Search results “Content analysis with r”

Introduction to R Tutorial to learn qualitative analysis package Word2Vec. Learn how to turn text into vectors and create a dendrogram and text map of your data. No prior coding experience necessary.

Views: 4471
Eren Kavvas

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: 64512
deltaDNA

For more information, please visit http://web.ics.purdue.edu/~jinsuh/.

Views: 11587
Jinsuh Lee

Text Mining with R. Import a single document into R.

Views: 16127
Jalayer Academy

Download FREE CD http://www.sendspace.com/file/pqzs5a CLICK TO DOWNLOAD

Views: 39475
RSectorCrew

Checking Linear Regression Assumptions in R ;
Dataset: https://goo.gl/tJj5XG; Linear Regression Concept and with R: https://bit.ly/2z8fXg1;
More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT;
How to test linear regression assumptions in R?
In this R tutorial, we will first go over some of the concepts for linear regression like how to add a regression line, how to interpret the regression line (predicted or fitted Y value, the mean of Y given X), how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) and the assumptions when fitting a linear regression model.
Then we will discuss the regression diagnostic plots in R, the reason for making diagnostic plots, and how to produce these plots in R; You will learn to check the linearity assumption and constant variance (homoscedasticity) for a regression model with residual plots in R and test the assumption of normality in R with QQ (Quantile Quantile) plots. You will also learn to check the constant variance assumption for data with non-constant variance in R, produce and interpret residual plots, QQ plots, and scatterplots for data with non-constant variance, and produce and interpret residual plots, QQ plots, and scatterplots for data with non-linear relationship in R.
►► Download the dataset here:
https://statslectures.com/r-stats-datasets
►► Watch More:
►Linear Regression Concept and Linear Regression with R Series: https://bit.ly/2z8fXg1
►Simple Linear Regression Concept https://youtu.be/vblX9JVpHE8
►Nonlinearity in Linear Regression https://youtu.be/tOzwEv0PoZk
► R Squared of Coefficient of Determination https://youtu.be/GI8ohuIGjJA
► Linear Regression in R Complete Series https://bit.ly/1iytAtm
■ Table of Content:
0:00:29 Introducing the data used in this video
0:00:49 How to fit a Linear Regression Model in R?
0:01:03 how to produce the summary of the linear regression model in R?
0:01:15 How to add a regression line to the plot in R?
0:01:24 How to interpret the regression line?
0:01:43 How to interpret the residuals or errors?
0:01:53 where to find the Residual Standard Error (Standard Deviation of Residuals) in R
0:02:14 What are the assumptions when fitting a linear regression model and how to check these assumptions
0:03:01 What are the built-in regression diagnostic plots in R and how to produce them
0:03:24 How to use Residual Plot for testing linear regression assumptions in R
0:03:50 How to use QQ-Plot in R to test linear regression assumptions
0:04:33 How to produce multiple plots on one screen in R
0:05:00 How to check constant variance assumption for data with non-constant variance in R
0:05:12 How to produce and interpret a Scatterplot and regression line for data with non-constant variance
0:05:40 How to produce and interpret the Residual plot for data with non-constant variance in R
0:06:02 How to produce and interpret the QQ plot for data with non-constant variance in R
0:06:12 How to produce and interpret a Scatterplot with regression line for data with non-linear relationship in R
0:06:40 How to produce and interpret the Residual plot for a data with non-linear relationship in R
0:06:52 How to produce and interpret the QQ plot for a data with non-linear relationship in R
0:07:02 what is the reason for making diagnostic plots
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
These #RTutorials are created by #marinstatslectures to support a course at The University of British Columbia (#UBC) although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 147816
MarinStatsLectures-R Programming & Statistics

An overview of the process of qualitative data analysis based on Alan Bryman's four stages of analysis.
Reference
Bryman, A (2001) Social Research Methods, Oxford: Oxford University Press
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
http://creativecommons.org/licenses/by-nc-sa/4.0/

Views: 194134
Graham R Gibbs

Sentiment Analysis Implementation and Wordcloud.
Find the terms here:
http://ptrckprry.com/course/ssd/data/positive-words.txt
http://ptrckprry.com/course/ssd/data/negative-words.txt

