Search results “R data analysis examples”

Here are two examples of numeric and non numeric data analyses. Both files are obtained from infochimps open access online database.

Views: 40144
Ani Aghababyan

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: 911125
David Langer

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: 16794
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: 124104
Ralf Becker

R programming for beginners - This video is an introduction to R programming in which I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life.
This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2
This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.

Views: 304529
Global Health with Greg Martin

Three R scripts showing some simple exploratory data analyses in R: contingency tables, histograms, boxplots/dotplots, and groupwise means.

Views: 28893
James Scott

( 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!
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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
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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.
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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: 418280
edureka!

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: 22601
ironfrown

Data Cleaning and Dates using lubridate, dplyr, and plyr

Views: 43276
John Muschelli

( Data Science Training - https://www.edureka.co/data-science )
In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future!
Below are the topics we will cover in this live session:
1. Why Time Series Analysis?
2. What is Time Series Analysis?
3. When Not to use Time Series Analysis?
4. Components of Time Series Algorithm
5. Demo on Time Series
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: 72179
edureka!

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: 6154
DataCamp

Tutorial on importing data into R Studio and methods of analyzing data.

Views: 165533
MrClean1796

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: 61480
David Langer

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: 18258
Udacity

Materials for exercise can be downloaded here: https://github.com/jeromyanglim/score-tests-with-r-exercise
The aim of this exercise is to practice implementing a basic set of analyses in R using R Markdown and ProjectTemplate. It assumes some familiarity with R.
• import data, and prepare a data file
• check missing data
• create scale scores
• perform reliability analysis
• run some additional analyses
See this link for more information about my ProjectTemplate customisations:
http://jeromyanglim.blogspot.com.au/2014/05/customising-projecttemplate-in-r.html

Views: 1500
Jeromy Anglim

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: 177650
CS Dojo

( Data Science Training - https://www.edureka.co/data-science )
This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1) Introduction to Machine Learning
2) What is Regression?
3) Types of Regression
4) Linear Regression Examples
5) Linear Regression Use Cases
6) Demo in R: Real Estate Use Case
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#LinearRegression #Datasciencetutorial #Datasciencecourse #datascience
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
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
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

Views: 62954
edureka!

** 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: 1776
edureka!

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, Dave Langer illustrates 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.
GitHub Files:
https://github.com/datasciencedojo/meetup/tree/master/r_programming_excel_users
Find out more about David here:
https://www.meetup.com/data-science-dojo/events/239049571/
--
Learn more about Data Science Dojo here:
https://hubs.ly/H0f8xxZ0
See what our past attendees are saying here:
https://hubs.ly/H0f8xyt0
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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: 25496
Data Science Dojo

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: 98803
Bharatendra Rai

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...
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
--
At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.
--
Learn more about Data Science Dojo here:
https://hubs.ly/H0f5JLp0
See what our past attendees are saying here:
https://hubs.ly/H0f5JZl0
--
Like Us: https://www.facebook.com/datascienced...
Follow Us: https://twitter.com/DataScienceDojo
Connect with Us: https://www.linkedin.com/company/data...
Also find us on:
Google +: https://plus.google.com/+Datasciencedojo
Instagram: https://www.instagram.com/data_scienc...
Vimeo: https://vimeo.com/datasciencedojo

Views: 62741
Data Science Dojo

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
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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: 11471
Parleys

This "Linear regression in R" video will help you understand what is linear regression, why linear regression, you will see how linear regression works using a simple example and you will also see a use case predicting the revenue of a company using linear regression. Linear Regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors. The first one is which variables, in particular, are significant predictors of the outcome variable and the second one is how significant is the regression line to make predictions with the highest possible accuracy. Now, lets deep dive into this video and understand what is linear regression.
Below topics are explained in this "Linear Regression in R" video:
1. Why linear regression? ( 00:28 )
2. What is linear regression? ( 03:09 )
3. How linear regression works? ( 03:48 )
4. Use case - Predicting the revenue using linear regression (10:05)
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/HBso29
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science 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.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training 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 the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
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
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-in-R-2Sb1Gvo5si8&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn courses, visit:
- 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: 3052
Simplilearn

