Outstandingly attractive scholarships are available for students willing to travel to Yorkshire. Thats where the Battle of Roses was fought by the British Royal Family.
Emphasis and spaces in email above are made by me.
Message from Dr Top i bell ow-
It is not New York but very old York, in the North of England.
The scholarships carry a tax-free stipend and financial assistance will be
given for travel expenses to and from York. Accommodation for successful
students is available on the University of York Campus.
I am hoping to put this on my pre-ordered or Amazon Wish list. The book the common people who wanted to do data mining with , but were unable to ask aloud they didnt know much. It is written by the seminal Australian authority on data mining Dr Graham Williams whom I interviewed here at https://decisionstats.com/2009/01/13/interview-dr-graham-williams/
Data Mining for the masses using an ergonomically designed Graphical User Interface.
Encourages the concept of programming with data – more than just pushing data through tools, but learning to live and breathe the data
Accessible to many readers and not necessarily just those with strong backgrounds in computer science or statistics
Details some of the more popular algorithms for data mining, as well as covering model evaluation and model deployment
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.
producing graphs alongside results. In most cases, each page or two-page spread completes a JMP task, which maximizes the book’s utility as a reference.
Marry Big Data Analytics to High Performance Computing, and you get the buzzword of this season- High Performance Analytics.
It basically consists of Parallelized code to run in parallel on custom hardware, in -database analytics for speed, and cloud computing /high performance computing environments. On an operational level, it consists of software (as in analytics) partnering with software (as in databases, Map reduce, Hadoop) plus some hardware (HP or IBM mostly). It is considered a high margin , highly profitable, business with small number of deals compared to say desktop licenses.
As per HPC Wire- which is a great tool/newsletter to keep updated on HPC , SAS Institute has been busy on this front partnering with EMC Greenplum and TeraData (who also acquired SAS Partner AsterData to gain a much needed foot in the MR/SQL space) Continue reading “High Performance Analytics”
Here are some broad guidelines for Graphs from EIA.gov , so you can say these are the official graphical guidelines of USA Gov
They can be really useful for sites planning to get into the Tableau Software/NYT /Guardian Infographic mode- or even for communities of blogs that have recurrent needs to display graphical plots- particularly since communication, statistical and design specialists are different areas/expertise/people.
Energy Information Administration Standard 2009-25
Title: Statistical Graphs
Superseded Version: Standard 2002-25
Purpose: To ensure the utility (usefulness to intended users) and objectivity (accuracy, clarity, completeness, and lack of bias) of energy information presented in statistical graphs.
Applicability: All EIA information products.
Required Actions:
Graphs should be used to show and compare changes, trends and/or relationships, and to assist users in visualizing the conclusions drawn from the data represented.
I just checked out this new software for making PMML models. It is called Augustus and is created by the Open Data Group (http://opendatagroup.com/) , which is headed by Robert Grossman, who was the first proponent of using R on Amazon Ec2.
Probably someone like Zementis ( http://adapasupport.zementis.com/ ) can use this to further test , enhance or benchmark on the Ec2. They did have a joint webinar with Revolution Analytics recently.
See Recent News for more details and all recent news.
Augustus
Augustus is a PMML 4-compliant scoring engine that works with segmented models. Augustus is designed for use with statistical and data mining models. The new release provides Baseline, Tree and Naive-Bayes producers and consumers.
There is also a version for use with PMML 3 models. It is able to produce and consume models with 10,000s of segments and conforms to a PMML draft RFC for segmented models and ensembles of models. It supports Baseline, Regression, Tree and Naive-Bayes.
Augustus is written in Python and is freely available under the GNU General Public License, version 2.
Predictive Model Markup Language (PMML) is an XML mark up language to describe statistical and data mining models. PMML describes the inputs to data mining models, the transformations used to prepare data for data mining, and the parameters which define the models themselves. It is used for a wide variety of applications, including applications in finance, e-business, direct marketing, manufacturing, and defense. PMML is often used so that systems which create statistical and data mining models (“PMML Producers”) can easily inter-operate with systems which deploy PMML models for scoring or other operational purposes (“PMML Consumers”).
Change Detection using Augustus
For information regarding using Augustus with Change Detection and Health and Status Monitoring, please see change-detection.
Open Data
Open Data Group provides management consulting services, outsourced analytical services, analytic staffing, and expert witnesses broadly related to data and analytics. It has experience with customer data, supplier data, financial and trading data, and data from internal business processes.
