Advanced Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 3: Using R to plot data
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/8yXNiM
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 3908
WekaMOOC

Shows steps for creating a R markdown file in a html, pdf or word format.
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: 7452
Bharatendra Rai

( R Training : https://www.edureka.co/r-for-analytics )
This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Why do we need Analytics ?
2. What is Business Analytics ?
3. Why R ?
4. Variables in R
5. Data Operator
6. Data Types
7. Flow Control
8. Plotting a graph in R
Check out our R Playlist: https://goo.gl/huUh7Y
Subscribe to our channel to get video updates. Hit the subscribe button above.
#R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming
How it Works?
1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you:
1. Understand concepts around Business Intelligence and Business Analytics
2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
3. Apply various supervised machine learning techniques
4. Perform Analysis of Variance (ANOVA)
5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
6. Use various packages in R to create fancy plots
7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
- - - - - - - - - - - - - - - - - - -
Who should go for this course?
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
- - - - - - - - - - - - - - - -
Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career
Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 472558
edureka!

Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub.
NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.
Blog: http://daveondata.com
GitHub: https://github.com/EasyD/IntroToDataScience
I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc

Views: 970245
David Langer

In this data science text analytics with R tutorial, I have talked about how you can analyze the sentiments from text using box plot chart in R. It helps us comparing sentiments of multiple texts or speeches or books to better analyze the sentiments from it.
Text mining in R is done with help of sentimentr package and tm package.
Text analytics with R,analyzing sentiments with boxplot chart,data science tutorial,boxplot chart,plotting sentiments,sentiment analysis in R,sentiment analysis with R,how to analyzing text in R,text processing in R,natural languge processing,NLP,nlp in R,r nlp,nlp anlaysis in R,what is text mining,how to do text mining in R,how to do NLP in R,NLP processing in R,process nlp in R,R tutorial for beginners,beginners tutorial for R,learn NLP using R

Views: 553
Data Science Tutorials

Advanced Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 5: Using R to preprocess data
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/8yXNiM
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 1974
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 6: Infrared data from soil samples
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/JyCK84
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2034
WekaMOOC

Learn how to Create Word document from R Data Programming Language.

Views: 2178
DevNami

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/2lXhDAx].
The aim of this video is to show how powerful R is as a data language. We will query an internal example dataset and show how it can be filtered and aggregated on.
• Learn about the structure of the internal mtcars dataset
• Filter on the dataset
• Aggregate on the dataset
For the latest Big Data and Business Intelligence video tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 851
Packt Video

Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST.
Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform.
We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection.
We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more!
Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom
At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected]
This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course

Views: 3821
KNIMETV

Data Science & Machine Learning -Creating a Shiny App- DIY- 43 -of-50
Do it yourself Tutorial
by
Bharati DW Consultancy
cell: +1-562-646-6746 (Cell & Whatsapp)
email: [email protected]
website: http://bharaticonsultancy.in/
Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU
Deploying you R application with Shiny
Shiny is an open source R package, which combines the computational power of R with the interactivity of the modern web.
It provides a powerful web framework for building web applications using R.
Shiny helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.
Enables standalone apps on a webpage or embed them in R Markdown documents or build dashboards.
Shiny Code Components
To use Shiny, initiate library.
There are two components of the code – ui and server.
You can create a single app.R with ui and server codes or you separately in ui.R and Server.R
Hands On – R Machine Learning Ex-19
Create a basic structure of a Shiny app.
Goto https://shiny.rstudio.com/ and click on getting started – read more about Shiny apps.
Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes, Apriori, Shiny

Views: 4198
BharatiDWConsultancy

Reference: (Book)
An Introduction to Statistical Learning with Applications in R
(Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani)
http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf
Reference (Lecture Notes)
[1] With permission from Dr. Tibshirani and Dr. Hastie, the Lecture notes are adopted from Stanford-Online StatLearning Statistical Learning
[2] With permission from Dr. Al Sharif (USC) part of the Lecture notes were adopted from "DSO 530: Applied Modern Statistical Learning Techniques".