This Time Series Analysis (Part-1) in R tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data.
Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs
You can also go through the slides here: https://goo.gl/RsAEB8
A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.
Below topics are explained in this " Time Series in R Tutorial " -
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forcast car sales for the 5th year
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#DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science 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.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training 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 the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
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
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-gj4L2isnOf8&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn courses, visit:
- Facebook: https://www.facebook.com/Simplilearn
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Get the iOS app: http://apple.co/1HIO5J0

Views: 15176
Simplilearn

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: 14585
Bharatendra Rai

( R Training : https://www.edureka.co/r-for-analytics )
This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session:
1. Why Data Mining?
2. What is Data Mining
3. Knowledge Discovery in Database
4. Data Mining Tasks
5. Programming Languages for Data Mining
6. Case study using R
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Website: https://www.edureka.co/data-science
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 59613
edureka!

What is the difference between Autoregressive (AR) and Moving Average (MA) models? Explanation Video: https://www.youtube.com/watch?v=2kmBRH0caBA

Views: 17009
The Data Science Show

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: 6288
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 Dave Langer will provide an introduction to data visualization with the ggplot2 package. The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.
Attendees will learn how to:
• Craft ggplot visualizations, including customization of rendered output.
• Choose optimal visualizations for the type of data and the nature of the analysis at hand.
• Leverage ggplot2’s powerful segmentation capabilities to achieve “visual drill-in of data”.
• Export ggplot2 visualizations from RStudio for use in documents and presentations.
Github:
https://github.com/datasciencedojo/IntroDataVisualizationWithRAndGgplot2
--
Learn more about Data Science Dojo here:
https://hubs.ly/H0dTtFq0
See what our past attendees are saying here:
https://hubs.ly/H0dTtFw0
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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: 95104
Data Science Dojo

1. Download the example data set: fitnessAppLog.csv https://drive.google.com/open?id=0Bz9Gf6y-6XtTczZ2WnhIWHJpRHc
2. Data Partition, Oversampling in the R Software Example Code: https://drive.google.com/open?id=13_EeM3neRu1QDSYx6myZoTQuEx7IHB8j

Views: 1622
The Data Science Show

( 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

Views: 298399
edureka!

Tim Young from CIC (Curtin Institute of Computation) talks about the use of R at the ‘Tools for Data Analysis: an overview of SPSS, NVivo , R and Python’ session that was held at Robertson Library on August 9th 2017.
Learn about the tools available to assist with analysing your quantitative or qualitative data. This workshop was designed to help staff and postgraduate students use library resources effectively for research.

Views: 1276
Curtin Library

Paper: Advanced Data Analysis
Module: Missing Data Analysis : Multiple Imputation in R
Content Writer: Souvik Bandyopadhyay

Views: 20026
Vidya-mitra

In this video, I show how to use R to fit a linear regression model using the lm() command. I also introduce how to plot the regression line and the overall arithmetic mean of the response variable, and I briefly explain the use of diagnostic plots to inspect the residuals. Basic features of the R interface (script window, console window) are introduced.
The R code used in this video is:
data(airquality)
names(airquality)
#[1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day"
plot(Ozone~Solar.R,data=airquality)
#calculate mean ozone concentration (na´s removed)
mean.Ozone=mean(airquality$Ozone,na.rm=T)
abline(h=mean.Ozone)
#use lm to fit a regression line through these data:
model1=lm(Ozone~Solar.R,data=airquality)
model1
abline(model1,col="red")
plot(model1)
termplot(model1)
summary(model1)

Views: 318528
Christoph Scherber

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: 10497
Bharatendra Rai

DragonflyStatistics.github.io | Data Analysis with R

Views: 5827
Dragonfly Statistics

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: 206498
Simplilearn

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: 29776
DataCamp

Introduction to multiple regression in r. The data set is discussed and exploratory data analysis is performed here using correlation matrix and scatterplot matrix.

