Tag: people

Computer Education grants from Google

Image representing Google as depicted in Crunc...
Image via CrunchBase

message from the official google blog-

http://googleblog.blogspot.com/2011/01/supporting-computer-science-education.html

With programs like Computer Science for High School (CS4HS), we hope to increase the number of CS majors —and therefore the number of people entering into careers in CS—by promoting computer science curriculum at the high school level.

For the fourth consecutive year, we’re funding CS4HS to invest in the next generation of computer scientists and engineers. CS4HS is a workshop for high school and middle school computer science teachers that introduces new and emerging concepts in computing and provides tips, tools and guidance on how to teach them. The ultimate goals are to “train the trainer,” develop a thriving community of high school CS teachers and spread the word about the awe and beauty of computing.

If you’re a university, community college, or technical School in the U.S., Canada, Europe, Middle East or Africa and are interested in hosting a workshop at your institution, please visit www.cs4hs.com to submit an application for grant funding.Applications will be accepted between January 18, 2011 and February 18, 2011.

In addition to submitting your application, on the CS4HS website you’ll find info on how to organize a workshop, as well as websites and agendas from last year’s participants to give you an idea of how the workshops were structured in the past. There’s also a collection ofCS4HS curriculum modules that previous participating schools have shared for future organizers to use in their own program.

Related Articles

Mapping Health Statistics at CDC.gov

Astronaut Buzz Aldrin during the first human l...
Image via Wikipedia

CDC.gov has a great tool for showing United States statistics on death and injury, drillable by various details.

The tool is hosted at http://wisqars.cdc.gov:8080/cdcMapFramework/

As a test I decided to map out injuries due to fire arms , and compare firearm deaths of white people versus the whole population.(see firearm deaths file)

See white people are more likely than black people to own guns (also read http://www.ncbi.nlm.nih.gov/pubmed/9572612 ), but it seems statistically they are less likely to be injured by firearms- so it could affect support for gun control laws on a racial ground- that was my null hypothesis. No politics, just plain statistics. I dont know- why dont you look at the data and decide-

 

 

 

 

 