Views: 20575
Jalayer Academy

Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language. Also it refers to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, i.e., whether the attitude behind this text is positive, negative or neutral. Understanding the opinions behind user-generated content automatically is of great help for commercial and political use, among others. The task can be conducted on different levels, classifying the polarity of words or sentences.
It is one of the most active research areas in natural language processing and text mining in recent years. Its popularity is mainly due to two reasons. First, it has a wide range of applications because opinions are central to almost all human activities and are key influencers of our behaviors. Whenever we need to make a decision, we want to hear others’ opinions. Second, it presents many challenging research problems, which had never been attempted before the year 2000. Part of the reason for the lack of study before was that there was little opinionated text in digital forms. It is thus no surprise that the inception and the rapid growth of the field coincide with those of the social media on the Web. In fact, the research has also spread outside of computer science to management sciences and social sciences due to its importance to business and society as a whole.

Views: 4052
Mavericks 045_049_078

Part A of a two part series on using R/qtl to analyze mouse strain breeding data.

Views: 12398
Bob Gotwals

Whatsapp Chat Sentiment analysis using R programming! Subscribe to my channel for new and cool tutorials.
You can also reach out to me on twitter: https://twitter.com/sudharsan1396
Code for this video:
https://github.com/sudharsan13296/Whatsapp-analytics

Views: 7618
Sudharsan Ravichandiran

Using R and the metafor package to conduct meta-analysis. See these previous posts for more information and code:
http://www.deeplytrivial.com/2018/04/e-is-for-effect-sizes.html
http://www.deeplytrivial.com/2018/04/v-is-for-meta-analysis-variance.html
http://www.deeplytrivial.com/2018/04/w-is-for-meta-analysis-weights.html
And the BMJ Open article mentioned in the video: http://bmjopen.bmj.com/content/6/7/e010247
Finally, the homepage for the metafor package: http://www.metafor-project.org/doku.php/metafor

Views: 1584
Sara Locatelli

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

Views: 19314
Vidya-mitra

Part of the free, online tutorial in data journalism skills at: http://mtweb.mtsu.edu/kblake/datajournalism.html

Views: 5142
Ken Blake

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: 5889
Udacity

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/2Gi1Gzx].
Content-based methods rely on the product properties to create recommendations, they can ignore the user preferences, to begin with. Content-based method dishes out the Needed recommendation and user profile can be built in the background. With a sufficient user profile, content-based methods can be further improved or can move on to using collaborative filtering methods.
The content-based filtering method provides a list of top N recommendations based on some similarity scores.
• Look at the content-based recommendation system example
For the latest Big Data and Business Intelligence 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: 160
Packt Video

Perform complex text analysis with ease. Automatically find unique phrase patterns within text, identify phrase and word frequency, custom latent variable frequency and definition, unique and common words within text phrases, and more. This is data mining made easy.
Video Topics:
1) How to insert text content data for analysis
2) Perform qualitative content analysis on sample survey
3) Review text content phrase themes and findings within data
4) Review frequency of words and phrase patterns found within data
5) Label word and phrase patterns found within data