Views: 34804
Hamed Hasheminia

In this video you will learn how to import your flat files into R.
Want to take the interactive coding exercises and earn a certificate? Join DataCamp today, and start our intermediate R tutorial for free: https://www.datacamp.com/courses/importing-data-into-r
In this first chapter, we'll start with flat files. They're typically simple text files that contain table data. Have a look at states.csv, a flat file containing comma-separated values. The data lists basic information on some US states. The first line here gives the names of the different columns or fields. After that, each line is a record, and the fields are separated by a comma, hence the name comma-separated values. For example, there's the state Hawaii with the capital Honolulu and a total population of 1.42 million.
What would that data look like in R? Well, actually, the structure nicely corresponds to a data frame in R, that ideally looks like this: the rows in the data frame correspond to the records and the columns of the data frame correspond to the fields. The field names are used to name the data frame columns. But how to go from the CSV file to this data frame?
The mother of all these data import functions is the read.table() function. It can read in any file in table format and create a data frame from it. The number of arguments you can specify for this function is huge, so I won't go through each and every one of these arguments. Instead, let's have a look at the read.table() call that imports states.csv and try to understand what happens.
The first argument of the read.table() function is the path to the file you want to import into R. If the file is in your current working directory, simply passing the filename as a character string works.
If your file is located somewhere else, things get tricky. Depending on the platform you're working on, Linux, Microsoft, Mac, whatever, file paths are specified differently. To build a path to a file in a platform-independent way, you can use the file.path() function.
Now for the header argument. If you set this to TRUE, you tell R that the first row of the text file contains the variable names, which is the case here. read.table() sets this argument FALSE by default, which would mean that the first row is already an observation.
Next, sep is the argument that specifies how fields in a record are separated. For our csv file here, the field separator is a comma, so we use a comma inside quotes.
Finally, the stringsAsFactors argument is pretty important. It's TRUE by default, which means that columns, or variables, that are strings, are imported into R as factors, the data structure to store categorical variables. In this case, the column containing the country names shouldn't be a factor, so we set stringsAsFactors to FALSE.
If we actually run this call now, we indeed get a data frame with 5 observations and 4 variables, that corresponds nicely to the CSV file we started with.
The read table function works fine, but it's pretty tiring to specify all these arguments every time, right? CSV files are a common and standardized type of flat files. That's why the utils package also provides the read.csv function. This function is a wrapper around the read.table() function, so read.csv() calls read.table() behind the scenes, but with different default arguments to match with the CSV format.
More specifically, the default for header is TRUE and for sep is a comma, so you don't have to manually specify these anymore. This means that this read.table() call from before is thus exactly the same as this read.csv() call.
Apart from CSV files, there are also other types of flat files. Take this tab-delimited file, states.txt, with the same data:
To import it with read.table(), you again have to specify a bunch of arguments. This time, you should point to the .txt file instead of the .csv file, and the sep argument should be set to a tab, so backslash t.
You can also use the read.delim() function, which again is a wrapper around read.table; the default arguments for header and sep are adapted, among some others. The result of both calls is again a nice translation of the flat file to a an R data frame.
Now, there's one last thing I want to discuss here. Have a look at this US csv file and its european counterpart, states_eu.csv.
You'll notice that the Europeans use commas for decimal points, while normally one uses the dot. This means that they can't use the comma as the field-delimiter anymore, they need a semicolon. To deal with this easily, R provides the read.csv2() function. Both the sep argument as the dec argument, to tell which character is used for decimal points, are different. Likewise, for read.delim() you have a read.delim2() alternative. Can you spot the differences again? This time, only the dec argument had to change.

Views: 50374
DataCamp

Advanced Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 4: Using R to run a classifier
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/8yXNiM
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2793
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 6: Application to Bioinformatics – Signal peptide prediction
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/4vZhuc
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2944
WekaMOOC

More Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 6: Working with big data
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/Le602g
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 10271
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 2: Setting up R with Weka
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/8yXNiM
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 6551
WekaMOOC

A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis

Views: 173695
APMonitor.com

Demonstrating how to create a basic R package in RStudio using devtools and roxygen2.