Related Articles

Carole-Ann’s 2011 Predictions for Decision Management

Carole-Ann’s 2011 Predictions for Decision Management

For Ajay Ohri on DecisionStats.com

What were the top 5 events in 2010 in your field?
  1. Maturity: the Decision Management space was made up of technology vendors, big and small, that typically focused on one or two aspects of this discipline.  Over the past few years, we have seen a lot of consolidation in the industry – first with Business Intelligence (BI) then Business Process Management (BPM) and lately in Business Rules Management (BRM) and Advanced Analytics.  As a result the giant Platform vendors have helped create visibility for this discipline.  Lots of tiny clues finally bubbled up in 2010 to attest of the increasing activity around Decision Management.  For example, more products than ever were named Decision Manager; companies advertised for Decision Managers as a job title in their job section; most people understand what I do when I am introduced in a social setting!
  2. Boredom: unfortunately, as the industry matures, inevitably innovation slows down…  At the main BRMS shows we heard here and there complaints that the technology was stalling.  We heard it from vendors like Red Hat (Drools) and we heard it from bored end-users hoping for some excitement at Business Rules Forum’s vendor panel.  They sadly did not get it
  3. Scrum: I am not thinking about the methodology there!  If you have ever seen a rugby game, you can probably understand why this is the term that comes to mind when I look at the messy & confusing technology landscape.  Feet blindly try to kick the ball out while superhuman forces are moving randomly the whole pack – or so it felt when I played!  Business Users in search of Business Solutions are facing more and more technology choices that feel like comparing apples to oranges.  There is value in all of them and each one addresses a specific aspect of Decision Management but I regret that the industry did not simplify the picture in 2010.  On the contrary!  Many buzzwords were created or at least made popular last year, creating even more confusion on a muddy field.  A few examples: Social CRM, Collaborative Decision Making, Adaptive Case Management, etc.  Don’t take me wrong, I *do* like the technologies.  I sympathize with the decision maker that is trying to pick the right solution though.
  4. Information: Analytics have been used for years of course but the volume of data surrounding us has been growing to unparalleled levels.  We can blame or thank (depending on our perspective) Social Media for that.  Sites like Facebook and LinkedIn have made it possible and easy to publish relevant (as well as fluffy) information in real-time.  As we all started to get the hang of it and potentially over-publish, technology evolved to enable the storage, correlation and analysis of humongous volumes of data that we could not dream of before.  25 billion tweets were posted in 2010.  Every month, over 30 billion pieces of data are shared on Facebook alone.  This is not just about vanity and marketing though.  This data can be leveraged for the greater good.  Carlos pointed to some fascinating facts about catastrophic event response team getting organized thanks to crowd-sourced information.  We are also seeing, in the Decision management world, more and more applicability for those very technology that have been developed for the needs of Big Data – I’ll name for example Hadoop that Carlos (yet again) discussed in his talks at Rules Fest end of 2009 and 2010.
  5. Self-Organization: it may be a side effect of the Social Media movement but I must admit that I was impressed by the success of self-organizing initiatives.  Granted, this last trend has nothing to do with Decision Management per se but I think it is a great evolution worth noting.  Let me point to a couple of examples.  I usually attend traditional conferences and tradeshows in which the content can be good but is sometimes terrible.  I was pleasantly surprised by the professionalism and attendance at *un-conferences* such as P-Camp (P stands for Product – an event for Product Managers).  When you think about it, it is already difficult to get a show together when people are dedicated to the tasks.  How crazy is it to have volunteers set one up with no budget and no agenda?  Well, people simply show up to do their part and everyone has fun voting on-site for what seems the most appealing content at the time.  Crowdsourcing applied to shows: it works!  Similar experience with meetups or tweetups.  I also enjoyed attending some impromptu Twitter jam sessions on a given topic.  Social Media is certainly helping people reach out and get together in person or virtually and that is wonderful!

A segment of a social network
Image via Wikipedia

What are the top three trends you see in 2011?

  1. Performance:  I might be cheating here.   I was very bullish about predicting much progress for 2010 in the area of Performance Management in your Decision Management initiatives.  I believe that progress was made but Carlos did not give me full credit for the right prediction…  Okay, I am a little optimistic on timeline…  I admit it…  If it did not fully happen in 2010, can I predict it again in 2011?  I think that companies want to better track their business performance in order to correct the trajectory of course but also to improve their projections.  I see that it is turning into reality already here and there.  I expect it to become a trend in 2011!
  2. Insight: Big Data being available all around us with new technologies and algorithms will continue to propagate in 2011 leading to more widely spread Analytics capabilities.  The buzz at Analytics shows on Social Network Analysis (SNA) is a sign that there is interest in those kinds of things.  There is tremendous information that can be leveraged for smart decision-making.  I think there will be more of that in 2011 as initiatives launches in 2010 will mature into material results.
    5 Ways to Cultivate an Active Social Network
    Image by Intersection Consulting via Flickr
  3. Collaboration:  Social Media for the Enterprise is a discipline in the making.  Social Media was initially seen for the most part as a Marketing channel.  Over the years, companies have started experimenting with external communities and ideation capabilities with moderate success.  The few strategic initiatives started in 2010 by “old fashion” companies seem to be an indication that we are past the early adopters.  This discipline may very well materialize in 2011 as a core capability, well, or at least a new trend.  I believe that capabilities such Chatter, offered by Salesforce, will transform (slowly) how people interact in the workplace and leverage the volumes of social data captured in LinkedIn and other Social Media sites.  Collaboration is of course a topic of interest for me personally.  I even signed up for Kare Anderson’s collaboration collaboration site – yes, twice the word “collaboration”: it is really about collaborating on collaboration techniques.  Even though collaboration does not require Social Media, this medium offers perspectives not available until now.

