Webinar – Building Dashboards for Real Business Results – 12/11

Which chart or graph is right for you?

 

This paper describes eleven chart types and helps you determine when to use each. It also includes dozens of tips on how to enhance visualizations to make your data pop. See preview.

You’ve got data and you’ve got questions. But which chart or graph helps you get to the heart of your goal?

“Visualizing data using color, shapes, positions on X and Y axes, bar charts, pie charts, whatever you use, makes it instantly visible and instantly significant to the people who are looking at it.”

Get started by creating the best type of chart for your data and questions. From there, you’ll quickly find you’re not only answering your initial questions, but telling amazing stories with your data.

The charts and graphs described in the paper include:

Lines, pies, maps, scatter plots, gantts, bubbles, histograms, bullets, heat maps, and highlight tables.

DOWNLOAD http://www.tableausoftware.com/which-chart-or-graph-is-right-for-you 

Top ranked technology innovation Priorities

The Future of Apache Hadoop

Register http://info.hortonworks.com/FutureofHadoopSeries.html

New sessions added!

Apache Hadoop has evolved rapidly to become the leading platform for managing, processing and analyzing big data. If your organization is examining how you can use Hadoop to store, transform and refine large volumes of data, this webinar series is for you.

Join us in this series with the people at the center of Hadoop movement to gain insight into current advances in Apache Hadoop, obtain use-cases and best practices on how to get started with Hadoop and live Q&A.

Why you should register for this series? 

• You’re interested in learning the essential components required in a Hadoop Platform.

• You want use-cases and tips on how you can add Apache hadoop to your existing investments.

• You’re currently using Hadoop and have questions for our committers.

Register now and join the conversations! 

High Availability with Hadoop
November 27, 2012 10 am PST/1 pm EST/ 6pm GMT

Featured Speaker: Rohit Bakhshi

Product Management at Hortonworks.

Back in September, we announced Hortonworks Data Platform 1.1, the only Hadoop distribution to provide full stack High Availability. With HDP 1.1, we extended our HA options with the ability to include the most current visions of Red Hat Enterprise Linux (RHEL) and the High Availability Add-On. Join Rohit Bakshi, Product Management, as he guides you through the current work on HA and Hadoop. Rohit will have a live demo of High Availability options on HDP 1.1 as well as answer any questions during this session.

Agile Data: Building Data Analytics Applications
December 18, 2012 10 am PST/1 pm EST/ 6pm GMT

Featured Speaker: Russell Jurney

Hadoop and Big Data Evangelist/Nerd at Hortonworks.

In this webinar, Russell Jurney will present about rapidly prototyping analytics applications using the Hadoop stack to return to agility in light of the ever-deepening analytics stack. The presentation uses Hadoop, Pig, NoSQL stores and lightweight web frameworks to rapidly connect end-users to real insights. Topics covered include writing User Defined Functions in Pig and publishing insights mined on Hadoop to NoSQL stores like MongoDB, HBase, and Cassandra, and using Python, Ruby, JRuby and Node.js to present data mined on Hadoop in a web browser.

The Business Value of Predictive Analytics

 Based on IDC research, the median ROI of predictive analytics projects is close
to three times higher than that of non-predictive projects.The median ROI of predictive analytics projects is 250%, which represents an increase from the 145% average ROI from IDC’s 2003 study. Other key differences over the past eight years in projects involving predictive analytics were:
 The volume and variety of data being analyzed. For example, an increasing number of organizations are improving their predictive models by evaluating and training them on combined sets of structured and semistructured data with unstructured content.
 The higher priority and imperative of predictive analytics among  organizations’ overall initiatives. Predictive analytics has certainly become a topic of many more conversations and gained a new respect among “nonquants.”
 Many of the benefits of predictive analytics projects are ancillary to the directly
quantifiable benefits that can be captured through a formal ROI calculation.
 Major benefits of business analytics projects that employed predictive analytics
center on business process enhancement, especially improving the quality of
operational decisions. This contrasts with the primary benefit of non-predictive
analytics projects, which focus on productivity improvement. Yet, in most cases,
predictive and non-predictive analytics are deployed together as part of a broader
business analytics solution.http://docs.media.bitpipe.com/io_10x/io_105085/item_542151/TheValueOfPredictiveAnalytics.pdf

INTRODUCTION TO SMOOTHING AND P-SPLINE TECHNIQUES USING R

Smoothing helps you maintain a view of the data forest, while not losing sight of the trees. Or vice-versa, if excessive globalism is your weakness. Our smoothing course with Brian Marx uses R at Statistics.com. More on Introduction to Smoothing and P-Splines Using R.