Views: 73683
trestletech

Import Data, Copy Data from Excel (or other spreadsheets) to R CSV & TXT Files; Practice with Dataset: (https://bit.ly/2rOfgEJ)
More Statistics and R Programming Tutorials: https://bit.ly/2Fhu9XU
How to Import CSV data into R or How to Import TXT files into R from Excel or other spreadsheets using function in R
►How to import CSV data into R? We will be using "read.table" function to import comma separated data into R
► How to import txt data file into R? You will learn to use "read.delim" function to import the tab-delimited text file into R
► You will also learn to use "file.choose" argument for file location, "header" argument to let R know the data has headers or variable names and "sep" argument to let R know how the data values are separated.
►►Download the dataset here: https://statslectures.com/r-scripts-datasets
►►Like to support us? You can Donate https://bit.ly/2CWxnP2 or Share the Videos!
►► Watch More:
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►R Tutorials for Data Science https://bit.ly/1A1Pixc
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
►ANOVA series https://bit.ly/2zBwjgL
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
►Puppet Master of Statistics: https://bit.ly/2RDAAv4
►Hypothesis Testing: Concepts in Statistics https://bit.ly/2Ff3J9e
◼︎ Table of Content
0:00:17 What are the two main file types for saving a data file (CSV and TXT)
0:00:36 How to save an Excel file as a CSV file (comma-separated value)
0:01:10 How to open a CSV data file in Excel
0:01:20 How to open a CSV file in a text editor
0:01:36 How to import CSV file into R? using read.csv function
0:01:44 How to access the help menu for different commands/functions in R
0:02:04 How to specify file location for R? using file.choose argument on read.csv function
0:02:31 How to let R know our data has headers or variable names when importing the data into R? By using the “header” argument on read.csv function
0:03:22 How to import CSV file into R? using read.table function
0:03:38 How to specify the file location for the read.table function in R? using file.choose argument
0:03:46 How to specify how variables/columns are separated when importing data into R? the "sep" argument on read.table function will do that; for example if you don't specify that your data is comma separated, R ends up reading it all in as one variable
0:04:10 How to save a file in Excel as tab-delimited text (TXT) file
0:04:50 How to open a tab-delimited (.TXT) data file in a text editor
0:05:07 How to open a tab-delimited (.TXT) data file in excel
0:05:20 How to import tab-delimited (.TXT) data file into R? using read.delim function
0:05:44 How to specify the file path for read.delim function in R? using file.choose argument
0:06:06 How to import tab-delimited (.TXT) data file into R? using read.table function
0:06:23 How to specify that the data has headers or variable names when importing the data into R? using header argument on read.table 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
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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 videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), 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: 578134
MarinStatsLectures- R Programming & Statistics

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: 52552
Udacity

Advanced Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 5: Lag creation, and overlay data
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/JyCK84
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2666
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 1: Invoking Python from Weka
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/7XXl63
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 4708
WekaMOOC

Introduction to R Programming is an open enrollment, live, interactive online course offered by the non-profit Georgia R School (http://georgia-r-school.org). All of the videos associated with this online R course, as well as dozens of other multi-month R courses (and all scripts, files, slides, exercises and data sets) are all available, on an all-inclusive basis, through May of 2013, to anyone with an inexpensive ($100-$125) R-Courses.org user account with the non-profit Georgia R School (see http://www.georgia-r-school.org/R-Courses.html). Anyone on earth with an Internet connection may participate. A course syllabus is here: http://www.georgia-r-school.org/uploads/INTRO-PROG-GENERAL.pdf. The course runs live on Tuesdays from Oct 9, 2012 through Feb 26, 2013 from 11:30AM-1:00PM ET and utilizes R Project software (http://www.r-project.org) and the RStudio (http://rstudio.org) Integrated Development Environment (IDE). Unlike most application development courses taught in the R environment, this course instructs with respect to "general-purpose" application development, as opposed to strictly statistical-simulation-monte carlo methods-type R applications. The course instructor is a PhD 20-year university professor with 30 years of experience in the software development industry. Course participants receive all live class session recordings, as well as all daily course exercises and solutions, R scripts, data files, documentation, slides and software (is freely available at http://cran.r-project.org and http://rstudio.org). Registration will remain open throughout October at discounted costs here: http://shop.georgia-r-school.org/Introduction-to-R-Programming-Oct-Dec-GRS-PROG-1.htm.

