, such as leaflet, plotly, ggvis, and rCharts. With interactive and reactive data visualizations, your audience directly engages with your data for stronger communication and better understanding. The Shiny apps can easily be launched directly to the web via shinyapps.io or Shiny Server to be run by anyone (they don't need to download your data or have R). This course assumes participants can already perform data analysis and visualization in R, but want to expand their skills with R Shiny. Therefore, the class exercises focus on transcribing code from a static to an interactive presentation of data products and information. Students must bring their own laptop with a current version of R and R Studio. Outline: - Overview of available interactive tools in R - Exercise to build a simple Shiny app - Shiny setup and orientation - Introduction to Shiny code structure - Overview of basic widgets - Example of conditional reactivity
Views: 3631 PyData
Data Exploration and Visualization in R
 
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Data Exploration and Visualization in R
Views: 388 Chuc Nguyen Van
Basic Analytical Techniques | Data Science With R Tutorial
 
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Basic Analytical Techniques Using R tools. After completing this course you will be able to: Watch the New Upgraded Video: https://www.youtube.com/watch?v=_WyUme_H2ZQ 1. Get a basic introduction to R 2. Understand exploration of data 3. Explore data using R 4. Visualize data using R 5. Understand diagnostic analytics 6. Implementing diagnostic analytics using R 7. Understand these concepts with the help of case studies Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=R-Language-Training-rqrrTfy-z-c&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 93241 Simplilearn
Data Visualization with R by Matthew Renze
 
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R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating professional data visualizations. This session will provide an introduction to the R programming language using RStudio. In addition, we will demonstrate how we can use R to create data visualizations to transform our data into actionable insight. Matthew is a data science consultant with over 17 years of professional experience. He is an international public speaker, an author for Pluralsight, a Microsoft MVP, an ASPInsider, and an open-source software contributor. His interests include data analytics, data visualization, and machine learning.
Views: 687 Devoxx
ggplot: Visualization in R
 
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Stanford SCI01: Introduction to Data Science
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 50005 edureka!
Data Visualization and R, part 7, Categorizing and Clustering
 
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Ryan Womack, Data Librarian Rutgers University http://ryanwomack.com twitter: @ryandata https://github.com/ryandata/DataViz Screencast version of a workshop at Rutgers University. This section presents some examples of clustering methods (tree plots, dendrograms) and the mosaic plot for displaying categorical data.
Views: 1077 librarianwomack
Jeffrey Heer - Interactive Data Analysis: Visualization and Beyond
 
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Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, my talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 3737 PyData
Introduction to Data Visualization with ggplot2
 
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The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality visualizations – where any visualization can be heavily customized with a minimal amount of code. In this webinar Arham Akheel will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business. Attendees will learn how to: • Craft ggplot visualizations, including customization of rendered output. • Choose optimal visualizations for the type of data and the nature of the analysis at hand. • Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”. • Export ggplot2 visualizations from RStudio for use in documents and presentations. Materials: https://github.com/datasciencedojo/tutorials Software: https://www.rstudio.com/products/rstudio/download2/ Dataset: https://www.kaggle.com/c/titanic/data About the Presenter: Arham holds a Masters degree in Technology Management from Texas A&M University and has a background of managing information systems. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8rCM0 See what our past attendees are saying here: https://hubs.ly/H0f8rD20 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 1748 Data Science Dojo
Bioconductor Workshop 1: R/Bioconductor Workshop for Genomic Data Analysis
 
04:29:57
The Computational Biology Core (CBC) at Brown University (supported by the COBRE Center for Computational Biology of Human Disease) and R/Bioconductor Staff team up to provide training on analysis, annotation, and visualization of Next Generation Sequencing (NGS) data. For more info: https://www.brown.edu/academics/computational-molecular-biology/bioconductor-workshop-1-rbioconductor-workshop-genomic-data-analysis Wednesday, February 7th 2018 Brown University
Views: 466 Brown University
R Stats: Data Prep and Imputation of Missing Values
 
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This video demonstrates how to prepare data for use with the Naive Bayes classifier and its cross-validation. It focuses primarily on the selection of suitable variables from a large data set and imputation of missing values. The video also explains the use of Spearman rank correlation for ordinal variables, where the traditional Pearson correlation is not applicable. The lesson is quite informal and avoids more complex statistical concepts. The data for this lesson can be obtained from the UCI Machine Learning Repository: * https://archive.ics.uci.edu/ml/datasets/wiki4he The R source code for this video can be found (some small discrepancies are possible): * http://visanalytics.org/youtube-rsrc/r-stats/Demo-B3-Imputing-Missing-Values.r Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 14786 ironfrown
Data Visualization in R  Part-2
 
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Visualization using R includes examples of, - Multiple plots - Color coded scatter plots - Motion charts (googleVis package) - Geographic chart - World (googleVis package) - Geographic chart - States (googleVis package) - Network diagram - Interactive pivot chart Visualizing data is an important tool related to analyzing big data or data science. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 4376 Bharatendra Rai
Analysis and Visualization of NYC Taxi Trip Data
 