Views: 59312
etableutilities

Lesson 8: Heatmaps — It’s gettin’ hot in here!
Code, data, and video downloads available on http://marianattestad.com/blog
Analyze your data yourself, beyond the limitations of Excel and without waiting for a collaborator.
My name is Maria Nattestad and I'm a computational biologist at Cold Spring Harbor Laboratory. Whenever I talk to other grad students and post-docs about their experience with analyzing their data, the most common answer I get is "I'm really frustrated with Excel and Prism, so I tried learning R but it was really complicated and I never got far enough to where I could apply it to my research." I made this course for them, and I hope you will like it too.
From the initial stages of quickly determining what your data is telling you, up to polished, publication-quality plots, this course will show you how to set up a smooth system for every step along the way.
It will save you time by automating your data analysis, so you can focus on designing experiments and interpreting the results for your next big paper.
Recently it has become more necessary for biologists to know some computational skills, but that doesn’t mean you have to be a programmer. My goal with this course is to give you an awesome introduction to the one skill that is immediately applicable to your research and also the easiest to learn right away. No matter what research you do, you will need to make some plots, and R is a great language for doing that. The best parts of R are the awesome packages that other people have built already. My favorites are ggplot and ComplexHeatmaps, so I go into detail on them in this course.
This video course will get you analyzing your data and plotting it quickly while teaching you only the programming skills that are most useful for your research, so you can stop messing around in Excel and get back to the fun part of your research.
Here's what I'll teach you.
Lesson 1: Hit the ground running — From data to plot with a few magic words
Lesson 2: Importing and downloading data — From Excel, text files, or publicly available data, I’ve got you covered.
Lesson 3: Interrogating your data — What are you???
Lesson 4: Filtering and cleaning up data — Kicking out the data that annoys you and polishing up the rest
Lesson 5: Tweaking everything in your plots — Satisfying your inner perfectionist…or the journal’s nit-picky demands…
Lesson 6: Plot anything! — A nice figure for any occasion
- Bar plots
- Scatter plots
- Box plots
- Violin plots
- Density plots
- Dot-plots
- Line-plots for time-course data
- Venn diagrams
Lesson 7: Multifaceted figures — When you just really need 92 plots to make your point
Lesson 8: Heatmaps — It’s gettin’ hot in here!
These lessons are a total of 3 hours of video content and all the code and data so you can follow along and play with the plots yourself.
By the end of this course, you will be able to confidently get your data into R (including straight from Excel), analyze it, produce several types of publication-quality plots, and automatically save them for your next big paper.
We will look at genomic data, gene lists, time-course data, structural variants, and copy number data for heatmaps. After just 3 hours of video lessons, you will be able to do things that took me months to learn.

Views: 28098
Maria Nattestad

Simple Linear Regression in R ; For more Statistics and R Programming Tutorials: https://goo.gl/4vDQzT; Simple Linear Regression Concept and Terminology: https://goo.gl/VhWmVD ;Dataset: https://goo.gl/tJj5XG
How to fit a Linear Regression Model in R, Produce Summaries and ANOVA table for it.
◼︎ What to Expect in this R Tutorial:
►In this R video tutorial you will learn When to use a regression model, and how to use the “lm” command in R to fit a linear regression model for your data
► Here you will also learn to produce summaries for your regression model using “summary” command in R statistics software; these summaries can include intercept, test statistic, p value, and estimates of the slope for your linear regression model
► in this tutorial, you will also become familiar with the Residual Error: a measure of the variation of observations in regression line
► You will also learn to ask R programming software for the attributes of the simple linear regression model using "attributes" command, extract certain attributes from the regression model using the dollar sign ($), add a regression line to a plot in R using "abline" command and change the color or width of the regression line.
► this R tutorial will show you how to get the simple linear regression model's coefficient using the "coef" command or produce confidence intervals for the regression model using "confint" commands; moreover, you will learn to change the level of confidence using the "level" argument within the "confint" command.
►You will also learn to produce the ANOVA table for the linear regression model using the "anova" command, explore the relationship between ANOVA table and the f-test of the regression summary, and explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table.
► ►You can access and download the dataset here:
https://statslectures.com/r-stats-datasets
► ► Watch this Statistics Tutorial on the concept and terminology for Simple Linear Regression Model https://youtu.be/vblX9JVpHE8
◼︎ Table of Content:
0:00:07 When to fit a simple linear regression model?
0:01:11 How to fit a linear regression model in R using the "lm" command
0:01:14 How to access the help menu in R for any command
0:01:36 How to let R know which variable is X and which one is Y when fitting a regression model
0:01:45 How to ask for the summary of the simple linear regression model in R including estimates for intercept, test statistic, p-values and estimates of the slope.
0:02:27 Residual standard error (residual error) in R
0:02:53 How to ask for the attributes of the simple linear regression model in R
0:03:06 How to extract certain attributes from the simple linear regression model in R
0:03:40 How to add a regression line to a plot in R
0:03:52 How to change the color or width of the regression line in R
0:04:07 How to get the simple linear regression model's coefficient in R
0:04:11 How to produce confidence intervals for model's coefficients in R
0:04:21 How to change the level of confidence for model's coefficients in R
0:04:38 How to produce the ANOVA table for the linear regression in R
0:04:47 Explore the relationship between ANOVA table and the f-test of the linear regression summary
0:04:55 Explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table
♠︎♣︎♥︎♦︎To learn more:
Subscribe: https://goo.gl/4vDQzT
website: http://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn
Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer: Ladan Hamadani (B.Sc., BA., MPH)
These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.

Views: 183827
MarinStatsLectures-R Programming & Statistics

We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.

