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. Repository: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Data%20Visualization%20with%20R%20and%20ggplot2 -- 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: 104142 Data Science Dojo
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: 965043 David Langer
Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 229695 CS Dojo
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: 6986 DataCamp
( 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] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 34990 edureka!
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: 3142 Simplilearn
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: 7489 Bharatendra Rai
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: 11297 Bharatendra Rai
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: 131698 Ralf Becker
Download Start Files: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/EDAB/E-DAB-05-Visualizations-Start.xlsx Download Finished Files: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/EDAB/E-DAB-05-Visualizations-Finished.xlsx Pdf notes: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/EDAB/E-DAB-05-Visualizations.pdf This video teaches about how to visualize in Excel with Tables, Conditional Formatting, Column and Bar Charts, Cross Tab Char (Clustered Column / Bar & Stacked Column / Bar), Line Chart, X Y Scatter Chart and Dashboards. Comprehensive Dashboard Example at end. This class : Data Analysis & Business Intelligence Made Easy with Excel Power Tools - Excel Data Analysis Basics = E-DAB Class – Sponsored by YouTube and taught by Mike Girvin, Highline College Instructor, Microsoft Excel MVP and founder of the excelisfun channel at YouTube. This is a free educational resource for people how want to learn about the Basics of Data Analysis and Business Intelligence using Microsoft Power Tools such as, PivotTables, Power Query, Power Pivot, Power BI Desktop and more. Topics: 1. (00:15) Introduction to topics, downloading files and visualizing examples in video. 2. (01:48) Why Visualize? Table or Visualization? 3. (03:47) Edward R. Tufte and High Data/Ink Ratio Rule and “No Chart Junk Rule” 4. (05:57) Tables Formatting Rules 5. (12:05) Conditional Formatting 6. (15:45) Column and Bar Charts 7. (24:04) Cross Tab Chart: Clustered Column / Bar & Stacked Column / Bar 8. (27:10) Line Chart: 1 Number 9. (29:47) Line Chart and IF Function for line chart that shows revenue and emphasizes promotions for company. 10. (35:40) X-Y Scatter Chart: 2 Numbers 11. (37:02) Comprehensive Dashboard example with PivotTables and Charts. Print Setup to allow printing. 12. (39:26) PivotTable Custom Style 13. (53:44) Summary
Views: 9245 ExcelIsFun
R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video 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.
Views: 381465 Global Health with Greg Martin
Text is one of the most interesting and varied data sources on the web and beyond, but it is one of the most difficult to deal with because it is fundamentally a messy, fragmented, and unnormalized format. If you have ever wanted to analyze and visualize text, but don’t know where to get started, this talk is for you. Irene will go through examples of text visualization approaches and the analysis methods required to create them. Irene is an information visualization researcher and developer creating engaging, informative and interactive data-driven interfaces and visualizations. Irene's career in data and its visual forms started at The Visual Communication Lab @ IBM Research and now continues at Bocoup where she is the Director of the Data Visualization team. Irene is also the organizer and program co-chair of Bocoup's OpenVis Conf, a two day conference about the practice of data visualization on the open web.
Views: 3596 Plotly
This video is a sample from Skillsoft's video course catalog. After watching this video, you will be able to identify important R functions and packages for data visualization. Skillsoft is the global leader in eLearning. We train more professionals than any other company and we are trusted by the world's leading organizations, including 65 percent of the Fortune 500. At Skillsoft, our mission is to build beautiful technology and engaging content. Our 165,000+ courses, videos and books are accessed more than 130 million times every month, in 160 countries and 29 languages. With 100% cloud access, anytime, anywhere.
Views: 918 Skillsoft YouTube
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 104242 Bharatendra Rai
In this webinar, Andy Catlin, Director of the Katz School's Data Analytics and Visualization program discusses the many career opportunities available in the growing field of data analytics. The M.S. in Data Analytics and Visualization is a 30-credit Master's degree offered in-person or fully online. Visit yu.edu/katz, email [email protected], or call 833-241-4723 for more information.
