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R For Qualitative Analysis
 
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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: 4152 Eren Kavvas
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 62422 deltaDNA
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 11116 Jinsuh Lee
Coding Part 1: Alan Bryman's 4 Stages of qualitative analysis
 
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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: 190061 Graham R Gibbs
Analysis of Variance (ANOVA) and Multiple Comparisons in R (R Tutorial 4.6)
 
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Analysis Of Variance (ANOVA) and Multiple Comparisons in R ; Find Find more #Statistics and #R ProgrammingTutorials here: https://goo.gl/4vDQzT ; tutoriasl for #Analysis_Of_Variance #ANOVA here: https://goo.gl/4Uswyo Tutorial on how to conduct one way Analysis Of Variance (ANOVA) and #KruskalWallis one-way ANOVA. You will also learn how to conduct multiple comparisons using #TukeyHSD command as well as "aov", "summary", "plot", "attributes" and "kruskal.test" commands. ▶︎To access and download the dataset visit https://www.statslectures.com/ Here is a quick overview of the topics addressed in this video: 0:00:12 when is it appropriate to use one-way analysis of variance (ANOVA) 0:00:37 how to conduct an analysis of variance in R using the "aov" command 0:00:42 how to access the help menu in R for one-way analysis of variance (ANOVA) 0:00:52 how to create a boxplot for the data in R 0:01:03 the null hypothesis in one-way analysis of variance (ANOVA) 0:01:16 how to conduct the one way analysis of variance (ANOVA) in R using the "aov" command 0:01:42 how to view #ANOVA table and other info 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. ♠︎♣︎♥︎♦︎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 videos are created by #marinstatslectures to support a course at The University of British Columbia (#UBC) although we make all videos available to the public for free.
Basic Data Analysis in RStudio
 
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This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4
Views: 113692 Ralf Becker
Sentiment Analysis in R | R Tutorial | R Analytics | R Programming | What is R | R language
 
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This tutorial will deep dive into data analysis using 'R' language. By the end of this tutorial you would have learnt to perform Sentiment Analysis of Twitter data using 'R' tool. To learn more about R, click here: http://goo.gl/uHfGbN This tutorial covers the following topics: • What is Sentiment Analysis? • Sentiment Analysis use cases • Sentiment Analysis tools • Hands-On: Sentiment Analysis in R The topics related to ‘R’ language are extensively covered in our ‘Mastering Data Analytics with R’ course. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 41466 edureka!
Missing Data Analysis : Multiple Imputation in R
 
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Paper: Advanced Data Analysis Module: Missing Data Analysis : Multiple Imputation in R Content Writer: Souvik Bandyopadhyay
Views: 18053 Vidya-mitra
Introduction to Data Science with R - Data Analysis Part 2
 
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Part 2 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 129281 David Langer
R-squared or coefficient of determination | Regression | Probability and Statistics | Khan Academy
 
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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: 564725 Khan Academy
Text Mining (part 3)  -  Sentiment Analysis and Wordcloud in R (single document)
 
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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: 18373 Jalayer Academy
Principal Component Analysis and Factor Analysis in R
 
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Principal Component Analysis and Factor Analysis in R https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis
Views: 92591 econometricsacademy
Intro to Power in R
 
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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/
Whatsapp chat sentiment analysis in R | Sudharsan
 
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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
11 Introduction to R (Programming language)
 
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This video is part of a video series by http://www.nextgenerationsequencinghq.com. It introduces the basic work flow of how to get information from your next generation sequencing data to build a bioinformatics pipeline. This video is a brief introduction to the programming language R. R is useful for sequencing analysis, as there are several tools for sequence data analysis that are packages for R.
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 9809 Stat Pharm
Sentimental Analysis in R
 
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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: 3932 Mavericks 045_049_078
Data Analytics with R Training Content
 
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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
Data Analysis in R
 
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Here are two examples of numeric and non numeric data analyses. Both files are obtained from infochimps open access online database.
Views: 38586 Ani Aghababyan
Conducting Meta Analysis in R
 
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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: 1067 Sara Locatelli
01 Introduction to RQDA
 
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This video is a introduction to RQDA. RQDA is a Qualitative Analysis software (text analysis). Free and working with R project.
Views: 25745 RQDAtuto
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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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: 157800 Timothy DAuria
R Sector - "Content Analysis" [OFFICIAL VIDEO] (SPIELBERG WOULD BE PROUD)
 
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Download FREE CD http://www.sendspace.com/file/pqzs5a CLICK TO DOWNLOAD
Views: 39467 RSectorCrew
Data Analytics with R Training Content
 
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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
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 652308 Kent Löfgren
Data Analytics with R Training Course Content
 
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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
How to easily perform text data content analysis with Excel
 