Views: 145313
Stuar51XT

Provides steps for applying artificial neural networks to do classification and prediction.
R file: https://goo.gl/VDgcXX
Data file: https://goo.gl/D2Asm7
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- neural network model
- input, hidden, and output layers
- min-max normalization
- prediction
- confusion matrix
- misclassification error
- network repetitions
- example with binary data
neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
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: 26421
Bharatendra Rai

Advanced Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 2: Installing with Apache Spark
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/msswhT
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 2584
WekaMOOC

This video covers following topics of unit-5 of M-III:
1. Definition of Normal distribution
2. Problems on Normal distribution
For any query and feedback, please write us at:
[email protected]
OR call us at: +919301197409(Hike number)
For latest updates subscribe our channel " Bhagwan Singh Vishwakarma" or join us on Facebook "Maths Bhopal"...

Views: 322307
Bhagwan Singh Vishwakarma

An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others).
SUBSCRIBE to learn data science with Python:
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JOIN the "Data School Insiders" community and receive exclusive rewards:
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RESOURCES:
- Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/
- Visualization: http://www.navan.name/roc/
- Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf
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Views: 298869
Data School

Advanced Data Mining with Weka: online course from the University of Waikato
Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/8yXNiM
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 1415
WekaMOOC

Slides for talk: http://files.meetup.com/1685538/Causal%20Inference.pdf
Billions of dollars a year are spent subsidising tuition of Australian university students. A controversial report last year by the Grattan Institute, Graduate Winners, asked 'is this the best use of government money?'
In this talk, Jim Savage, one of the researchers who worked on the report, walks us through the process of doing the analysis in R. The talk will focus on potential pitfalls/annoyances in this sort of research, and on causal inference when all we have is observational data. He will also outline his new method of building synthetic control groups of observational data using tools more commonly associated with data mining.
Jim Savage is an applied economist at the Grattan Institute, where he has researched education policy, the structure of the Australian economy, and fiscal policy. Before that, he worked in macroeconomic modelling at the Federal Treasury.

Views: 2521
Jeromy Anglim

Statgraphics 18 is used to analyze 9 famous speeches. It uses the tm Text Mining library in R to construct a document-term matrix, which is then used to create a wordcloud. A comparison of 2 speeches is also shown using a tornado/bufferfly plot. For more examples and information on this procedure, please visit our website: http://www.statgraphics.com/data-mining.

Views: 347
Statgraphics

statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

Views: 416163
statslectures

Hi guys....in this tableau tutorial video I have talked about how you can integrate tableau with R also I have given an example of tableau r integration to better understand this.
For any tableau training, tableau consulting and tableau freelancing for tableau dashboard development please reach out to my email [email protected]

Views: 500
Abhishek Agarrwal

Advanced Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 2: Linear regression with lags
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/JyCK84
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 8396
WekaMOOC

Python and R are the preferred languages for data science. In 2018, RStudio introduced its package reticulate and clearly demonstrates that it favours to join forces. Both languages have strengths and weaknesses. Tools to combine the strengths will enable easier collaboration in projects and more possibilities to succeed. Using Python from R gives R users wider access to functions and makes it easier for Python beginners to just run scripts and being able to collaborate in Python projects. The talk will show the possibilities of reticulate: The main part starts with demonstrating the Python interpreter within R. It will show how to source Python scripts as well as install and import modules. Then it will deal with the most important types of Python objects, how they are represented in R and how to further manipulate them. Thereby, a special focus is on using Python for data science. In addition, it will be presented how Conda environments can be created and used from R. A further application will be the creation of reports with Markdown and LaTeX where R and Python can be used within one document and share objects. A last topic is about showing the possibilities for easier development in RStudio (help regarding Python functions, auto completion).

Views: 317
PyConDE

Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 3: Repeated training and testing
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/D3ZVf8
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 45486
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 1: Incremental classifiers in Weka
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/4vZhuc
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 3255
WekaMOOC

