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Introduction to Data Science with R - Data Analysis Part 1
 
01:21:50
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: 1018502 David Langer
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
01:33:00
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R 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 #Rforbeginners #Rprogramming 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 Telegram: https://t.me/edurekaupdates
Views: 516865 edureka!
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: 140427 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. 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.
Data Analysis in R
 
27:20
Here are two examples of numeric and non numeric data analyses. Both files are obtained from infochimps open access online database.
Views: 42929 Ani Aghababyan
Predictive Modelling Techniques | Data Science With R Tutorial
 
03:10:36
This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&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: 216072 Simplilearn
R Studio: Importing & Analyzing Data
 
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Tutorial on importing data into R Studio and methods of analyzing data.
Views: 192975 MrClean1796
R vs Python | Best Programming Language for Data Science and Analysis | Edureka
 
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***** Python Online Training: https://www.edureka.co/python ***** ***** R Online Training: https://www.edureka.co/r-for-analytics ***** This Edureka video on R vs Python provides you with a short and crisp description of the top two languages used in Data Science and Data Analytics i.e. Python and R (Blog:http://bit.ly/2ClaowR). You will also see the head to head comparison between the two on various parameters and learn why one is preferred over the other in certain aspects. Following topics are covered in the video: 1:30 Various Aspects of Comparison 1:40 Speed 1:56 Legacy 2:13 Code 2:28 Databases 2:45 Practical Agility 3:10 Trends 3:31 Salary 4:25 Syntax Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------- Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ------------------------------------------------------------------------------------------------ #PythonVsR #Python #R #Pythononlinetraining #Javaonlinetraining ----------------------------------------------------------------- For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 89405 edureka!
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
01:10:56
( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions 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 #Rforbeginners #Rprogramming 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 Telegram: https://t.me/edurekaupdates
Views: 403905 edureka!
Introduction to Data Science with R - Data Analysis Part 2
 
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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: 147942 David Langer
R Introduction: Data Analysis and Plotting
 
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This video uses a complex, yet not to large, data set to conduct a simple manipulation of data in R and RStudio. We will introduce data frames, matrices and variables. It demonstrates how to plot charts in R and how to gradually build them out of basic visual elements. The explanation will carefully avoid more complex statistical concepts. The data for this lesson can be obtained from (note different file name): * http://visanalytics.org/youtube-rsrc/r-data/Vic-2013-LGA-Profiles-NoPc.csv The source for the R code of this video can be found here (with some small discrepancies): * http://visanalytics.org/youtube-rsrc/r-intro/Demo-A2-Basic-Data-Analysis-and-Plotting.r Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 26866 ironfrown
Basic Analytical Techniques | Data Science With R Tutorial
 
01:50:45
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: 97382 Simplilearn
Introduction to R Data Analysis: Data Cleaning
 
01:04:00
Data Cleaning and Dates using lubridate, dplyr, and plyr
Views: 47992 John Muschelli
Data Analysis Using R - Session 1 - Bank Marketing
 
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Data Analysis By using Bank Marketing data
Views: 9297 Naveen Balawat
R tutorial: Introduction to cleaning data with R
 