Real-life data often are not well-described by a single simple function. Splines combine multiple functions differentially in a smooth fashion over different ranges. P-splines are widely applicable, effective, and popular: over 500 citations for the instructors’ Statistical Science article that introduced P-splines. You will be introduced to P-splines via B-splines (basis splines), and learn how to balance the competing demands of fidelity to the data and smoothness, and how to optimize the smoothing.

 

Who Should Take This Course?

Medical and social science researchers, data miners, environmental analysts;  any researcher who must develop statistical models with “messy” data.

Course Program:

 

Course outline: The course is structured as follows

SESSION 1:  Smoothing via Regression – Local vs Global Bases

• Global bases can be ineffective
• Local bases are attractive
• B-splines
• Difference penalties

 

SESSION 2: Introducing P-splines

• Dealing with non-normal data
• Moving from GLM to P-spline
• Density estimation
• Variance smoothing

 

SESSION 3: Optimizing the Smoothing

• Fidelity to the data vs smooth curve
• Cross-validation, AIC
• Error bands

 

SESSION 4: Multidimensional Smoothing

• Generalized Addition Models
• Varying coefficient models
• Tensor products

 

The instructors:

Brian Marx is Professor of Statistics at Louisiana State University, Chair of the Statistical Modeling Society, and the Coordinating Editor of “Statistical Modeling: An International Journal.” 

Paul Eilers is Professor of Genetical Statistics at the Erasmus University Medical Center (Netherlands). His research interests include high throughput genomic data analysis, chemometrics, smoothing, and filtering and smoothing of time series and signals from chemical instruments. 

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board so that they will be able to ask questions and exchange comments with instructor, Dr. Brian Marx and Dr. Paul Eilers. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

 

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

 

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

 

For India Registration and pricing, please visit us at www.india.statistics.com.

 

For More details contact at

Email: info@c-elt.com

Call: 020 66009116

 

Websites:

www.india.statistics.com

www.c-elt.com

 

 

With Cross-Tab OLAP Analytics, I’m (Almost) the Queen of the World!

Posted on October 15, 2012

http://blog.elegantjbi.com/2012/10/15/october-15-2012/?goback=%2Egde_3891169_member_188514899

Last year I attended a business conference. You know the kind – they are designed to show you new ways to manage your business and to remind you that there are always new opportunities for success and business growth. One of the speakers regaled us with tales of how business intelligence OLAP analysis could help us effectively and objectively manage and measure our success. I was intrigued.

I contacted my IT consultant and today I have BI with cross-tab OLAP analytics! It offers a 360° view of the business to provide objective metrics and helps me quickly determine whether my organization, department, or team is achieving its goals. Integration with data extraction, cube management features, and ad-hoc queries and BI reporting helps me achieve a clear view of performance.

Everybody needs an analytical tool that can produce summary, detail, graphics, charts, and reports with ease. You can have complex OLAP functionality in a simple user interface that is integrated with enterprise systems and sources and provides precise, cross-tab or tabular reports without technical assistance.

 

Dynamic analysis design	Interactive, easy-to-use analysis design wizard
Multiple page dimension filter	Runtime retrieval parameter on loading OLAP analysis
Drill-up, drill-down and through	Graphical view of analysis
User defined rows and columns	Spot lighters with color
Data Value – Display Value Mapping	Sorting and ranking of rows and columns
Group and ungroup rows and columns	Value mapping for user friendly display of data
Access to central cube repository objects	Cell notes and table notes with private or public option
Cell format with resizing and renaming	Sharing and Publishing
Exports: PDF, CSV, XML, JPEG, XLS	Publishing and Delivery agent

I got all of this and more with my new BI OLAP analysis tool. You can too! Contact the pros: http://www.elegantjbi.com/ or emailsales@elegantjbi.com.

Intelligently yours, Betsy Bizintel

If Data is Currency, BI is Indispensable

By SiliconIndia   |   Thursday, 22 November 2012, 05:20 Hrs

http://bi.siliconindia.com/news/If-Data-is-Currency-BI-is-Indispensable-nid-134825.htmlBangalore: With data and analytics being lauded as fundamentally essential for business, innovative uses for BI have emerged. Recent reports suggest that BI could be used to combat organized crime. Much has been discussed about how it could be used in weather forecasting and health science. What cannot be dismissed is the fact that we are progressing to a future where data will be currency. The usage of BI in different domains and analytics as a science was discussed recently at the Business Intelligence Conference organized in Bangalore.