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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: 57955 etableutilities
R Data Analysis Projects: Introducing Content-Based Recommendation| packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/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: 89 Packt Video
R tutorial: Cleaning and preprocessing text
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Now that you have a corpus, you have to take it from the unorganized raw state and start to clean it up. We will focus on some common preprocessing functions. But before we actually apply them to the corpus, let’s learn what each one does because you don’t always apply the same ones for all your analyses. Base R has a function tolower. It makes all the characters in a string lowercase. This is helpful for term aggregation but can be harmful if you are trying to identify proper nouns like cities. The removePunctuation function...well it removes punctuation. This can be especially helpful in social media but can be harmful if you are trying to find emoticons made of punctuation marks like a smiley face. Depending on your analysis you may want to remove numbers. Obviously don’t do this if you are trying to text mine quantities or currency amounts but removeNumbers may be useful sometimes. The stripWhitespace function is also very useful. Sometimes text has extra tabbed whitespace or extra lines. This simply removes it. A very important function from tm is removeWords. You can probably guess that a lot of words like "the" and "of" are not very interesting, so may need to be removed. All of these transformations are applied to the corpus using the tm_map function. This text mining function is an interface to transform your corpus through a mapping to the corpus content. You see here the tm_map takes a corpus, then one of the preprocessing functions like removeNumbers or removePunctuation to transform the corpus. If the transforming function is not from the tm library it has to be wrapped in the content_transformer function. Doing this tells tm_map to import the function and use it on the content of the corpus. The stemDocument function uses an algorithm to segment words to their base. In this example, you can see "complicatedly", "complicated" and "complication" all get stemmed to "complic". This definitely helps aggregate terms. The problem is that you are often left with tokens that are not words! So you have to take an additional step to complete the base tokens. The stemCompletion function takes as arguments the stemmed words and a dictionary of complete words. In this example, the dictionary is only "complicate", but you can see how all three words were unified to "complicate". You can even use a corpus as your completion dictionary as shown here. There is another whole group of preprocessing functions from the qdap package which can complement these nicely. In the exercises, you will have the opportunity to work with both tm and qdap preprocessing functions, then apply them to a corpus.
Views: 16225 DataCamp
Text Analytics with R | How to Scrap Website Data for Text Analytics | Web Scrapping in R
 
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In this text analytics with R tutorial, I have talked about how you can scrap website data in R for doing the text analytics. This can automate the process of web analytics so that you are able to see when the new info is coming, you just run the R code and your analytics will be ready. Web scrapping in R is done by using the rvest package. Text analytics with R,how to scrap website data in R,web scraping in R,R web scraping,learn web scraping in R,how to get website data in R,how to fetch web data in R,web scraping with R,web scraping in R tutorial,web scraping in R analytics,web scraping in r rvest,web scraping and r,web scraping regex,web scraping facebook in r,r web scraping rvest,web scraping in R,web scraper with r,web scraping in r pdf,web scraping avec and r,web scraping and r
Plotting in R for Biologists -- Lesson 8: Heatmaps
 
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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: 26731 Maria Nattestad
Text Analysis For Call Center Short Demo Using R and Google Speech API
 
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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: 1503 The Smart Home Ninja
Machine Learning with R Tutorial: Identifying Clustering Problems
 
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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: 1907 DataCamp
08 Coding in RQDA (part 1)
 
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In this tutorial we'll see how to code in RQDA.
Views: 9674 RQDAtuto
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 1866 SuperDataScience
Bioinformatics through R Language - Part 3 (Calculation of GC Content)
 
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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.
Getting Logical - Data Analysis with R
 
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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: 3839 Udacity
Text mining with Voyant Tools, no R or any other coding required
 
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Please explore free and beautiful Voyant Tools that allow you to perform any text analysis or even mining - word frequency, clouds, co-occurrence (collocations), spider diagrams, context analysis - anything you dreamt of without any prior programming experience or need to buy expensive software. To those interested in reproducing what we've done and further analyzing comments to Indian political articles (dated March-April and January 2016), please use this link to get the ball rolling: http://voyant-tools.org/?corpus=0c17d82dbd8b04baae655f90db84a672 Lastly, creators of the video are eternally grateful to our Big Data class professor, who believed in us and kept us going despite any technical or analytical difficulties.
Views: 6388 Adventuruous Mind
Harmonic Analysis: Allegretto Placido (from Content) - R. van Hest
 
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This is a music theory video on the harmonic analysis (Roman numeral analysis) of Rowy van Hest's second piece from the ablum 'Content' (which you can buy on her website, see below). her website: http://composer.rowy.net/ The audio is generated using a midi-file from the album.
Views: 121 Timon de Nood
Discourse Map with qdap in R
 
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An example discourse map created in R with the discourse_map function in the qdap package: https://github.com/trinker/qdap Music by Dan-O at DanoSongs.com
Views: 529 Tyler Rinker
Webinar - WordStat 7 Content Analysis and Text Mining Software
 
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General presentation of WordStat 7 features to categorize information with taxonomies or to explore text data using text mining approach
Demo: Using R to explore Facebook Insights data
 
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In contrast to the last video, this is a lightning-fast demonstration of how you might use the R language (and the RStudio IDE) to summarise and visualise data from Facebook Insights. In this case, I’m loading all the organic reach data from a year’s worth of posts, running some quick summaries, then splitting out the Photo posts (80% of the posts from this Page are Photo posts) to do a deeper dive. I’ve not provided an VO track for this — I wanted to keep the video short!
Views: 6120 Mediaczar
Sampling Observations - Data Analysis with R
 