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Learn more about cleaning data with R: https://www.datacamp.com/courses/cleaning-data-in-r Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your instructor for this course on Cleaning Data in R. Let's kick things off by looking at an example of dirty data. You're looking at the top and bottom, or head and tail, of a dataset containing various weather metrics recorded in the city of Boston over a 12 month period of time. At first glance these data may not appear very dirty. The information is already organized into rows and columns, which is not always the case. The rows are numbered and the columns have names. In other words, it's already in table format, similar to what you might find in a spreadsheet document. We wouldn't be this lucky if, for example, we were scraping a webpage, but we have to start somewhere. Despite the dataset's deceivingly neat appearance, a closer look reveals many issues that should be dealt with prior to, say, attempting to build a statistical model to predict weather patterns in the future. For starters, the first column X (all the way on the left) appears be meaningless; it's not clear what the columns X1, X2, and so forth represent (and if they represent days of the month, then we have time represented in both rows and columns); the different types of measurements contained in the measure column should probably each have their own column; there are a bunch of NAs at the bottom of the data; and the list goes on. Don't worry if these things are not immediately obvious to you -- they will be by the end of the course. In fact, in the last chapter of this course, you will clean this exact same dataset from start to finish using all of the amazing new things you've learned. Dirty data are everywhere. In fact, most real-world datasets start off dirty in one way or another, but by the time they make their way into textbooks and courses, most have already been cleaned and prepared for analysis. This is convenient when all you want to talk about is how to analyze or model the data, but it can leave you at a loss when you're faced with cleaning your own data. With the rise of so-called "big data", data cleaning is more important than ever before. Every industry - finance, health care, retail, hospitality, and even education - is now doggy-paddling in a large sea of data. And as the data get bigger, the number of things that can go wrong do too. Each imperfection becomes harder to find when you can't simply look at the entire dataset in a spreadsheet on your computer. In fact, data cleaning is an essential part of the data science process. In simple terms, you might break this process down into four steps: collecting or acquiring your data, cleaning your data, analyzing or modeling your data, and reporting your results to the appropriate audience. If you try to skip the second step, you'll often run into problems getting the raw data to work with traditional tools for analysis in, say, R or Python. This could be true for a variety of reasons. For example, many common algorithms require variables to be arranged into columns and for missing values to be either removed or replaced with non-missing values, neither of which was the case with the weather data you just saw. Not only is data cleaning an essential part of the data science process - it's also often the most time-consuming part. As the New York Times reported in a 2014 article called "For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights", "Data scientists ... spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets." Unfortunately, data cleaning is not as sexy as training a neural network to identify images of cats on the internet, so it's generally not talked about in the media nor is it taught in most intro data science and statistics courses. No worries, we're here to help. In this course, we'll break data cleaning down into a three step process: exploring your raw data, tidying your data, and preparing your data for analysis. Each of the first three chapters of this course will cover one of these steps in depth, then the fourth chapter will require you to use everything you've learned to take the weather data from raw to ready for analysis. Let's jump right in!
Views: 36278 DataCamp
Why Use R? - R Tidyverse Reporting and Analytics for Excel Users
 
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https://www.datastrategywithjonathan.com Free YouTube Playlist https://www.youtube.com/playlist?list=PL8ncIDIP_e6vQ0uQofezvKv3yPnL5Unxe From Excel To Big Data and Interactive Dashboard Visualizations in 5 Hours If you use Excel for any type of reporting or analytics then this course is for you. There are a lot of great courses teaching R for statistical analysis and data science that can sometimes make R seem a bit too advanced for every day use. Also since there are many different ways of using R that can often add to the confusion. The reality is that R can be used to make your every day reporting analytics that you do in Excel much faster and easier without requiring any complex statistical techniques while at the same time giving you a solid foundation to expand into those areas if you so wish. This course uses the Tidyverse standards for using R which provides a single, comprehensive and easy to understand method for using R without complicating things via multiple methods. It's designed to build upon the the skills you are already familiar with in Excel to shortcut your learning journey. If you're looking to learn Advanced Excel, Excel VBA or Databases then you need to check out this video series. In this videos series, I will show you how to use Microsoft Excel in different ways that will make you far more effective at working with data. I'm also going to expand your knowledge beyond Excel and show you tips, tricks, and tools from other top data analytics tools such as R Tidyverse, Python, Data Visualisation tools such as Tableau, Qlik View, Qlik Sense, Plotly, AWS Quick Sight and others. We'll start to touch on areas such as big data, machine learning, and cloud computing and see how you can develop your data skills to get involved in these exciting areas. Excel Formulas such as vlookup and sumifs are some of the top reasons for slow spreadsheets. Alternatives for vlookup include power query (Excel 2010 and Excel 2013) which has recently been renamed to Get and Transform in Excel 2016. Large and complex vlookup formulas can be also done very efficiently in R. Using the R Tidyverse libraries you can use the join functions to merge millions of records effortlessly. In comparison to Excel Vlookup, R Tidyverse Join can pull on multiple columns all at the same time. Microsoft Excel Power Query and R Tidyverse Joins are similar to the joins that you do in databases / SQL. The benefit that they have over relational databases such as Microsoft Access, Microsoft SQL Server, MySQL, etc is that they work in memory so they are actually much faster than a database. Also since they are part of an analytics tool instead of a database it is much faster and easier to build your analysis and queries all in the same tools. My very first R Tidyverse program was written to replace a Microsoft Access VBA solution which was becoming complicated and slow. Note that Microsoft Access is very limited in analytics functions and is missing things as simple as Median. Even though I had to learn R programming from scratch and completely re-write the Microsoft Access VBA solution it was so much easier and faster. It blew my mind how much easier R programming with R Tidyverse was than Microsoft Access VBA or Microsoft Excel VBA. If you have any VBA skills or are looking to learn VBA you should definitely checkout my videos on R Tidyverse. To understand why R Tidyverse is so much easier to work with than VBA. R Tidyverse is designed to work directly with your data. So If you want to add a calculated column that’s around one line of script. In Excel VBA, the VBA is used to control the DOM (Document Object Model). In Excel that means that you VBA controls things like cells and sheets. This means your VBA is designed to capture the steps that you would normally do manually in Microsoft Excel or Microsoft Access. VBA is not actually designed to work directly with your data. Note the most efficient path is to reduce the data pulled down from the database in the first place. This is referring to the amount of data you are pulling down from your data warehouse or data lake. It makes no sense to pull data from a data warehouse / data lake to pull into another database to query add joins / lookups to then pull it into Excel or other analysis tool. Often analyst build these intermediate databases because they either don’t have control of the data warehouse or they need to join additional information. All of these operations are done significantly faster in a tool such as R Tidyverse or Microsoft Excel Power Query.
Views: 15890 Jonathan Ng
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: 49598 Domino Data Lab
Learning Data Analysis with R : Introducing the Raster Format | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2mIPNJq]. Raster data is fundamentally different from vector data, since its values refer to specific areas (cells) and no single locations. This video will clearly explain this difference and teach users how to import this data in R. • Explain what raster data is • Importing with rgdal • Introducing the raster package For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 2575 Packt Video
Introduction to Data Science with R - Exploratory Modeling 1
 