Views: 160724
Timothy DAuria

Analysis Of Variance (ANOVA), Multiple Comparisons & Kurskal Wallis in R ;
Dataset: https://bit.ly/2RNeR0f ANOVA Explanation: https://goo.gl/QfQv9b More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT
How to conduct one way Analysis of Variance (ANOVA) in R, ANOVA Pairwise Comparison in R, (Multiple Comparisons in R), and Kruskal Wallis one-way ANOVA in R:
►►In this Tutorial you will learn to use various commands to :
► Conduct one way analysis of variance ANOVA test in R
►View ANOVA table in R
►Produce a visual display for the pair-wise comparisons of the analysis of variance in R
► Conduct multiple comparisons/ANOVA pair-wise comparisons in R
► Produce Kruskal-Wallis one-way analysis of variance using ranks with R Statistical Software.
▶︎To access and download the dataset visit https://www.statslectures.com/
■Table of Content
0:00:12 when to use one-way analysis of variance (ANOVA)
0:00:37 how to conduct ANOVA in R using the "aov" command
0:00:42 how to access the help menu in R for ANOVA commands
0:00:52 how to create a boxplot in R
0:01:42 how to view ANOVA table in R using "summary" command
0:02:07 how to ask R to let us know what is stored in an object using the "attributes" command.
0:02:23 how to extract certain attributes from an object in R using the dollar sign ($)
0:02:48 how to conduct multiple comparisons/pair-wise comparisons for the analysis of variance in R using the "TukeyHSD" command
0:03:17 how to produce a visual display for the pair-wise comparisons of the analysis of variance in R using "plot" command
0:03:50 how to produce Kruskal-Wallis one-way analysis of variance using ranks in R using the "kruskal.test" command
0:03:56 when is it appropriate to use Kruskal-Wallis one-way analysis of variance for data
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
▶︎▶︎ Watch More
▶︎ANOVA Use and Assumptions https://youtu.be/_VFLX7xJuqk
▶︎ Understanding Sum of Squares in ANOVA ,concept of analysis of variance, and ANOVA hypothesis testing https://youtu.be/-AeU4y2vkIs
▶︎ ANOVA Test Statistic and P Value: https://youtu.be/k-xZzEYL8oc
▶︎ ANOVA & Bonferroni Multiple Comparisons Correction https://youtu.be/pscJPuCwUG0
▶︎ Two Sample t test for independent groups https://youtu.be/mBiVCrW2vSU
▶︎ Paired t test https://youtu.be/Q0V7WpzICI8
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn
Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer: Ladan Hamadani (B.Sc., BA., MPH)
These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 121239
MarinStatsLectures-R Programming & Statistics

This video will introduce how to calculate statistical power in R using the pwr package.
All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/

Views: 6062
Center for Open Science

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: 4390
Udacity

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] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004

Views: 42880
edureka!

Stephan Reimann + Wilfried Hoge
http://ibm.biz/joinIBMCloud
http://www.meetup.com/Cloud-Scale-Data-Science-virtual-UserGroup-worldwide/

Views: 2152
Romeo Kienzler

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: 485
Udacity

This is Video 3 of series of video casts on bioinformatics through R language. This video shows as how to perform analysis on GC content in RStudio using R Language.

Views: 998
Social Research Insights

In this text analytics with R video, I've talked about how you can analyze twitter data for doing sentiment analysis. Here I've taken an example of US President Donald Trump and analyze the tweets that general public is tweeting about him and then categorize the tweets in positive and negative tweets and create a wordcloud of it to better visualize the data.
Text analytics with R,Sentiment Analysis on twitter data,how to analyze tweets in R,r sentiment anlaysis,sentiment analysis in r,r twitter data analysis,analyzing twitter data in R,twitter sentiment analysis,analyzing sentiments from tweets,example of sentiment analysis in r,r sentiment analysis tutorial,r twitter tutorial,sentiment analysis of twitter data in R,how to analyze sentiments of twitter data,R Text analytics tutorial,step by step text analytics in R