Brief Bio-

Carole-Ann is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry. Her claim to fame is the strategy and direction of Blaze Advisor, the then-leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience. She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

Leveraging her Masters degree in Applied Mathematics / Computer Science from a “Grande Ecole” in France, she started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication – as well as conducting strategic consulting gigs around change management.

She now tweets as @CMatignon, blogs at blog.sparklinglogic.com and interacts at community.sparklinglogic.com.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication.  At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs mostly around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM).  She developed a growing interest in Optimization as well as Business Rules.  At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart.  She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business.  Her technical background kept her very much in touch with technology as she advanced.

She also became addicted to Twitter in the process.  She is active on all kinds of social media, always looking for new digital experience!

Outside of work, Carole-Ann loves spending time with her two boys.  They grow fruits in their Northern California home and cook all together in the French tradition.

profile on LinkedIn

TwitterFollow me on Twitter

Filtering to Gain Social Network Value
Image by Intersection Consulting via Flickr
Social Networks Hype Cycle
Image by fredcavazza via Flickr
Related Articles

Using R from within Python

Python logo
Image via Wikipedia

I came across this excellent JSS paper at www.jstatsoft.org/v35/c02/paper

on a Python package called PypeR which allows you to use R from within Python using the pipe functionality.

It is an interesting package and given Python’s increasing buzz , one worthy to be checked out by people using or thinking Python in their packages.

























Citation:
	@article{Xia:McClelland:Wang:2010:JSSOBK:v35c02,
	  author =	"Xiao-Qin Xia and Michael McClelland and Yipeng Wang",
	  title =	"PypeR, A Python Package for Using R in Python",
	  journal =	"Journal of Statistical Software, Code Snippets",
	  volume =	"35",
	  number =	"2",
	  pages =	"1--8",
	  day =  	"30",
	  month =	"7",
	  year = 	"2010",
	  CODEN =	"JSSOBK",
	  ISSN = 	"1548-7660",
	  bibdate =	"2010-03-23",
	  URL =  	"http://www.jstatsoft.org/v35/c02",
	  accepted =	"2010-03-23",
	  acknowledgement = "",
	  keywords =	"",
	  submitted =	"2009-10-23",
	}

 

Related Articles

Interview Luis Torgo Author Data Mining with R

Example of k-nearest neighbour classification
Image via Wikipedia

Here is an interview with Prof Luis Torgo, author of the recent best seller “Data Mining with R-learning with case studies”.

Ajay- Describe your career in science. How do you think can more young people be made interested in science.

Luis- My interest in science only started after I’ve finished my degree. I’ve entered a research lab at the University of Porto and started working on Machine Learning, around 1990. Since then I’ve been involved generally in data analysis topics both from a research perspective as well as from a more applied point of view through interactions with industry partners on several projects. I’ve spent most of my career at the Faculty of Economics of the University of Porto, but since 2008 I’m at the department of Computer Science of the Faculty of Sciences of the same university. At the same time I’ve been a researcher at LIAAD / Inesc Porto LA (www.liaad.up.pt).

I like a lot what I do and like science and the “scientific way of thinking”, but I cannot say that I’ve always thought of this area as my “place”. Most of all I like solving challenging problems through data analysis. If that translates into some scientific outcome than I’m more satisfied but that is not my main goal, though I’m kind of “forced” to think about that because of the constraints of an academic career.

That does not mean I’m not passionate about science, I just think there are many more ways of “doing science” than what is reflected in the usual “scientific indicators” that most institutions seem to be more and more obsessed about.

Regards interesting young people in science that is a hard question that I’m not sure I’m qualified to answer. I do tend to think that young people are more sensible to concrete examples of problems they think are interesting and that science helps in solving, as a way of finding a motivation for facing the hard work they will encounter in a scientific career. I do believe in case studies as a nice way to learn and motivate, and thus my book 😉

Ajay- Describe your new book “Data Mining with R, learning with case studies” Why did you choose a case study based approach? who is the target audience? What is your favorite case study from the book

Luis- This book is about learning how to use R for data mining. The book follows a “learn by doing it” approach to data mining instead of the more common theoretical description of the available techniques in this discipline. This is accomplished by presenting a series of illustrative case studies for which all necessary steps, code and data are provided to the reader. Moreover, the book has an associated web page (www.liaad.up.pt/~ltorgo/DataMiningWithR) where all code inside the book is given so that easy copy-paste is possible for the more lazy readers.