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Part 4 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: 53008 David Langer
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68203 deltaDNA
Introduction to Data Science with R - Data Analysis Part 3
 
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Part 3 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: 66533 David Langer
Sentiment Analysis in R | R Tutorial | R Analytics | R Programming | What is R | R language
 
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This tutorial will deep dive into data analysis using 'R' language. By the end of this tutorial you would have learnt to perform Sentiment Analysis of Twitter data using 'R' tool. To learn more about R, click here: http://goo.gl/uHfGbN This tutorial covers the following topics: • What is Sentiment Analysis? • Sentiment Analysis use cases • Sentiment Analysis tools • Hands-On: Sentiment Analysis in R The topics related to ‘R’ language are extensively covered in our ‘Mastering Data Analytics with R’ course. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 46081 edureka!
Social Network Analysis with R | Examples
 
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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: 23385 Bharatendra Rai
Introduction to R Programming for Excel Users
 
01:45:58
R programming is rapidly becoming a valuable skill for data professionals of all stripes and a must-have skill for aspiring data scientists. Adding R programming to your data analyst skillset allows you to leverage powerful data visualizations, statistical analyses, and even machine learning in your daily work. In this presentation, we illustrate how your knowledge of performing data analyses in Microsoft Excel gives you a unique foundation for quickly learning how to apply R in your daily work. No knowledge of R coding is required for this meetup as Dave will illustrate scenarios in Excel and then walk through how each Excel scenario is implemented in R. Attendees will learn how: • Fundamental concepts of Excel (e.g., working with tables, collections of cells, and functions) translate 100% to working with data in R. • Excel pivot tables translate to R code. • Creating charts in Excel is very similar to creating data visualizations in R. • R offers visualizations not available in Excel out of the box. An Excel spreadsheet and R code will be made available prior to the meetup via GitHub for attendees interested in following along during the talk. Repository: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel -- Learn more about Data Science Dojo here: https://hubs.ly/H0hC49J0 Watch the latest video tutorials here: https://hubs.ly/H0hC3hk0 See what our past attendees are saying here: https://hubs.ly/H0hC3jz0 -- 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: 35031 Data Science Dojo
Introduction to Text Analytics with R: Overview
 
30:38
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – 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 About the Series 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 if 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 Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 73885 Data Science Dojo
Time Series Analysis with forecast Package in R Example Tutorial
 
31:04
What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA
Views: 23013 The Data Science Show
Introduction to Cluster Analysis with R - an Example
 
18:11
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: 111625 Bharatendra Rai
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: 2945 Brown University
Rstudio - an introduction for data analysis
 