Views: 2238
Data Science Tutorials

Analytics Accelerator Program- May 2016-July 2016 Batch

Views: 1833
Equiskill Insights LLP

Views: 2289
Udacity

Views: 1764
Erin Berry

Make sure to like & comment if you liked this video!
Take Hank's course here: https://www.datacamp.com/courses/unsupervised-learning-in-r
Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.
Transcript:
Hi! I'm Hank Roark, I'm a long-time data scientist and user of the R language, and I'll be your instructor for this course on unsupervised learning in R.
In this first chapter I will define ‘unsupervised learning’, provide an overview of the three major types of machine learning, and you will learn how to execute one particular type of unsupervised learning using R.
There are three major types of machine learning. The first type is unsupervised learning. The goal of unsupervised learning is to find structure in unlabeled data. Unlabeled data is data without a target, without labeled responses.
Contrast this with supervised learning. Supervised learning is used when you want to make predictions on labeled data, on data with a target.
Types of predictions include regression, or predicting how much of something there is or could be, and classification which is predicting what type or class some thing is or could be.
The final type is reinforcement learning, where a computer learns from feedback by operating in a real or synthetic environment.
Here is a quick example of the difference between labeled and unlabeled data. The table on the left is an example with three observations about shapes, each shape with three features, represented by the three columns. This table, the one on the left is an example of unlabeled data. If an additional vector of labels is added, like the column of labels on the right hand side, labeling each observation as belonging to one of two groups, then we would have labeled data.
Within unsupervised learning there are two major goals. The first goal is to find homogeneous subgroups within a population. As an example let us pretend we have a population of six people. Each member of this population might have some attributes, or features — some examples of features for a person might be annual income, educational attainment, and gender. With those three features one might find there are two homogeneous subgroups, or groups where the members are similar by some measure of similarity. Once the members of each group are found, we might label one group subgroup A and the other subgroup B. The process of finding homogeneous subgroups is referred to as clustering.
There are many possible applications of clustering. One use case is segmenting a market of consumers or potential consumers. This is commonly done by finding groups, or clusters, of consumers based on demographic features and purchasing history. Another example of clustering would be to find groups of movies based on features of each movie and the reviews of the movies. One might do this to find movies most like another movie.
The second goal of unsupervised learning is to find patterns in the features of the data. One way to do this is through ‘dimensionality reduction’. Dimensionality reduction is a method to decrease the number of features to describe an observation while maintaining the maximum information content under the constraints of lower dimensionality.
Dimensionality reduction is often used to achieve two goals, in addition to finding patterns in the features of the data.
Dimensionality reduction allows one to visually represent high dimensional data while maintaining much of the data variability. This is done because visually representing and understanding data with more than 3 or 4 features can be difficult for both the producer and consumer of the visualization.
The third major reason for dimensionality reduction is as a preprocessing step for supervised learning. More on this usage will be covered later.
Finally a few words about the challenges and benefits typical in performing unsupervised learning.
In unsupervised learning there is often no single goal of the analysis. This can be presented as someone asking you, the analyst, “to find some patterns in the data.” With that challenge, unsupervised learning often demands and brings out the deep creativity of the analyst.
Finally, there is much more unlabeled data than labeled data. This means there are more opportunities to apply unsupervised learning in your work.
Now it's your turn to practice what you've learned.

Views: 2111
DataCamp

This was from about 25 call recordings at a call center that were transcribed from speech to text using the Google Speech API. Once I transcribed the calls - I converted them into plain text (i.e. rtf to txt) and just dropped them in a folder and then used R to play with the text. Basically these packages and R scripts just take the data- put it in a friendly format and clean it up and then fondle it to bring some very general but meaningful insight. You can use this to see what types of calls you are getting, to find correlations with the calls and groupings with the cluster, and to see words that are most often used as well as toggle the thresholds to make it more strict or loose in finding words used more often/less.
I will be sharing my scripts at some point, as they are very basic at the moment.
But to reproduce this, get your R studio and the libraries needed and run this script here: https://github.com/thesmarthomeninja/
Under the Text Analysis Repo (not the twitter repo). Keep in mind you need your own text to analyze as I can't share that content. Although there are libraries with books and example text datasets if you google tidytext examples and documents online.
Also, I'm working on putting together a blog to share this content or sub-domain off my website: https://www.thesmarthomeninja.com
You can follow me on any social platform under the handle- The Smart Home Ninja. Subscribe for more content to come!