Views: 38162
Jalayer Academy

Provides an example of steps involved in carrying out association rule analysis in R. Association rule analysis is also called market basket analysis or affinity analysis. Some examples of companies using this method include Amazon, Netflix, Ford, etc. Definitions for support, confidence and lift are also included. Also includes,
- use of rules package and a priori function
- reducing number of rules to manageable size by specifying parameter values
- finding interesting and useful rules
- finding and removing redundant rules
- sorting rules by lift
- visualizing rules using scatter plot, bubble plot and graphs
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: 16787
Bharatendra Rai

I analyze a dataset of wind turbines in the US as an example of exploratory data analysis in R, performed without looking at the data in advance. This is part of the #tidytuesday project: https://github.com/rfordatascience/tidytuesday

Views: 2366
David Robinson

Intellipaat Data Science with R Programming Course:- https://intellipaat.com/data-scientist-course-training/
This logistic regression in r tutorial is an introduction to what is regression, what is logistic regression, linear regression vs logistic regression, logistic regression in r and how to implement logistic regression in r studio.
Interested to learn r language and r programming still more? Please check similar r Programming blogs here:- https://goo.gl/94cLeV
Watch complete r programming tutorials here:- https://goo.gl/Szm1Li
This logistic regression tutorial helps you to learn following topics:
00:23 - what is regression
02:16 - logistic regression
02:52 - linear regression vs logistic regression
05:02 - logistic regression in r
Are you looking for something more? Enroll in our r programming language course & become a certified r programming Professional (https://goo.gl/YHq2Ms). It is a 16 hrs instructor led r programming training provided by Intellipaat which is completely aligned with industry standards and certification bodies.
If you’ve enjoyed this logistic regression in r tutorial, Like us and Subscribe to our channel for more similar informative r tutorials.
Got any questions about r training? Ask us in the comment section below.
----------------------------
Intellipaat Edge
1. 24*7 Life time Access & Support
2. Flexible Class Schedule
3. Job Assistance
4. Mentors with +14 yrs
5. Industry Oriented Course ware
6. Life time free Course Upgrade
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Why should you watch this r programming tutorial?
R programming has been the most promising language when it came to data analytics and machine learning as it is equipped with a wide array of functionalities. We are offering the top r programming tutorial that can be watched by anybody to learn r language. Our r programming tutorial has been created with extensive inputs from the industry so that you can learn Data Science easily.
Who should watch this r programming tutorial?
If you are a software engineers and data analysts, Business intelligence professionals or working as a SAS developers wanting to learn open source technology or graduates aspiring for a career in data science. This Intellipaat r programming tutorial is your first step to learn Data Science. Since this r programming video can be taken by anybody, you can also watch this logistic regression in r to take your skills to the next level.
Why r programming is important?
Because of it's extensible nature, R programming is finding higher adoption rates for Data Science specialization. It can be widely deployed for various applications and can be easily scaled.learning this r language will help you grab all those jobs that are being created at large companies offering very good pay scales.
Why should you opt for a r programming career?
It is one of the booming technologies in the market and hence R professionals are highly required by the companies. You will grab the best jobs in top MNCs after finishing this Intellipaat r programming online training. The entire Intellipaat r programming course is in line with the industry needs. There is a huge demand for r programming certified professional. The salaries for r programming professional are very good. Hence this Intellipaat logistic regression in r is your stepping stone to a successful career!
#logisticregressioninr #logisticregression #datasciencetutorial
------------------------------
For more Information:
Please write us to [email protected], or call us at: +91- 7847955955
Website: https://goo.gl/YHq2Ms
Facebook: https://www.facebook.com/intellipaatonline
LinkedIn: https://www.linkedin.com/in/intellipaat/
Twitter: https://twitter.com/Intellipaat

Views: 2510
Intellipaat

( Data Science Training - https://www.edureka.co/data-science )
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
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1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
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About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
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Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
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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
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Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

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edureka!