24:25
RStudio for beginners doing data analysis -R environment - creating objects -loading data (a txt file) - saving your code -How to refer to variables | plots for univariate continuous variables (histogram, boxplot) -Comments with # - Altering a commands default settings (eg 1 sample t-test)
Views: 7110 Phil Chan
R Tutorial - Introduction to R for Data Analysis
 
10:15
Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com R is an open-source, statistical programming language widely used for data analysis and developing statistical software. In this R Tutorial, we give an overview of the big data analysis process and of the R programming language. We also explain the functionality of the RStudio GUI and demonstrate basic commands for exploring large data sets. Watch this video to learn: - The steps of "big data" analysis - What is Exploratory Data Analysis and why R is a good choice for that - How to use R to explore large data sets
QuantBros.com Introduction to R Programming for Financial Timeseries
 
01:05:30
Learn Financial Programming and Timeseries Analysis Basics in R and R Studio Not enough for you? Want to learn more R? Our friends over at DataCamp will whip you into shape real quick if you need help: https://www.datacamp.com/courses/free-introduction-to-r?tap_a=5644-dce66f&tap_s=84932-063f71 Or if you're more of a Python guy, we have an intro to finance for Python course live on DataCamp right now: https://www.datacamp.com/courses/introduction-to-portfolio-analysis-in-r?tap_a=5644-dce66f&tap_s=84932-063f71 Join the Quants by taking our Quant Course at http://quantcourse.com 1) Basics of R Programming / Downloading R 2) Using Data Frames 3) An Intro to the Quantmod Package 4) Reading in Financial Data from Quantmod 5) Using Vectors in R 6) Reading and Writing Data as CSV Files 7) Plotting Timeseries Data in R 8) Working with Split / Dividend Adjusted Data 9) Calculating Log Returns 10) Converting Log Returns to Arithmetic and Vice-Versa 11) Apply Function in R / Working With Multivariate Data 12) Intro to the Performance Analytics Package 13) XTS and Zoo Objects for Financial Data 14) Chart the Cumulative Return of an Asset 15) Chart the Drawdown and Daily Returns of an Asset 16) Charting Multiple Assets at Once in R 17) Merging Different Datasets With Different Indexes 18) Calculating Sharpe Ratios and other Performance Metrics
Views: 27719 QuantCourse
R: Exploratory Data Analysis (EDA), Univariate analysis
 
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One of the first steps to data analysis is to perform Exploratory Data Analysis. In this video we go over the basics of univariate data analysis, or analyzing each variable to better get to know our data. Here's the dataset used in this video: https://drive.google.com/open?id=0B67hcgV97X0mbnRYNzhYLU53X2c
Views: 5511 James Dayhuff
Introduction to Data Science with R - Cross Validation
 
01:00:34
Part 5 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: 53263 David Langer
Linear Regression in R | Linear Regression Model in R | R Programming Tutorial | Edureka
 
01:20:45
This R tutorial gives an introduction to Linear Regression in R tool. This R tutorial is specially designed to help beginners. View upcoming batches schedule: http://goo.gl/BJJn0B This video helps you understand: • What is Data Mining? • What is Business Analytics? • Stages of Analytics / data mining • What is R? • Overview of Machine Learning • What is Linear Regression? • Case Study The topics related to ‘Data Analytics with R’ have been widely covered in our course. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 36759 edureka!
Text Mining In R | Natural Language Processing | Data Science Certification Training | Edureka
 
36:29
** Data Science Certification using R: https://www.edureka.co/data-science ** In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session: (01:18) Need for Text Mining (03:56) What Is Text Mining? (05:42) What is NLP? (07:00) Applications of NLP (08:33) Terminologies in NLP (14:09) Demo Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 5155 edureka!
Introduction To Business Analytics With R | Data Science With R Training
 
21:22
This is an introduction video to the course business analytics foundation with R tools in which you will be able to learn the difference between analysis and analytics, Understand what is analytics and know the different stages in analytics, Understand where analytics is applied and the process involved in it, get an overview of the various topics covered in different lessons, know the career path of a business analyst, know the popular tools used in analyitcs, understand the role of a data scientist, know the processes involved in analytics, defining a problem statement, collecting and summarizing the data, Detecting and treating outliers in the data. Data Scientist with R Language Certification Training: http://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Introduction-to-Business-Analytics-Foundation-20Neb7zkHVQ&utm_medium=SC&utm_source=youtube 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: 33532 Simplilearn
Data is Ubiquitous - Data Analysis with R
 