07:50
EECSE6893_001_2015_3 Big Data Analytics Xianglu Kong, Junfei Shen, Guochen Jing
Views: 1255 Xianglu Kong
4.2. Reading Data (Exploratory Data Analysis with data.table)
 
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See here for the course website, including a transcript of the code and an interactive quiz for this segment: http://dgrtwo.github.io/RData/lessons/lesson4/segment2/
R Studio: Importing & Analyzing Data
 
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Tutorial on importing data into R Studio and methods of analyzing data.
Views: 156384 MrClean1796
Pathview: an R/Bioconductor Package for Pathway-based Data Integration and Visualization
 
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Weijun Luo http://video.open-bio.org/video/24/pathview-an-rbioconductor-package-for-pathway-b
Views: 3736 Next Day Video
Statistics Essentials for Analytics | R Statistics | Statistics for Data Science Training | Edureka
 
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***** Statistics for Data Science - https://www.edureka.co/data-science ***** This Edureka video will provide you with a detailed introduction to data and statistics involved in Data Analysis. It will also provide you with detailed knowledge of Analytics to work with Entropy, Deviation, Range, Gains, Sensitivity and other statistical terms. ----------------------- About the Course A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real-world data set to analyze and conclude insights. Statistics and its methods are the backend of Data Science to "understand, analyze and predict actual phenomena". Machine learning employs different techniques and theories drawn from statistical & probabilistic fields. ----------------------- Course Objective After completing this Google Cloud Certification training, you should be able to : Understanding the Data Probability and its uses Statistical Inference Data Clustering Testing the Data Regression Modelling ----------------------------------------------------------------- Please write back to us at [email protected] or call us at +91 88808 62004 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 5113 edureka!
4.6 Exploratory Data Analysis (Exploratory Data Analysis with data.table)
 
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See here for the course website, including a transcript of the code and an interactive quiz for this segment: http://dgrtwo.github.io/RData/lessons/lesson4/segment6/
ChIP-Seq Analysis and Visualization
 
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ChIP-Seq Analysis and Visualization Using Galaxy and IGB
Views: 9241 IGB Channel
R Markdown for a Data Analysis Report
 
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Guide for my students on producing data analysis reports using R Markdown in the R Studio IDE.
Views: 14833 Homer White
Data Analysis with MATLAB for Excel Users
 
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This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Views: 227774 MATLAB
Introduction to Text Analytics with R: Overview
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data is far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 1 of this video series provides an introduction to the video series and includes specific coverage: - Overview of the spam dataset used throughout the series - Loading the data and initial data cleaning - Some initial data analysis, feature engineering, and data visualization Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/tu... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 56540 Data Science Dojo
Metabolomic Data Analysis Using MetaboAnalyst
 