Fixed Effects and Random Effects Models in R
https://sites.google.com/site/econometricsacademy/econometrics-models/panel-data-models

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econometricsacademy

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QualityGurus

Microarray affymatrix data Analysis using R studio.

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Thugs of Science -Lab 127

This video describes how to use RCommander to convert numeric variables to factors, reverse code items, compute Cronbach's alpha, compute scale scores, and create a variable that is a median split of another variable.

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Tera Letzring

Discover the power of the data frame in R!
Join DataCamp today, and start our interactive intro to R programming tutorial for free: https://www.datacamp.com/courses/free-introduction-to-r
By now, you already learned quite some things in R. Data structures such as vectors, matrices and lists have no secrets for you anymore. However, R is a statistical programming language, and in statistics you'll often be working with data sets. Such data sets are typically comprised of observations, or instances. All these observations have some variables associated with them. You can have for example, a data set of 5 people. Each person is an instance, and the properties about these people, such as for example their name, their age and whether they have children are the variables. How could you store such information in R? In a matrix? Not really, because the name would be a character and the age would be a numeric, these don't fit in a matrix. In a list maybe? This could work, because you can put practically anything in a list. You could create a list of lists, where each sublist is a person, with a name, an age and so on. However, the structure of such a list is not really useful to work with. What if you want to know all the ages for example? You'd have to write a lot of R code just to get what you want. But what data structure could we use then?
Meet the data frame. It's the fundamental data structure to store typical data sets. It's pretty similar to a matrix, because it also has rows and columns. Also for data frames, the rows correspond to the observations, the persons in our example, while the columns correspond to the variables, or the properties of each of these persons. The big difference with matrices is that a data frame can contain elements of different types. One column can contain characters, another one numerics and yet another one logicals. That's exactly what we need to store our persons' information in the dataset, right? We could have a column for the name, which is character, one for the age, which is numeric, and one logical column to denote whether the person has children.
There still is a restriction on the data types, though. Elements in the same column should be of the same type. That's not really a problem, because in one column, the age column for example, you'll always want a numeric, because an age is always a number, regardless of the observation.
So, for the practical part now: creating a data.frame. In most cases, you don't create a data frame yourself. Instead, you typically import data from another source. This could be a csv file, a relational database, but also come from other software packages like Excel or SPSS.
Of course, R provides ways to manually create data frames as well. You use the data dot frame function for this. To create our people data frame that has 5 observations and 3 variables, we'll have to pass the data frame function 3 vectors that are all of length five. The vectors you pass correspond to the columns. Let's create these three vectors first: `name`, `age` and `child`.
Now, calling the data frame function is simple:
The printout of the data frame already shows very clearly that we're dealing with a data set. Notice how the data frame function inferred the names of the columns from the variable names you passed it. To specify the names explicitly, you can use the same techniques as for vectors and lists. You can use the names function, ... , or use equals sings inside the data frame function to name the data frame columns right away.
Like in matrices, it's also possible to name the rows of the data frame, but that's generally not a good idea so I won't detail on that here.
Before you head over to some exercises, let me shortly discuss the structure of a data frame some more.
If you look at this structure, ..., there are two things you can see here: First, the printout looks suspiciously similar to that of a list. That's because, under the hood, the data frame actually is a list. In this case, it's a list with three elements, corresponding to each of the columns in the data frame. Each list element is a vector of length 5, corresponding to the number of observations. A requirement that is not present for lists is that the length of the vectors you put in the list has to be equal. If you try to create a data frame with 3 vectors that are not all of the same length, you'll get an error.
Second, the name column, which you expect to be a character vector, is actually a factor. That's because R by default stores the strings as factors. To suppress this behaviour, you can set the stringsAsFactors argument of the data.frame function to FALSE
Now, the name column actually contains characters.
With this new knowledge, you're ready for some first exercises on this extremely useful and powerful data structure.