Views: 368 Katz School at Yeshiva University
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: 3970 PyData
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: 8254 Bharatendra Rai
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: 6554 PyData
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: 47467 Domino Data Lab
Video content: ○ Discussion of packages used: 0:50 ○ Pulling in the data we'll use: 2:30 ○ Line Chart: 5:00 ○ Histogram: 16:35 ○ Scatter: 25:00 ○ Scatter with interaction: 31:30 ○ Scatter with interaction and animation: 38:06 ○ Chloropleth (map/geographic): 40:30 ○ Basic OHLC: 49:45 ○ OHLC with volume and RSI: 53:55 ○ Recap: 1:08:13 You can find the code I wrote here: https://github.com/joelalcedo/codewithjoel_r/tree/visualization ...but I recommend programming along with the video! ↓↓↓ Resources ↓↓↓ My first video on data wrangling: https://www.youtube.com/watch?v=S0n3o0HwNPU Install RStudio here: https://www.rstudio.com/products/rstudio/download/ Install R here: https://cran.r-project.org/ Learn more about tidyverse here: https://www.tidyverse.org/ Learn about the wbstats package here: https://cran.r-project.org/web/packages/wbstats/README.html Learn about quantmod here: https://www.quantmod.com/ Learn about plotly here: https://plot.ly/ Learn about highcharter here: http://jkunst.com/highcharter/ Check out these viridis color scales! https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html Don't forget to like 👍, comment 💬 and subscribe 🔔 if you learned something new!
Views: 1025 Joel Alcedo
Part 2 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: 142592 David Langer
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: 12690 Analytics University
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: 1919 Brown University
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: 3726 Simplilearn
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: 737 Devoxx
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection 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: 19479 Bharatendra Rai
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. Repository: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Data%20Visualization%20with%20R%20and%20ggplot2 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: 2629 Data Science Dojo
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 7139 5 Minutes Engineering
This video will give you a brief overview of 15 useful interdisciplinary R visualization packages. I have listed below all the links and reference projects sites reviewed in the video. To find out more about the package, type help(package = "packagename") in your R console once you've installed it. My list of R packages for data viz : ggplot2 - CRAN http://ggplot2.org/ patchwork - GitHub https://github.com/thomasp85/patchwork ggiraph - CRAN http://davidgohel.github.io/ggiraph/ dygraphs - CRAN https://rstudio.github.io/dygraphs/index.html googleVis - CRAN https://github.com/mages/googleVis metricsgraphics - GitHub http://hrbrmstr.github.io/metricsgraphics/ taucharts - GitHub https://github.com/hrbrmstr/taucharts RColorBrewer - CRAN http://cran.r-project.org/web/packages/RColorBrewer/index.html Shiny - CRAN http://shiny.rstudio.com/ flexdashboard - CRAN https://rmarkdown.rstudio.com/flexdashboard/ rcdimple - GitHub https://github.com/timelyportfolio/rcdimple plotly - CRAN https://plot.ly/r/ Highcharter - CRAN http://jkunst.com/highcharter/ echarts4r - GitHub http://echarts4r.john-coene.com/index.html geofacet - CRAN https://github.com/hafen/geofacet If I’ve missed any of your favorite R data viz packages in this short list, let me know! comment below.
Views: 1483 Anto Franklin Christuraj
Description Visualization is an essential method in any data scientist’s toolbox and is a key data exploration method and is a powerful tool for presentation of results and understanding problems with analytics. Attendees are introduced to Python visualization packages, Matplotlib, Pandas, and Seaborn. The Jupyter notebook can be downloaded at https://github.com/StephenElston/ExploringDataWithPython Abstract Visualization of complex real-world datasets presents a number of challenges to data scientists. By developing skills in data visualization, data scientists can confidently explore and understand the relationships in complex data sets. Using the Python matplotlib, pandas plotting and seaborn packages attendees will learn to: • Explore complex data sets with visualization, to develop understanding of the inherent relationships. • Create multiple views of data to highlight different aspects of the inherent relationships, with different graph types. • Use plot aesthetics to project multiple dimensions. • Apply conditioning or faceting methods to project multiple dimensions 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: 15277 PyData
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: 16986 ironfrown
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: 1155 librarianwomack
Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUOKu A bit of a change of gears from the usual modeling stuff, this one is about visualizing your data on Google maps using the ggmap package. Code and walkthrough: http://amunategui.github.io/ggmap-example/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!/
Views: 37077 Manuel Amunategui
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: 98 HasGeek TV
R vs Python. Here I argue why Python is the best language for doing data science. Answering the question 'What is the best programming language for' is never black and white and there will be circumstances when python is not the best choice. Python is, though, the best general programming language. It is well supported, comparatively simple to learn, has a wealth of libraries specifically designed for data science such as pandas for data analysis and matplotlib for data visualization, and is extremely versatile. You won't regret learning python for data science. My Python Course - https://www.youtube.com/watch?v=Aah3TmR-dHc&list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J