Views: 1823
The Smart Home Ninja

Download FREE CD http://www.sendspace.com/file/pqzs5a CLICK TO DOWNLOAD

Views: 109
Doller Go Getters

In this video Philip demonstrates the R code needed for SAP HANA to predict future values with a loss triangle using the chain ladder algorithm.
All content from this project is available on Github https://github.com/saphanaacademy/Live4Insurance
Video by the SAP HANA Academy

Views: 3704
SAP HANA Academy

Thematic coding is one of the most common forms of qualitative data analysis and it is found in grounded theory, several forms of phenomenological analysis and framework analysis. The analyst tries to identify themes, categories or classifications of the data. Passages of the data (commonly an interview transcript) are coded to the themes - that is the passages are tagged or marked with the name of the theme.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
http://creativecommons.org/licenses/by-nc-sa/4.0/

Views: 177290
Graham R Gibbs

Note: Package "SocialMediaLab" is now renamed as "vosonSML"
R File: https://goo.gl/4gpVdp
YouTube data File: https://goo.gl/2p8V9L
Includes,
- Obtaining Google developer API key
- Collecting data using YouTube video IDs
- Saving and reading YouTube data file
- Creating user network
- Histogram of node degree
- YouTube user network diagram
- Sentiment analysis of YouTube user comments
- Obtaining sentiment scores
- Sentiment visualization
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: 5543
Bharatendra Rai

R-Squared or Coefficient of Determination
Watch the next lesson: https://www.khanacademy.org/math/probability/regression/regression-correlation/v/calculating-r-squared?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/regression/regression-correlation/v/second-regression-example?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to KhanAcademy’s Probability and Statistics channel:
https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1
Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 579925
Khan Academy

Importing Data, Checking the Imported Data and Working With Data in R; Dataset: https://goo.gl/tJj5XG
More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT
How to import a datasets into R , How to make sure data was imported correctly into R and How to begin to work with the imported data in R.
▶︎We will learn to use read.table function (which reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file), and some of the arguments such as header argument and sep argument.
▶︎We will learn to use file.choose function to choose a file interactively
▶︎We will discuss how to use Menu options in RStudio to import data into R
▶︎and how to check the imported data to make sure it was imported correctly into R using the dim function to retrieve dimension of an object and let you know the number of rows and columns of the imported data, the head function in R (head() function), which returns the first or last parts of a vector, matrix, table, data frame and will let you see the first several rows of the data, the tail function in R (tail() function) to see the last several rows of the data in R, the double square brackets in R to subset data (brackets lets you select or subset data from a vector, matrix, array, list or data frame) , and the names function in R to get the names of an object in R.
▶︎▶︎ Download the dataset here:
https://statslectures.com/r-stats-datasets
▶︎▶︎Watch More
▶︎Export Data from R (CSV , TXT and other formats): https://bit.ly/2PWS84w
▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg
▶︎Probability Distributions in R: https://bit.ly/2AT3wpI
▶︎Bivariate Analysis in R: https://bit.ly/2SXvcRi
▶︎Linear Regression in R: https://bit.ly/1iytAtm
▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH
◼︎ Topics in the video:
0:00:07 How to read a dataset into R using read.table function and save it as an object
0:00:27 How to access the help menu in R
0:01:02 How to let R know that the first row of our data is headers by using header argument
0:01:14 How to let R know how the observations are separated by using sep argument
0:02:03 How to specify the path to the file using file.choose function
0:03:15 How to use Menu options in R Studio to import data into R
0:05:23 How to prepare the Excel data for importing into R
0:06:15 How to know the dimensions (the number of rows and columns) of the data in R using the dim function
0:06:35 How to see the first several rows of the data using the head command in R
0:06:45 How to see the last several rows of the data in R using the tail function
0:07:18 How to check if the data was read correctly into R using square brackets and subsetting data
0:08:21 How to check the variable names in R using the names function
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
The #RTutorial is created by #marinstatslectures to support the statistics course (SPPH400 #IntroductoryStatistics) at The University of British Columbia(UBC) although we make all videos available to the everyone everywhere for free!
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 341305
MarinStatsLectures-R Programming & Statistics