02:11
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 34902 Udacity
Combining R with Java for Data Analysis
 
51:05
Java is a general-purpose language and is not particularly well suited for performing statistical analysis. Special languages and software environments have been created by and for statisticians to use. Statisticians think about programming and data analysis much different from Java programmers. These languages and tools make it easy to perform very sophisticated analyses on large data sets easily. Tools, such as R and SAS, contain a large toolbox of statistical tools that are well tested, documented and validated. For data analysis you want to use these tools. In this session we will provide an overview of how to leverage the power of R from Java. R is the leading open source statistical package/language/environment. The first part of the presentation will provide an overview of R focusing on the differences between R and Java at the language level. We’ll also look at some of the basic and more advanced tests to illustrate the power of R. The second half of the presentation will cover how to integrate R and Java using rJava. We’ll look at leverage R from the new Java EE Batching (JSR 352) to provide robust statistical analysis for enterprise applications. Authors: Ryan Cuprak undefined Elsa Cuprak Elsa was a statistician for the Cardiology/Heart Failure and Transplant Departments at Yale School of Medicine. She is an expert in statistics as well as SAS and Excel. Elsa has a masters degree in Actuary Science from the University of Iowa and bachelors in statistics from the University of California Berkley. She worked for several years as an actuary at both Met Life and the West Coast Life Insurance Company.
Views: 12640 Parleys
Exploratory Analysis of Biological Data using R Session 1
 
01:41:48
This is the first session of the Exploratory Analysis of Biological Data using R 2018 workshop hosted by the Canadian Bioinformatics Workshops at the Ontario Institute for Cancer Research. This session is by Boris Steipe from the University of Toronto.
Views: 2187 Bioinformatics DotCa
R tutorial: Exploratory Data Analysis with Baseball Data
 
03:36
Learn more about exploratory data analysis: https://www.datacamp.com/courses/exploring-pitch-data-with-r Hello, and welcome to this course on exploring pitch data in R. My name is Brian Mills and I’ll be your instructor. In this course you’ll use Zack Greinke’s 2015 season as your case study. While Greinke’s been an excellent MLB pitcher for a number of years, he had an especially dominant month in July of 2015. The data in this course will allow you to examine this more closely, and compare his July to other months. Throughout the course, you’ll use a single data set describing every pitch thrown by Greinke to explore changes that may have taken place with respect to his pitch velocity, pitch usage, pitch location, and batted ball outcomes across different months. You’ll begin in this chapter by exploring pitch velocity. By the end of the chapter, you’ll have done enough exploratory analysis to answer the question of whether Greinke’s fastball velocity was higher in July, relative to the other months he pitched in 2015. Throughout the exercises in this chapter, you’ll gradually develop graphical skills to make colorful histograms to meaningfully compare velocity distributions. Finally, note that you’ll return to some of that analysis in the final chapter of the course, where you’ll evaluate whether changes in fastball velocity had any impact on hitting outcomes. The data used for this course are usually referred to as PitchF/X or Statcast data. These data use high tech cameras and a Doppler radar system to collect information on how fast pitches are thrown, the type of pitch thrown, the location of each pitch, the movement and spin rate of each pitch, the velocity of batted balls, and the direction and distance the ball is hit. There’s also information on batter handedness and the ultimate outcome of each pitch that’s thrown. For example, if the batter swung and missed, this is included in the data in the variable “pitch_result”, with the entry “Swinging Strike”. Alternatively, if the batter didn’t swing, the data include information on whether the pitch was called a ball or a strike by the umpire. Finally, there’s information on the outcome of the at bat, such as whether the batter hit a home run, popped out, walked, struck out, or any number of other general baseball outcomes. As you proceed through the course, you’ll examine all of these variables, but first you’ll want to clean the data so that certain analyses can be performed correctly in R. The initial data set you’ll use is named “greinke”. The dates in the data set require some editing, and for you to tell R that it should read the game_date column as a date. Notice that when you check the “class” of the “game_date” variable, it’s listed as a character string. It works better if this is entered as a date to allow for plotting time series data later in the chapter. To reformat the variable as a date, you’ll use the “as.Date” function. This function allows you to specify how your dates are formatted in your data. The dates in the “greinke” data set should be formatted as month-day-year, separated by forward slashes. You’ll also make use of the “separate” function in the “tidyr” package to manipulate the “game_date” variable. This allows you to specify a new variable called “month” so that you can break up your data into each month later in the chapter. As you proceed through the next few exercises, remember that tidying up your data is always an important first step before diving into your analyses. This step ensures that more complex analysis down the road will go smoothly. Go ahead and get started.
Views: 2124 DataCamp
R - Sentiment Analysis and Wordcloud with R from Twitter Data | Example using Apple Tweets
 