Views: 30670 Python Programmer
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/
Views: 7036 Data Analysis and Visualization Using R
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: 5213 Bharatendra Rai
This is the 3rd video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1 From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to start looking at your data with a fresh perspective! Transcript: You may have seen node-link diagrams involving more than a hundred thousand nodes. They purport to show a visual representation of the network, but in reality just show a hairball. In this section, we are going to look at alternate ways of visualizing network data that are much more rational. I’m going to introduce to you three different types of network visualizations. The first is visualizing a network using a Matrix Plot. The second is what we call an “Arc Plot”, and the third is called “Circos Plot”. Let’s start first with a Matrix Plot. In a Matrix Plot, nodes are the rows and columns of a matrix, and cells are filled in according to whether an edge exists between the pairs of nodes. On these slides, the left matrix is the matrix plot of the graph on the right. In an undirected graph, the matrix is symmetrical around the diagonal, which I’ve highlighted in grey. I’ve also highlighted one edge in the toy graph, edge (A, B), which is equivalent to the edge (B, A). Likewise for edge (A, C), it is equivalent to the edge (C, A), because there’s no directionality associated with it. If the graph were a directed graph, then the matrix representation is not necessarily going to be symmetrical. In this example, we have a bidirectional edge between A and C, but only an edge from A to B and not B to A. Thus, we will have (A, B) filled in, but not (B, A). If the nodes are ordered along the rows and columns such that neighbours are listed close to one another, then a matrix plot can be used to visualize clusters, or communities, of nodes. Let’s now move on to Arc Plots. An Arc Plot is a transformation of the node-link diagram layout, in which nodes are ordered along one axis of the plot, and edges are drawn using circular arcs from one node to another. If the nodes are ordered according to some some sortable rule, e.g. age in a social network of users, or otherwise grouped together, e.g. by geographic location in map for a transportation network, then it will be possible to visualize the relationship between connectivity and the sorted (or grouped) property. Arc Plots are a good starting point for visualizing a network, as it forms the basis of the later plots that we’ll take a look at. Let’s now move on to Circos Plots. A CircosPlot is a transformation of the ArcPlot, such that the two ends of the ArcPlot are joined together into a circle. Circos Plots were originally designed for use in genomics, and you can think of them as an aesthetic and compact alternative to Arc Plots. You will be using a plotting utility that I developed called nxviz. Here’s how to use it. Suppose we had a Graph G in which we added nodes and edges. To visualize it using nxviz, we first need to import nxviz as nv, and import matplotlib to make sure that we can show the plot later. Next, we instantiate a new nv.ArcPlot() object, and pass in a graph G. We can also order nodes by the values keyed on some “key”. Finally, we can call the draw() function, and as always, we also call plt.show(). The code example here shows you how to create an Arc Plot using nxviz, and you’ll get a chance to play around with the other plots in the exercises. Alright! Let’s get hacking! https://www.datacamp.com/courses/network-analysis-in-python-part-1
Views: 8347 DataCamp
Predictive Analytics & Machine Learning with SAP HANA combines the depth and speed of in-memory analytics with the power of native predictive algorithms. Together with SAP Predictive Analysis for visualization, R's extensive library of statistical and data mining techniques, and the SAP HANA predictive analytic library, you get everything you need to predict the future -- in real-time.
Views: 59013 SAP Technology
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: 6739 Matthew Leonawicz
This workshop with cover how data visualization techniques within ArcGIS can help you explore your data, interpret the results of analysis, and communicate findings. Learn how different data visualization methods, from maps to charts to 3D scenes, can help you compare categories and amounts, visualize distributions and frequency, explore relationships and correlations, and understand change over time or distance. This workshop will focus on charting in ArcGIS Pro, spatial statistical techniques, and communication tools like layouts and Story Maps.
Views: 1940 Esri Events
In this video, you will learn how to construct line graph for time series data and adjust the attributes of the graphs and for interacting with the graph that will enable you to produce quality publication level graphical display. R is a free software and you can download it from the link given below https://www.r-project.org/ Downlaod link for R studio https://www.rstudio.com/products/rstudio/download/ Visit blog for more details: https://agroninfotech.blogspot.com/2018/06/visualizing-graphs-using-r.html Video contains: 1:00 Labeling the axis and adding a title 1:14 Changing the plot type and plotting symbol 1:34 PCH argument 1:58 Overlaying lines and line types 2:40 Line pattern 3:33 Using different colors for points and lines 4:00 Changing the format of the text Get connnected with us on ___________________________________________ G+: https://plus.google.com/u/1/101269397606526645442 Facebook page: https://www.facebook.com/AgronInfoTech/?ref=bookmarks Twitter: https://twitter.com/AgronInfoTech Linked In: https://www.linkedin.com/in/agron-info-tech-7429a6156/ Instagram: https://www.instagram.com/agroninfo/ ____________________________________________ If you have any question please comment below. Thanks for watching this video.