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This is the fifth module in the 2016 Informatics and Statistics for Metabolomics workshop hosted by the Canadian Bioinformatics Workshops. This lecture is by David Wishart from the University of Alberta. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/ Table of Contents: 00:10 - 00:56 - Learning Objectives 01:15 - A Typical Metabolomics Experiment 03:16 - 2 Routes to Metabolomics 03:31 - Metabolomics Data Workflow 05:11 - Data Integrity/Quality 06:46 - Data/Spectral Alignment 07:28 - Binning (3000 pts to 14 bins) 07:53 - Data Normalization/Scaling 09:40 - Data Normalization/Scaling 10:20 - Data QC, Outlier Removal & Data Reduction 12:08 - MetaboAnalyst 13:25 - MetaboAnalyst History 14:37 - MetaboAnalyst Overview 16:00 - MetaboAnalyst Modules 17:00 - MetaboAnalyst Modules 18:03 - Example Datasets 19:09 - Example Datasets 19:34 - Metabolomic Data Processing 19:44 - Example Datasets 19:45 - Example Datasets 19:45 - MetaboAnalyst Modules 19:48 - Example Datasets 19:48 - Example Datasets 19:49 - Metabolomic Data Processing 19:51 - Common Tasks 20:38 - Select a Module (Statistical Analysis) 20:48 - Common Tasks 20:49 - Metabolomic Data Processing 20:49 - Example Datasets 20:49 - Example Datasets 20:55 - Example Datasets 20:55 - Metabolomic Data Processing 20:56 - Common Tasks 20:56 - Select a Module (Statistical Analysis) 21:07 - Data Upload 21:37 - Alternatively … 22:18 - Data Set Selected 23:24 - Data Integrity Check 24:11 - Data Normalization 25:51 - Data Normalization 26:28 - Data Normalization 27:40 - Normalization Result 29:49 - Data Normalization 29:59 - Normalization Result 30:03 - Data Normalization 30:14 - Normalization Result 30:15 - Data Normalization 30:15 - Data Normalization 30:16 - Data Normalization 30:21 - Data Normalization 30:21 - Data Normalization 30:36 - Normalization Result 31:24 - Data Normalization 31:26 - Next Steps 31:37 - Quality Control 32:41 - Visual Inspection 33:36 - Outlier Removal (Data Editor) 34:01 - Noise Reduction (Data Filtering) 35:09 - Noise Reduction (cont.) 35:36 - Data Reduction and Statistical Analysis 36:07 - Common Tasks 36:30 - 37:00 - ANOVA 37:31 - ANOVA 38:58 - View Individual Compounds 39:46 - What’s Next? 40:06 - Overall Correlation Pattern 41:20 - High Resolution Image 41:53 - What’s Next? 42:40 - Pattern Matching 43:29 - Pattern Matching (cont.) 44:57 - 45:16 - Pattern Matching (cont.) 45:17 - Pattern Matching 45:32 - Pattern Matching (cont.) 45:34 - 45:55 - Multivariate Analysis 46:58 - PCA Scores Plot 47:40 - PCA Loading Plot 47:58 - PCA Scores Plot 48:15 - PCA Loading Plot 49:14 - 3D Score Plot 50:28 - 51:06 - 3D Score Plot 51:07 - PCA Loading Plot 51:08 - PCA Scores Plot 51:46 - PCA Loading Plot 51:47 - 3D Score Plot 51:49 - 52:30 - Multivariate Analysis 52:54 - PLS-DA Score Plot 53:23 - Evaluation of PLS-DA Model 55:19 - Important Compounds 57:07 - Model Validation 58:07 - 58:16 - Hierarchical Clustering (Heat Maps) 58:34 - Heatmap Visualization 58:48 - Heatmap Visualization (cont.) 59:16 - What’s Next? 59:28 - Heatmap Visualization (cont.) 59:29 - Heatmap Visualization 59:32 - Heatmap Visualization (cont.) 01:00:18 - What’s Next? 01:02:45 - Download Results 01:02:56 - Analysis Report 01:03:21 - Select a Module (Enrichment Analysis) 01:03:29 - Metabolite Set Enrichment Analysis (MSEA) 01:04:18 - Enrichment Analysis 01:04:56 - MSEA 01:05:09 - The MSEA Approach 01:05:18 - Data Set Selected 01:06:02 - Start with a Compound List for ORA 01:06:10 - Upload Compound List 01:06:38 - Perform Compound Name Standardization 01:06:55 - Name Standardization (cont.) 01:07:11 - Select a Metabolite Set Library 01:07:46 - Result 01:08:39 - Result (cont.) 01:09:04 - The Matched Metabolite Set 01:09:18 - Phenylalanine and Tyrosine Metabolism in SMPDB 01:09:42 - Single Sample Profiling (SSP) (Basically used by a physician to analyze a patient) 01:10:14 - Concentration Comparison 01:10:34 - Concentration Comparison (cont.) 01:11:13 - Quantitative Enrichment Analysis (QEA) 01:11:30 - Result 01:11:47 - The Matched Metabolite Set 01:12:02 - Select a Module (Pathway Analysis) 01:12:09 - Pathway Analysis 01:12:57 - Data Set Selected 01:13:03 - Pathway Analysis Module 01:13:09 - Data Upload 01:13:22 - Perform Data Normalization 01:13:38 - Select Pathway Libraries 01:13:54 - Perform Network Topology Analysis 01:14:04 - Pathway Position Matters 01:14:44 - Which Node is More Important? 01:14:54 - Pathway Position Matters 01:14:56 - Which Node is More Important? 01:15:01 - Pathway Visualization 01:17:15 - Pathway Visualization (cont.) 01:17:28 - Pathway Impact 01:17:43 - Result 01:17:54 - Select a Module (Biomarker Analysis) 01:18:58 - Biomarker Analysis 01:19:37 - Select Test Data Set 1 01:19:42 - Data Set Selected 01:20:34 - Perform Data Integrity Check
Views: 8485 Bioinformatics DotCa
Data Visualization with R: global climate change
 
04:06
leonawicz.github.io github.com/leonawicz twitter.com/leonawicz linkedin.com/in/leonawicz blog.snap.uaf.edu An example animation of modeled historical and projected global temperature change. The data analysis, processing and generation of still frames for the video is done using R. Typically an ensemble of models would be used but this video demonstrates a basic animation using one climate model, both with a monthly time series and a monthly moving average time series. If wondering about the y-axis range, the animation shows anomalies, or delta change, from the climate model's historical baseline monthly average temperatures using a given climatology window. In a later video I will use annual and seasonal averages, which will display a smoother signal than monthly series.
Views: 6411 Matthew Leonawicz
Data Visualization and R, part 4, lattice and the small multiple
 
38:55
Ryan Womack, Data Librarian Rutgers University http://ryanwomack.com twitter: @ryandata https://github.com/ryandata/DataViz Screencast version of a workshop at Rutgers University. This segment reviews the use of the lattice package in R for data analysis, including the display of "small multiples" of data in Trellis panels, as well as the historical role of William Cleveland.
Views: 1947 librarianwomack
Introduction to Exploring Social Network Structure with Visualization in R
 
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This video is meant as an introductory excursion into using the research software R to explore social network structure through visualization. Examples of visualization by layout, by node arrangement, and by changing visual node characteristics are provided. Recorded for the University of Maine at Augusta.
Views: 7905 James Cook

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