Get The Complete MATLAB Course Bundle for 1 on 1 help!
https://josephdelgadillo.com/product/matlab-course-bundle/
Enroll in the FREE Uthena course!
https://uthena.com/courses/matlab?coupon=youtube&ref=744aff
Time Stamps
00:51 What is Matlab, how to download Matlab, and where to find help
07:52 Introduction to the Matlab basic syntax, command window, and working directory
18:35 Basic matrix arithmetic in Matlab including an overview of different operators
27:30 Learn the built in functions and constants and how to write your own functions
42:20 Solving linear equations using Matlab
53:33 For loops, while loops, and if statements
1:09:15 Exploring different types of data
1:20:27 Plotting data using the Fibonacci Sequence
1:30:45 Plots useful for data analysis
1:38:49 How to load and save data
1:46:46 Subplots, 3D plots, and labeling plots
1:55:35 Sound is a wave of air particles
2:05:33 Reversing a signal
2:12:57 The Fourier transform lets you view the frequency components of a signal
2:27:25 Fourier transform of a sine wave
2:35:14 Applying a low-pass filter to an audio stream
2:43:50 To store images in a computer you must sample the resolution
2:50:13 Basic image manipulation including how to flip images
2:57:29 Convolution allows you to blur an image
3:02:51 A Gaussian filter allows you reduce image noise and detail
3:08:55 Blur and edge detection using the Gaussian filter
3:16:39 Introduction to Matlab & probability
3:19:47 Measuring probability
3:26:53 Generating random values
3:35:40 Birthday paradox
3:43:25 Continuous variables
3:48:00 Mean and variance
3:55:24 Gaussian (normal) distribution
4:03:21 Test for normality
4:10:32 2 sample tests
4:16:28 Multivariate Gaussian

Views: 1125332
Joseph Delgadillo

Using R for Spatial Analysis, PDF available here: http://gis.harvard.edu/events/seminar-series/using-r-spatial-analysis-example-mapping-forest-biomass

Views: 2708
Harvard CGA

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: 66978
deltaDNA

A tutorial on how to build a treemap graph in R and refining it using Illustrator, utilizing a dataset used in an article by The New York Times on Feb. 25, 2007 -- treemap visualization by Amanda Cox. Although described in Chapter 5 of the book Visualize This by Nathan Yau, this tutorial used a different package (treemap with RColorBrewer) instead of "portfolio". The PDF of the tutorial with the related links and files can be found here: http://bit.ly/1qPzKCt
Disclaimer: I am not a programmer and my descriptions and terminology are probably off here and there. The viewer is encouraged to consult the original book and other sources on R on the web. In the example I replicate a detail from the original NY Times treemap, complete with key (100K and 25K rectangles) and color scale (diverging color ramp). Many thanks to Amanda Cox, graphics editor at the NY Times, for the suggestion to use "bucket" to customize the color ramp. The key with the rectangles was built with a separate scrap file. The book Visualize This is used as a textbook in my Information Design: Data Visualization class at San Francisco State University. In my tutorial RStudio is used as a gentler interface for design students with minimal programming experience.

Views: 12421
Pino Trogu

Advanced Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 6: Application: Image classification
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/msswhT
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 8228
WekaMOOC

Advanced Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 4: MOA classifiers and streams
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/4vZhuc
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 3001
WekaMOOC

R Shiny Tutorial | Shiny and brushedPoints | remove outlier data points from plot using brush (2)
Best viewed in full screen
Link to the code files
https://github.com/aagarw30/R-Shinyapp-Tutorial/blob/master/Shiny_and_brushedpoints/2_brushed_points_remove_datapoints.R

Views: 635
Abhinav Agrawal

The interest in the (quantitative) analysis of textual data has increased considerably over the last few years. For researchers investigating the scholarly literature the full text archive of JSTOR (http://www.jstor.org) offers a rich and diverse set of journal articles and other texts. Through its service Data for Research (http://www.jstor.org/dfr/), JSTOR gives researchers the opportunity to analyse this data, by delivering metadata, n-grams and, upon special request, full-text materials. jstor (https://tklebel.github.io/jstor/) enables researchers to easily import the supplied metadata to R. These metadata can either be analysed on their own, or be used in conjunction with n-grams or full-text-data. The presentation will show how jstor supports investigations of scholarly literature, covering the analysis of n-grams and citation analysis. Besides introducing possible applications, the paper will also discuss limitations regarding data quality and possible solutions thereof.

Views: 345
R Consortium

Advanced Data Mining with Weka: online course from the University of Waikato
Class 2 - Lesson 2: Weka’s MOA package
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/4vZhuc
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 3014
WekaMOOC

Reading in data to Rstudio, specifically files that are csv, txt, and excel.

Views: 300
James Dayhuff

Advanced Data Mining with Weka: online course from the University of Waikato
Class 1 - Lesson 1: Introduction
http://weka.waikato.ac.nz/
Slides (PDF):
https://goo.gl/JyCK84
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 7277
WekaMOOC