It has staff in Chicago and San Francisco and clients throughout the U.S. Open Data Group began operations in 2002.
Overview
The above example contains plots generated in R of scoring results from Augustus. Each point on the graph represents a use of the scoring engine and a chart is an aggregation of multiple Augustus runs. A Baseline (Change Detection) model was used to score data with multiple segments.
Typical Use
Augustus is typically used to construct models and score data with models. Augustus includes a dedicated application for creating, or producing, predictive models rendered as PMML-compliant files. Scoring is accomplished by consuming PMML-compliant files describing an appropriate model. Augustus provides a dedicated application for scoring data with four classes of models, Baseline (Change Detection) Models, Tree Models, Regression Models and Naive Bayes Models. The typical model development and use cycle with Augustus is as follows:
Identify suitable data with which to construct a new model.
Provide a model schema which proscribes the requirements for the model.
Run the Augustus producer to obtain a new model.
Run the Augustus consumer on new data to effect scoring.
Separate consumer and producer applications are supplied for Baseline (Change Detection) models, Tree models, Regression models and for Naive Bayes models. The producer and consumer applications require configuration with XML-formatted files. The specification of the configuration files and model schema are detailed below. The consumers provide for some configurability of the output but users will often provide additional post-processing to render the output according to their needs. A variety of mechanisms exist for transmitting data but user’s may need to provide their own preprocessing to accommodate their particular data source.
In addition to the producer and consumer applications, Augustus is conceptually structured and provided with libraries which are relevant to the development and use of Predictive Models. Broadly speaking, these consist of components that address the use of PMML and components that are specific to Augustus.
Post Processing
Augustus can accommodate a post-processing step. While not necessary, it is often useful to
Re-normalize the scoring results or performing an additional transformation.
Supplements the results with global meta-data such as timestamps.
Formatting of the results.
Select certain interesting values from the results.
Restructure the data for use with other applications.
I have not been really posting or writing worthwhile on the website for some time, as I am still busy writing ” R for Business Analytics” which I hope to get out before year end. However while doing research for that, I came across many types of graphs and what struck me is the actual usage of some kinds of graphs is very different in business analytics as compared to statistical computing.
The criterion of top ten graphs is as follows-
1) Usage-The order in which they appear is not strictly in terms of desirability but actual frequency of usage. So a frequently used graph like box plot would be recommended above say a violin plot.
2) Adequacy- Data Visualization paradigms change over time- but the need for accurate conveying of maximum information in a minium space without overwhelming reader or misleading data perceptions.
3) Ease of creation- A simpler graph created by a single function is more preferrable to writing 4-5 lines of code to create an elaborate graph.
4) Aesthetics– Aesthetics is relative and in addition studies have shown visual perception varies across cultures and geographies. However , beauty is universally appreciated and a pretty graph is sometimes and often preferred over a not so pretty graph. Here being pretty is in both visual appeal without compromising perceptual inference from graphical analysis.
so When do we use a bar chart versus a line graph versus a pie chart? When is a mosaic plot more handy and when should histograms be used with density plots? The list tries to capture most of these practicalities.
Let me elaborate on some specific graphs-
1) Pie Chart- While Pie Chart is not really used much in stats computing, and indeed it is considered a misleading example of data visualization especially the skewed or two dimensional charts. However when it comes to evaluating market share at a particular instance, a pie chart is simple to understand. At the most two pie charts are needed for comparing two different snapshots, but three or more pie charts on same data at different points of time is definitely a bad case.
In R you can create piechart, by just using pie(dataset$variable)
As per official documentation, pie charts are not recommended at all.
Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. A bar chart or dot chart is a preferable way of displaying this type of data.
Cleveland (1985), page 264: “Data that can be shown by pie charts always can be shown by a dot chart. This means that judgements of position along a common scale can be made instead of the less accurate angle judgements.” This statement is based on the empirical investigations of Cleveland and McGill as well as investigations by perceptual psychologists.
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Despite this, pie charts are frequently used as an important metric they inevitably convey is market share. Market share remains an important analytical metric for business.
The pie3D( ) function in the plotrix package provides 3D exploded pie charts.An exploded pie chart remains a very commonly used (or misused) chart.
par(bg="gray")
pie(rep(1,24), col=rainbow(24), radius=0.9)
title(main="Color Wheel", cex.main=1.4, font.main=3)
title(xlab="(test)", cex.lab=0.8, font.lab=3)
(Note adding a grey background is quite easy in the basic graphics device as well without using an advanced graphical package)
DECISION STATS 