The language used in the book is very informal without many theoretical details on the used data mining techniques. For obtaining these theoretical insights there are already many good data mining books some of which are referred in “further readings” sections given throughout the book. The decision of following this writing style had to do with the intended target audience of the book.

In effect, the objective was to write a monograph that could be used as a supplemental book for practical classes on data mining that exist in several courses, but at the same time that could be attractive to professionals working on data mining in non-academic environments, and thus the choice of this more practically oriented approach.

Regards my favorite case study that is a hard question for an author… still I would probably choose the “Predicting Stock Market Returns” case study (Chapter 3). Not only because I like this challenging problem, but mainly because the case study addresses all aspects of knowledge discovery in a real world scenario and not only the construction of predictive models. It tackles data collection, data pre-processing, model construction, transforming predictions into actions using different trading policies, using business-related performance metrics, implementing a trading simulator for “real-world” evaluation, and laying out grounds for constructing an online trading system.

Obviously, for all these steps there are far too many options to be possible to describe/evaluate all of them in a chapter, still I do believe that for the reader it is important to see the overall picture, and read about the relevant questions on this problem and some possible paths that can be followed at these different steps.

In other words: do not expect to become rich with the solution I describe in the chapter !

Ajay- Apart from R, what other data mining software do you use or have used in the past. How would you compare their advantages and disadvantages with R

Luis- I’ve played around with Clementine, Weka, RapidMiner and Knime, but really only playing with teaching goals, and no serious use/evaluation in the context of data mining projects. For the latter I mainly use R or software developed by myself (either in R or other languages). In this context, I do not think it is fair to compare R with these or other tools as I lack serious experience with them. I can however, tell you about what I see as the main pros and cons of R. The main reason for using R is really not only the power of the tool that does not stop surprising me in terms of what already exists and keeps appearing as contributions of an ever growing community, but mainly the ability of rapidly transforming ideas into prototypes. Regards some of its drawbacks I would probably mention the lack of efficiency when compared to other alternatives and the problem of data set sizes being limited by main memory.

I know that there are several efforts around for solving this latter issue not only from the community (e.g. http://cran.at.r-project.org/web/views/HighPerformanceComputing.html), but also from the industry (e.g. Revolution Analytics), but I would prefer that at this stage this would be a standard feature of the language so the the “normal” user need not worry about it. But then this is a community effort and if I’m not happy with the current status instead of complaining I should do something about it!

Ajay- Describe your writing habit- How do you set about writing the book- did you write a fixed amount daily or do you write in bursts etc

Luis- Unfortunately, I write in bursts whenever I find some time for it. This is much more tiring and time consuming as I need to read back material far too often, but I cannot afford dedicating too much consecutive time to a single task. Actually, I frequently tease my PhD students when they “complain” about the lack of time for doing what they have to, that they should learn to appreciate the luxury of having a single task to complete because it will probably be the last time in their professional life!

Ajay- What do you do to relax or unwind when not working?

Luis- For me, the best way to relax from work is by playing sports. When I’m involved in some game I reset my mind and forget about all other things and this is very relaxing for me. A part from sports I enjoy a lot spending time with my family and friends. A good and long dinner with friends over a good bottle of wine can do miracles when I’m too stressed with work! Finally,I do love traveling around with my family.

Luis Torgo

Short Bio: Luis Torgo has a degree in Systems and Informatics Engineering and a PhD in Computer Science. He is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto. He is also a researcher of the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) belonging to INESC Porto LA. Luis Torgo has been an active researcher in Machine Learning and Data Mining for more than 20 years. He has lead several academic and industrial Data Mining research projects. Luis Torgo accompanies the R project almost since its beginning, using it on his research activities. He teaches R at different levels and has given several courses in different countries.