The BI conference had Subrahmanyam DVR, Senior Data Architect, Intel & Nagaraj Kulkarni, Director – Information Excellence, Compegence- 2 prominent members of the BI community as MCs.

The two track event was kickstarted by an inspirational keynote by Viros Sharma, VP & Global Practice Head – DWBI & Analytics, ITC Infotech in the business value track while on the other hand the Technical track featured a panel discussion regarding how BI is utilized in various domains and about its future. He highlighted an oft ignored concept of how BI consulting should be about enablement of the customer. He also stated emphatically, “FOCUS – In today’s BI environment, where the number of horizontals, verticals, tools and technologies that DW, BI and Analytics professionals need to work upon, FOCUS becomes essential. If one chooses to cover as much space as he/she can cover; the simile would be to become a Blue Whale in BI Ocean… You can travel 14000 miles per day, but at one point in time, you will be at one place only. Attempt to be universally present can make you a bacteria in Ocean. To become a consultant, Leadership skills are extremely important and that ranges from Truth, Discipline, Influencing, Selflessness, Objectivity to Fearlessness and positivity.”

How can BI be made relevant to each business? “Business Intelligence priorities need to evolve as per business dynamics for achieving end user patronage! The BI solution with increased life span works well for long run & that is achieved by support addressed with appropriate solution dimensions especially for information driven businesses like Modern Trade,” stated Sushma Kondalkana, Manager – Information Management at Metro Cash & Carry.

About the intricate relation between BI and pharmaceuticals, Dr. Ambrish Joshi, AVP – Healthcare and Life Sciences, Smart Decision Services, Genpact LLC, had this to say, “Business Intelligence (BI) is used across the Pharmaceutical value chain and several factors are further augmenting the application/ usage of BI. Factors like rising drug development time & cost, changes in market dynamics, globalization, increased regulations, change in business model and change in strategy, etc.

The ultimate goal of BI is to serve as backbone of strategic/ tactical/ operational planning & performance measurement and it is currently playing a vital role in Strategic Decision Making process within lifesciences industry. Pharma organizations are advocating a serious need for BI solutions that address specific business scenarios in this industry. BI need and focus varies across the business chain and industry is poised to gain more and more by leveraging this across several functions and graduating towards Enterprise BI. BI teams within the organizations should act like a start-up company with business driven methodology & project management.”

With innovation in utility of BI, there will also be innovation in the technology itself and how the tools adopt to the growing demand and market conditions.

Derick Jose, Director-Big Data Solutions, Flutura Decision Sciences who spoke about architecting big data architecture said that, “2 Key takeaways for architecting big data architecture would be

-Use case backwards vs Hadoop/Technology forward
-Consider having ensemble machine learning models embedded into analytical processes”

The future of BI is expanding across verticals and as a technology. What will this mean for the global economy? How will the job market be influenced by the huge demand for Data Scientists and BI Architects? Only time will tell, but what can’t be denied is the fact that analytics in the future will be omnipresent.

 

Real-World Data Science

http://www.information-management.com/blogs/real-world-data-science-10023560-1.html

I had the good fortune of participating in day one of the*IE BI and Predictive Analytics Summit last Thursday in Chicago. With over 300 attendees, the 2012 version was considerably larger than the one I attended two years ago and, in my view, better as well. The insights on the conduct of data science in large organizations were especially informative.

Digvijay Lamba of WalmartLabs discussed a system for using big data to deliver “unexpected insights” for the very lucrative Walmart Halloween season. Lamba was the first of many speakers to emphasize domain expertise as a critical data scientist skill. To help remove the gap between business and tech/analytics, his group has articulated the “social genome” that consists of products, people, locations, events and interests. Dashboards for ideas are generated when the genome taxonomy is cross-classified with data sources that include transactions, web-based data, social media data, blogs, etc.

Robin Glinton and Herman Asorey of Sears Holdings data science center of excellence eat their own dog food with a system that helps manage their Operational Data Engine that supports 1,000s of analytics users on a large, massively-parallel Teredata platform. Starting with over a hundred system performance metrics, SH deploys dimension-reducing principal components to distill key performance indicators, among which turn out to be parallel efficiency, gating efficiency, burn rate and time expansion. The DS group then creates segments using techniques like k-mean clustering, and examines KPI trends against features volatility and momentum that are so important in financial services. With support vector machines and neural nets as their essential classification engines, the authors note off-the-charts efficiency improvements from the efforts.