http://goo.gl/LcUcg2
Big data is changing the competitive landscape
Those who are in a position to take advantage of it often get to market faster with products and services that are better aligned with customer needs and desires.
BigDataTraining.IN is the brain child of leading Big Data Architects & Consultants in India - with extensive approach on Job Oriented Hands-On Sessions Followed with placement
We believe in quality training which will enable the individuals skillfully effecient & be in phase with the Cutting Edge Technologies in the IT Space
We are now connected with numerous Corporates to fill up the positions which the Industry finds difficult to find the right resource for the Trending jobs.
http://goo.gl/P5nQis
BigDataTraining.IN Benefits!
Learn from Cloud & Big Data Architects
Learn from the experts who deal with real world scenarios, Founders & Lead Architects of various Successful Cloud & big Data Projects.
Hands-On Sessions
Big Data Academy focuses on practical oriented training than just theoritical, We being Technology consultants & Architects can address any kind of technical support required,
Cloud Server Access
We believe in the fact - some thing learnt should be practiced
We compliment that with Cloud Server access, which provide you un-interrupted access to Our Development Servers on the Cloud.
.
http://goo.gl/j3F33I
Learn Real Big data Techniques
Learn the core techniques & Concepts of Big Data & whole Hadoop Eco system - rahter just theoritical overview
We are the founders & Lead Architects of successful Big Data products on Big data Analytics, Market Intelligence, Sentiment Analysis, Financial Data Analysis, Fraud Analysis, Intrusion Detection and many more
.
http://goo.gl/NrTO8O http://www.bigdatatraining.in/hadoop-development/
WebSite:
http://www.bigdatatraining.in
Mail:
[email protected]
Call:
+91 9789968765
044 - 42645495
Weekdays / Fast Track / Weekends / Corporate Training modes available
Our Trainings Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati & Chennai
On-Demand Fast track Trainings globally available also at Singapore, Dubai, Malaysia, London, San Jose, Beijing, Shenzhen, Shanghai, Ho Chi Minh City, Boston, Wuhan, San Francisco, Chongqing

Views: 0
Harish Kumar

http://goo.gl/LcUcg2
Big data is changing the competitive landscape
Those who are in a position to take advantage of it often get to market faster with products and services that are better aligned with customer needs and desires.
BigDataTraining.IN is the brain child of leading Big Data Architects & Consultants in India - with extensive approach on Job Oriented Hands-On Sessions Followed with placement
We believe in quality training which will enable the individuals skillfully effecient & be in phase with the Cutting Edge Technologies in the IT Space
We are now connected with numerous Corporates to fill up the positions which the Industry finds difficult to find the right resource for the Trending jobs.
http://goo.gl/P5nQis
BigDataTraining.IN Benefits!
Learn from Cloud & Big Data Architects
Learn from the experts who deal with real world scenarios, Founders & Lead Architects of various Successful Cloud & big Data Projects.
Hands-On Sessions
Big Data Academy focuses on practical oriented training than just theoritical, We being Technology consultants & Architects can address any kind of technical support required,
Cloud Server Access
We believe in the fact - some thing learnt should be practiced
We compliment that with Cloud Server access, which provide you un-interrupted access to Our Development Servers on the Cloud.
.
http://goo.gl/j3F33I
Learn Real Big data Techniques
Learn the core techniques & Concepts of Big Data & whole Hadoop Eco system - rahter just theoritical overview
We are the founders & Lead Architects of successful Big Data products on Big data Analytics, Market Intelligence, Sentiment Analysis, Financial Data Analysis, Fraud Analysis, Intrusion Detection and many more
.
http://goo.gl/NrTO8O http://www.bigdatatraining.in/hadoop-development/
WebSite:
http://www.bigdatatraining.in
Mail:
[email protected]
Call:
+91 9789968765
044 - 42645495
Weekdays / Fast Track / Weekends / Corporate Training modes available
Our Trainings Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati & Chennai
On-Demand Fast track Trainings globally available also at Singapore, Dubai, Malaysia, London, San Jose, Beijing, Shenzhen, Shanghai, Ho Chi Minh City, Boston, Wuhan, San Francisco, Chongqing

Views: 0
satya parthipan

General presentation of WordStat 7 features to categorize information with taxonomies or to explore text data using text mining approach

Views: 1918
Provalis Research - Text Analytics Software

Paper: Advanced Data Analysis
Module: Missing Data Analysis : An application of EM ALgorithm in R
Content Writer: Souvik Bandyopadhyay