23:01
Provides sentiment analysis and steps for making word clouds with r using tweets about apple obtained from Twitter. Link to R and csv files: https://goo.gl/B5g7G3 https://goo.gl/W9jKcc https://goo.gl/khBpF2 Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of apple tweets before and after quarterly earnings report 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: 20508 Bharatendra Rai
Advanced Analytics with R and SQL
 
59:22
R is the lingua franca of Analytics. SQL is the world’s most popular database language. What magic can you make happen by combining the power of R and SQL for Data Science and Advanced Analytics? Imagine the power of exploring, transforming, modeling, and scoring data at scale from the comfort of your favorite R environment. Now, imagine operationalizing the models you create directly in SQL Server, allowing your applications to use them from T-SQL, executed right where your data resides. Come learn how to build and deploy intelligent applications that combine the power of R, SQL Server, thousands of open source R extension packages, and high-performance implementations of the most popular machine learning algorithms at scale.
Transforming Data - Data Analysis with R
 
03:01
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 57794 Udacity
Ridge, Lasso & Elastic Net Regression with R | Boston Housing Data Example,  Steps & Interpretation
 
28:54
Provides example with interpretations of applying Ridge, Lasso & Elastic Net Regression using Boston Housing data. R file: https://goo.gl/ywtVYg Machine Learning videos: https://goo.gl/WHHqWP Includes. - example with Boston housing data - illustrates use of caret package - data partition - custom control parameters - cross validation - linear model - residuals plot - use of glmnet package - ridge regression - plot results - log lambda plot - fraction deviance explained plot - variable importance plot - interpretation - lasso regression - elastic net regression - compare models - best model - saving and reading final model for later use - prediction 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: 16454 Bharatendra Rai
Introduction to Bayesian data analysis - part 1: What is Bayes?
 
29:30
Try my new interactive online course "Fundamentals of Bayesian Data Analysis in R" over at DataCamp: https://www.datacamp.com/courses/fundamentals-of-bayesian-data-analysis-in-r ---- This is part one of a three part introduction to Bayesian data analysis. This first part aims to explain *what* Bayesian data analysis is. See here for part 2: https://youtu.be/mAUwjSo5TJE Here are links to the exercises mentioned in the video: R - https://goo.gl/cxfnYK (if this link does not work for you try http://rpubs.com/rasmusab/257829) Python - https://goo.gl/ceShN5 More Bayesian stuff can be found on my blog: http://sumsar.net. :)
Views: 95034 rasmusab
Survival Analysis in R
 
13:59
Survival and Hazard Functions, Kaplan-Meier Survival, Cox Proportional Hazards Model in R https://sites.google.com/site/econometricsacademy/econometrics-models/survival-analysis
Views: 93642 econometricsacademy
Microarray affymatrix data Analysis using R
 
09:16
Microarray affymatrix data Analysis using R studio.
KNN Algorithm Using R | KNN Algorithm Example | Data Science Training | Edureka
 
24:59
** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka video on "KNN algorithm using R", will help you learn about the KNN algorithm in depth, you'll also see how KNN is used to solve real-world problems. Below are the topics covered in this module: (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On (00:52) Introduction to Machine Learning (03:45) What is KNN Algorithm? (08:09) KNN Use Case (09:07) KNN Algorithm step by step (12:12) Hands - On Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #knn #datasciencewithr #datasciencecourse #datascienceforbeginners #knnalgorithm #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling 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. Analyze 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. Analyze 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. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 5547 edureka!
Linear Regression Analysis using R | Data Science With R Tutorial
 
35:43
This video offers an overview of Linear Regression Analysis using R. Linear regression is a common technique to determine how one variable of interest is affected by another. It is used to predict values of other variables from those that have more data for corroboration, and also for correcting the linear dependence of one variable on the other. Get your Data Scientist Certification Training Here - http://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-Regression-Analysis-OsJ-ho-IQsY&utm_medium=SC&utm_source=youtube 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: 6375 Simplilearn