Views: 627 AGRON Info-Tech
Data Analysis and Visualization is a Richfull series Directed to all students interested in Analysing and Visualizing Data using Excel, MATLAB and Wolfram Mathematica. This Course has been made by an expert prophesors in University of Wistern Australia, and Contains the main folowing Topics: 1 Data Visualization in Excel 2 Array Formula in Excel 3 2D Array Formula in Excel 4 Excel Macros 5 Why Matlab 6 Problem Solving in MATLAB 7 MATLAB Orientation - Data Types and Expressions 8 MATLAB Scripts and Functions, Storing Instructions in Files, Getting Help on Build-in Functions 9 Matrices in MATLAB 10 MATLAB Scripts and Functions 11 Random Numbers, Gaussian Random Numbers, Complex Numbers 12 An Examples of Script and Function Files 13 Control Flow, Flow Chart, Relational Operators, Logical Operators, Truth Table, if clause, elseif, Nested if statments, Switch Structure, 14 MATLAB Loops, Nested Loops, Repetition, while, For, 15 Problems with Scripts, Workspace, Why Functions, How to Write a MATLAB Function, Anonymous Functions, 16 MATLAB Programs Input / Output, Escape Characters, Formatted Output, Syntax of Conversion Sequence, 17 Defensive Programming, error, warning, msg, isnumeric, ischar, nargin, nargout, nargchk, narginchk, all, 18 Cell Arrays, Array Types to Store data, Normal Arrays, Curly Brackets, Round Brackets, 19 MATLAB Structures, What is a Structure?, Adding a Field to a Structure, Struct Function, Manipulate the Fields, Preallocate Memory for a Structure Array 20 Basic 2D Plotting, title, xlabel, ylabel, grid, plot 21 Multiple Plots, figure, hold on, off, legend Function, String, Axis Scaling, Subplot, 22 Types of 2D Plots, Polar Plot, Logarithmic Plot, Bar Graphs, Pie Charts, Histograms, X-Y Graphs with 2 y Axes, Function Plots, 23 3D Plot, Line Plot, Surface Plot, Contour plots, Cylinder Plots, mesh, surf, contour, meshgrid, 24 Parametric Surfaces, Earth, Triangular Prism, Generating Points, Default Shading, Shading Flat, Shading Interp, 25 Arrays vs. Matrix Operations, 26 Dot Products, Example Calculating Center of Mass, Center of Gravity, 27 Matrix Multiplication and Division, Matrix Powers, Matrix Inverse, Determinatnts, Cross Products, 28 Applications of Matrix Operations, Solving Linear Equations, Linear Transformations, Eigenvectors 29 Engineering Application of Solving Systems of Linear Equations, Systems of Linear Equations, Kirchhoff's Circuit Laws, 30 Symbolic Differentiation, sym, syms, diff 31 Numerical Differentiation, fplot, Forward Difference, Backward Difference, Central Difference, 32 Numerical Integration, Engineering Applications, Integration, Trapezoid Rule, Simpson's Rule, 33 Monte Carlo Integration, 34 Introduction to ODE in System Biology 35 Introduction to System Biology, Gene Circuits, 36 Solving ODEs in Matlab, Repressilator, Programming steps 37 Interpolation, Cubic Spline Interpolation, Nearest Neighbor, Cubic, Two Dimensional, Three Dimensional, 38 Curve Fitting, Empirical Modelling, Linear Regression, Polynomial Regression, polyfit, polyval, Least Squres, 39 Introduction to Mathematica, 40 Programming in Mathematica 41 Basic Function in Mathematica, Strings, Characters, Polynomials, Solving Equations, Trigonometry, Calculus, 2D Ploting, Interactive Plots, Functions, Matlab vs. MAthematica 42 Numerical Data, Arthematic Operators, Data Types, Lists, Vectors, Matrices, String, Characters, 43 Mathematica Rule Based Programming, Functional Programming, 44 MAthematica Procedural Programming, Procedural Programs, Conditionals and Compositions, Looping Constructs, Errors, Modules, 45 Mathematica Predicates in Rule Based Programming, Patterns and Rules, Rules and Lists, Predicates, Blank, Blanksequence, BlackNullSequence, Number Puzzle, 46 Symbolic Mathematics and Programming, Rule Based Computation, Simplify, Expand, Solve, NSolve, Symbolic Visualisation, 47 Symbolic Computing in Matlab, Symbolic Algebra, sym, syms, Equations, Expressions, Systems of Equations, Calculus,
Views: 136 TO Courses