Views: 3896
Vidya-mitra

http://goo.gl/LcUcg2
Big data is changing the competitive landscape
Those who are in a position to take advantage of it often get to market faster with products and services that are better aligned with customer needs and desires.
BigDataTraining.IN is the brain child of leading Big Data Architects & Consultants in India - with extensive approach on Job Oriented Hands-On Sessions Followed with placement
We believe in quality training which will enable the individuals skilfully efficient & be in phase with the Cutting Edge Technologies in the IT Space
We are now connected with numerous Corporates to fill up the positions which the Industry finds difficult to find the right resource for the Trending jobs.
http://goo.gl/P5nQis
BigDataTraining.IN Benefits!
Learn from Cloud & Big Data Architects
Learn from the experts who deal with real world scenarios, Founders & Lead Architects of various Successful Cloud & big Data Projects.
Hands-On Sessions
Big Data Academy focuses on practical oriented training than just theoritical, We being Technology consultants & Architects can address any kind of technical support required,
Cloud Server Access
We believe in the fact - some thing learnt should be practiced
We compliment that with Cloud Server access, which provide you un-interrupted access to Our Development Servers on the Cloud.
.
http://goo.gl/j3F33I
Learn Real Big data Techniques
Learn the core techniques & Concepts of Big Data & whole Hadoop Eco system - rahter just theoritical overview
We are the founders & Lead Architects of successful Big Data products on Big data Analytics, Market Intelligence, Sentiment Analysis, Financial Data Analysis, Fraud Analysis, Intrusion Detection and many more
.
http://goo.gl/NrTO8O http://www.bigdatatraining.in/hadoop-development/
WebSite:
http://www.bigdatatraining.in
Mail:
[email protected]
Call:
+91 9789968765
044 - 42645495
Weekdays / Fast Track / Weekends / Corporate Training modes available
Our Trainings Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati & Chennai
On-Demand Fast track Trainings globally available also at Singapore, Dubai, Malaysia, London, San Jose, Beijing, Shenzhen, Shanghai, Ho Chi Minh City, Boston, Wuhan, San Francisco, Chongqing.

Views: 4
aasharna raghu

Polynomial Regression in R: How to fit polynomial regression models in R;
Download Dataset & R Script:https://goo.gl/tJj5XG More Statistics and R Programming Tutorials here: https://goo.gl/4vDQzT
Complete Linear Regression Playlist: Coming soon
How to fit polynomial regression models in R and assess polynomial regression models using the partial F-test with R.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable X and the dependent variable Y is modelled as an nth degree polynomial in x. Polynomial regression models are useful when the relationship between the independent variables(X) and the dependent variables(Y) is not linear.
Download LungCapData2 Dataset and Polynomial Regression R Script
http://statslectures.com/index.php/r-stats-datasets
Table of Content:
coming soon
This video provides a tutorial for programming in R Statistical Software and RStudio for beginners.
Watch More:
Intro to Statistics Course: https://bit.ly/2SQOxDH
Getting Started with R: https://bit.ly/2PkTneg
Graphs and Descriptive Statistics in R:
Probability distributions in R: https://bit.ly/2AT3wpI
Bivariate analysis in R: https://bit.ly/2SXvcRi
Linear Regression in R: https://bit.ly/1iytAtm
ANOVA series https://bit.ly/2zBwjgL
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn
Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
The #RTutorial is created by #marinstatslectures to support the statistics course (SPPH400 #IntroductoryStatistics) at The University of British Columbia(UBC) although we make all videos available to the everyone everywhere for free!
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 26950
MarinStatsLectures-R Programming & Statistics

Paper: Regression Analysis III
Module:The GLM function in R
Content Writer: Sayantee Jana/ Sujit Ray

Views: 7063
Vidya-mitra

Critical thinking project washington state university

Paper presentation topics for ece in ieee format reference

Merrill lynch ric report september 2019

Nanotechnology work term report guidelines

© 2018 How to start mobile phone business

Divide and Conquer. This is another area that is very industry-dependent, but it is highly unlikely that any company can afford to have an entire contract team devoted to managing one portfolio. More than likely, it is more realistic to divvy up the team and the contracts so that there is a leader for each relevant sphere. The entire team will obviously have to coordinate and communicate, but resources must be allocated in the most efficient manner possible. In turn, this will allow for several individuals to keep an eye on a smaller batch of contracts, thereby facilitating those periodic reviews. Outsource the Tedium to Technology.