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DataCamp

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Get code used in this video from: https://raw.githubusercontent.com/steviep42/youtube/master/YOUTUBE.DIR/BB_phys_stats_ex1.R
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Part 1 - This video tutorial guides the user through a manual principal components analysis of some simple data. The goal is to acquaint the viewer with the underlying concepts and terminology associated with the PCA process. This will be helpful when the user employs one of the "canned" R procedures to do PCA (e.g. princomp, prcomp), which requires some knowledge of concepts such as loadings and scores.

Views: 137652
Steve Pittard

Learn how you can create basic graphics in R.
Join DataCamp today, and start our interactive intro to R programming tutorial for free: https://www.datacamp.com/courses/free-introduction-to-r
One of the main reasons that both academic and professional workers turn to R, is because of its very strong graphical capabilities. Walking away from graphical point and click programs such as Excel and Sigmaplot can be quite scary, but you'll soon see that R has much to offer. The big difference is that you create plots with lines of R code. You can thus perfectly replicate and modify plots inside R. This idea nicely fits into the spirit of reproducibility. The graphics package, that is loaded by default in R, already provides quite some functionality to create beautiful, publication-quality plots. Of course, throughout time, also a bunch of R packages have been developed to create visualizations differently or to build visualizations in very specific fields of study. Examples of very popular packages are ggplot2, ggvis and lattice, but I won't talk about those here.
Among many others, the graphics package contains two functions that I'll talk about: `plot()` and `hist()`. I'll start with the `plot()` function first. This function is very generic, meaning that it can plot many different things. Depending on the type of data you give it, `plot()` will generate different plots. Typically, you'll want to plot one or two columns from a data frame, but you can also plot linear models, kernel density estimates and many more.
Suppose we have a data frame `countries`, containing information on all of the countries in the world. There's of course the name of the country, but also the area, the continent it belongs to and other information such as population and religion. If you have a look at the structure of this data frame, you can see that there are numerical variables, characters, but also categorical variables, such as continent and religion. How would `plot()` handle these different data types? Let's find out!
Let's try to plot the continent column of `countries`. We do the selection using the dollar sign:
Cool. It looks like R figured out that continent is a factor, and that you'll probably want a bar chart, containing the number of countries there are in each continent. It also automatically assigned labels to the different bars. Now, what if you decide to plot a continuous variable, such as the population:
This time, the populations of the countries are shown on an indexed plot. The first country corresponds to the index 1, while the population of the fiftieth observation in the data frame corresponds to index 50. Of course it's also possible to plot two continuous variables, such as area versus population:
The result is a scatter plot, showing a dot for each country. There are some huge countries with many people living there, but also many smaller countries with also less people. It makes perfect sense that area and population are somewhat related, right? To make this relationship more clear, we can apply the logarithm function on both of the area and population vectors. You can use the `log()` function twice:
For every continent now, a stacked bar chart of the different religions is depicted. On the right, you conveniently see the axis showing the proportions of each religion in each continent. If you switch the two variables inside the plot function, for every religion, a stacked bar chart of the different continents is depicted. This means that the first element in the `plot()` function is the variable on the horizontal axis, the x axis, while the second element is the element on the vertical axis, the y axis.
All these examples show that the plot function is very capable at visualizing different kinds of information and manages to display the information in an interpretable way.
To better understand your numerical data, it's often a good idea to have a look at its distribution. You can do this with the `hist()` function, which is short for histogram. Basically, a histogram is a visual representation of the distribution of your dataset by placing all the values in bins and plotting how many values there are in each bin. Say we want to get a first impression on the population in all of Africa's countries. With a logical vector, africa_obs, you can perform a selection on the countries data frame, to create sub data frame that contains only african countries.

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DataCamp

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Selling in special circumstances. shares you bought at different times and prices in one company shares through an investment club shares after a company merger or takeover employee share scheme shares. Jointly owned shares and investments. If you sell shares or investments that you own jointly with other people, work out the gain for the portion that you own, instead of the whole value. There are different rules for investment clubs. What to do next. Deduct costs. Apply reliefs.