Anne Hale of Pfizer was quick to acknowledge her work with customer segmentation for company drugs involves no big data. Years of primary research on segmenting and predicting the potential for Pfizer products have led her to reject most attitudinal measures for behavioral ones. And the linkage hypotheses that relate company behaviors to leading indicators to, in turn, physician intent to prescribe Pfizer drugs, have taken her down the path of the same Simultaneous Equation Models taught in econometrics courses. Indeed, I haven’t seen “path analysis” since grad school 30 years ago, at the time rejecting the technique as too grand. Hale proves me wrong: SEMs have now been a successful staple of her marketing work at Pfizer for decades.

Stanford-trained economist and Chief Data Scientist of Accretive Health, Scott Nicholson, is both excited and frustrated with the opportunities for DS in health care. Having cut his chops at analytics hotbed LinkedIn, Nicholson has seen the potential of data science and fully appreciates the possibilities in health care. On the other hand, health care lags the Internet world in technology / analytics and is saddled by legal compliance concerns – one, for example, being its reticence on open source. Not dismayed, Nicholson sees a big present/future in health care for DS. And like Digvijay Lamba, Nicholson obsesses on domain expertise, defining data science as “using data to solve problems end-to-end, starting from asking the right questions to making insights actionable.”

Mukund Raghunath of consultancy Mu Sigma distinguishes muddy from clear data science challenges. The latter lend themselves to the traditional scientific problem-driven cycle of hypothesis, data, analyses. Muddy problems, in contrast, generally mandate discovery-driven solutions, with initial data observation needed to clarify business issues. While acknowledging that data science portfolios must include both types, Raghunath argues that discovery-driven solutions, even as they present lower information to noise ratios, are less biased and more likely to lead to game-changing results.

My favorite among the outstanding presentations was Clifford Lyon’s discussion of the impact of experimentation or A/B testing on different aspects of Web user experience with CBS Interactive sites. Colors, typography, positioning, navigation, graphics, tag lines, and quotations are all in play at CBS. The value of experimentation is, of course, that cause and effect can be established between design decisions and subsequent traffic behavior. The experimentation cycle is simple:

1. Create variations
2. Apportion users randomly
3. Measure key indicators
4. Conduct tests and perform analyses

Lyon notes that only one out of every six experiments yields results of business value.  Multivariate testing, with multiple factors and their interactions, is particularly nettlesome. Finally, Lyon’s team must re-test its successes over time, often with A/A randomization, lest what provided lift at first now fails.

Good stuff. I wish I could have returned to hear more presentations on day two. I’ll certainly have future *IE Group, Chicago-area analytics conferences on my radar.

 

 

Cloudwick’s next batch of Hadoop Training

Cloudwick’s next batch of Hadoop Training starts from Dec 3rd at our Secunderabad office.  

Timings: 7am to 9am
Duration: 3 weeks
Course Content:  Covering basics of Administration (Installation, maintenance and monitoring of Hadoop clusters) and complete Developer course with more focus on practical exercises.  Real life use cases will be provided as part of the training and guidance will be provided for Cloudera Developer Certification.   You will get a chance to work on 30 node production ready CDH4 cluster. Please download the detailed course content fromwww.cloudwick.com/training. 

Attend a FREE Demo on Nov 24th (Saturday) at 9am.  

Cloudwick Technologies is a 3 years old Big Data company based at Bay Area. We worked on many Hadoop projects for fortune 500 clients (http://cloudwick.com/portfolio/) and provided training to hundreds of individuals and corporates.  

Location of Cloudwick Secunderabad:   https://maps.google.co.in/maps?q=cloudwick&hl=en&ll=17.440435,78.485017&spn=0.001436,0.002642&hq=cloudwick&hnear=Hyderabad,+Andhra+Pradesh&t=h&z=19

Directions:  
Coming from Ameerpet area:  Take a right turn onto Ministers road and take right on to PG Road towards Sunshine Hospital/Paradise Restarent.  Travel approximately 1KM and take left at Makhan Bhog (or the lane opposite to Baskin Robbins). We are located opposite to Karani Infotech.  Coming from other areas:  Get onto the PG Road in front of Paradise Restaurant, Take immediate left at HDFC bank and then take immediate right.  Cross Arya Vysya Abhyudaya Sangam and get into the lane opposite to Baskin Robbins.    

Thanks and Regards,
Venkat Ankam
Big Data Architect
Cloudwick Technologies
Email: venkat@cloudwick.com