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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: 464 Udacity
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming in which I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
Qualitative analysis using Template Analysis: What it is and how it may be used
 
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A lecture given by Professor Nigel King Institute for Research in Citizenship and Applied Human Sciences University of Huddersfield. Filmed and edited by Graham R Gibbs. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. (http://creativecommons.org/licenses/by-nc-sa/4.0/ ) Music: ¿Que? #1 by La Tabù is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. http://freemusicarchive.org/music/La_Tab/ Image: Brick cobbles texture by Titus Tscharntke from www.public-domain-image.com The Template Analysis Website http://www.hud.ac.uk/hhs/research/template-analysis/ References mentioned in the video. • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. • Brooks, J., McCluskey, S., King, N. and Burton, A.K. (2013). Illness perceptions in the context of differing work participation outcomes: exploring the influence of significant others in persistent back pain. BMC Musculoskeletal Disorders, 14. • Brooks, J. and King, N. (2012). Qualitative psychology in the real world: the utility of Template Analysis. British Psychological Society Annual Conference, London, 18-20 April. Available at: http://eprints.hud.ac.uk/13656/ • King, N. (2012). Doing Template Analysis. In G.Symon and C.Cassell (eds.) The Practice of Qualitative Organizational Research: Core Methods and Current Challenges. London: Sage. • King, N., Bravington, A., Brooks, J., Hardy, B., Melvin, J. and Wilde, D. (2013) The Pictor Technique: a method for exploring the experience of collaborative working. Qualitative Health Research, 23 (8), 1138-1152. • King, N, Carroll, C, Newton, P & Dornan, T (2002) ‘You can’t cure it so you have to endure it’: The experience of adaptation to diabetic renal disease, Qualitative Health Research, 12 (3), 329-346. • Kirkby-Geddes, E., King, N. and Bravington, A. (2013). Social capital and community group participation: examining ‘bridging’ and ‘bonding’ in the context of a Healthy Living Centre in the UK. Journal of Community & Applied Social Psychology, 23 (4), 271-285. • Szreter, S., & Woolcock, M. (2004). Health by association? Social capital, social theory, and the political economy of health. International Journal of Epidemiology, 33, 650-667.
Views: 6802 Graham R Gibbs
Coding Part 2: Thematic coding
 
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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: 172343 Graham R Gibbs
"Business Analytics":R Programming Tutorials 2018: Statistical Analysis Using R (Part -1) | ExcelR
 
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R Studio - Statistical Analysis describes in brief about the collection, interpretation, analysis and etc. All these are explained in this video in detail.R Programming Tutorials 2018 Course Content: https://www.excelr.com/r-tool/ This video is a tutorial for programming in R Statistical Software for beginners. Part 2: https://youtu.be/xAJuUiUbH0Q Part 3: https://youtu.be/gK7FH0jyUpk About The Course: ------------------------------ R has a half of the market share in terms of analytical tools according to KD Nuggets. R is open source which makes it a first choice for students, universities & researchers. With over 2 million users world wide, the enhancements & updates to the product outsmart the pace at which the business world is upgrading. Other statistical tools can perform a lot of statistical analysis, however, R can perform ALL the statistical analysis which can be done by all other statistical tools put together. All this come for free as there is no license cost for R. A lot of technology giants including Oracle, SAS, Teradata have embraced R by incorporating integration with R and having enterprise versions of R to stay relevant in the market. R tool is not a threat for the other statistical tools but a pioneer which paves way for the other tools. There are a lot of graphical user interfaces for base R which also come for free. This adds to the already giant hold which R has on the analytical world. The R training course offered by ExcelR has depth & breadth of the programming as well as statistical knowledge. R program which spans for 20+ hours will ensure that you are market ready & have an additional edge over others who are already into statistical analysis using R. The course starts with the assumption that participants/aspirants/students are naive to programming & statistics. Hence there is no prerequisite to join this program. Only requirement is the passion to excel in the career which promises to be extremely rewarding both in terms of monetary and personal satisfaction of working in most lucrative profession. Who Should do The Course? Anyone who wants to be a data scientist & a few of such roles include: Statisticians Mathematicians Economists Professionals already working in data analytics Professionals pursuing to work in data analytics Data warehouse & Business Intelligence professionals Data visualisation professionals Software developers (Java, .Net, C, C++, etc.) Process consultants Six Sigma consultants Fresh graduates aspiring to work in corporate world Things You Will Learn… Installation Guide for Windows Installation Guide for Mac Installation Guide for Ubuntu Introduction to R Programming Essentials of R Language Data Inputs Data Frames Graphics Tables Mathematics Classical Tests Statistical Modelling Regression Analysis of Variance Analysis of Covariance Generalized Linear Models Count Data in Tables Proportion Data Binary Response Variables Generalized Additive Models Mixed-Effects Models Non-Linear Regression Meta-Analysis Bayesian Statistics Tree Models Time Series Analysis Multivariate Statistics Spatial Statistics Survival Analysis Simulation Models Changing the Look of Graphics MODES OF TRAININGS: Online Training E-learning ClassRoom For More Info contact us: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com