For reading “Data Mining with R” – you can visit this site, also to avail of a 20% discount the publishers have generously given (message below)-

For more information and to place an order, visit us at http://www.crcpress.com.  Order online and apply 20% Off discount code 907HM at checkout.  CRC is pleased to offer free standard shipping on all online orders!

link to the book page  http://www.crcpress.com/product/isbn/9781439810187

Price: $79.95
Cat. #: K10510
ISBN: 9781439810187
ISBN 10: 1439810184
Publication Date: November 09, 2010
Number of Pages: 305
Availability: In Stock
Binding(s): Hardback 

Related Articles

Interview Ajay Ohri Decisionstats.com with DMR

From-

http://www.dataminingblog.com/data-mining-research-interview-ajay-ohri/

Here is the winner of the Data Mining Research People Award 2010: Ajay Ohri! Thanks to Ajay for giving some time to answer Data Mining Research questions. And all the best to his blog, Decision Stat!

Data Mining Research (DMR): Could you please introduce yourself to the readers of Data Mining Research?

Ajay Ohri (AO): I am a business consultant and writer based out of Delhi- India. I have been working in and around the field of business analytics since 2004, and have worked with some very good and big companies primarily in financial analytics and outsourced analytics. Since 2007, I have been writing my blog at http://decisionstats.com which now has almost 10,000 views monthly.

All in all, I wrote about data, and my hobby is also writing (poetry). Both my hobby and my profession stem from my education ( a masters in business, and a bachelors in mechanical engineering).

My research interests in data mining are interfaces (simpler interfaces to enable better data mining), education (making data mining less complex and accessible to more people and students), and time series and regression (specifically ARIMAX)
In business my research interests software marketing strategies (open source, Software as a service, advertising supported versus traditional licensing) and creation of technology and entrepreneurial hubs (like Palo Alto and Research Triangle, or Bangalore India).

DMR: I know you have worked with both SAS and R. Could you give your opinion about these two data mining tools?

AO: As per my understanding, SAS stands for SAS language, SAS Institute and SAS software platform. The terms are interchangeably used by people in industry and academia- but there have been some branding issues on this.
I have not worked much with SAS Enterprise Miner , probably because I could not afford it as business consultant, and organizations I worked with did not have a budget for Enterprise Miner.
I have worked alone and in teams with Base SAS, SAS Stat, SAS Access, and SAS ETS- and JMP. Also I worked with SAS BI but as a user to extract information.
You could say my use of SAS platform was mostly in predictive analytics and reporting, but I have a couple of projects under my belt for knowledge discovery and data mining, and pattern analysis. Again some of my SAS experience is a bit dated for almost 1 year ago.

I really like specific parts of SAS platform – as in the interface design of JMP (which is better than Enterprise Guide or Base SAS ) -and Proc Sort in Base SAS- I guess sequential processing of data makes SAS way faster- though with computing evolving from Desktops/Servers to even cheaper time shared cloud computers- I am not sure how long Base SAS and SAS Stat can hold this unique selling proposition.

I dislike the clutter in SAS Stat output, it confuses me with too much information, and I dislike shoddy graphics in the rendering output of graphical engine of SAS. Its shoddy coding work in SAS/Graph and if JMP can give better graphics why is legacy source code preventing SAS platform from doing a better job of it.

I sometimes think the best part of SAS is actually code written by Goodnight and Sall in 1970’s , the latest procs don’t impress me much.

SAS as a company is something I admire especially for its way of treating employees globally- but it is strange to see the rest of tech industry not following it. Also I don’t like over aggression and the SAS versus Rest of the Analytics /Data Mining World mentality that I sometimes pick up when I deal with industry thought leaders.

I think making SAS Enterprise Miner, JMP, and Base SAS in a completely new web interface priced at per hour rates is my wishlist but I guess I am a bit sentimental here- most data miners I know from early 2000’s did start with SAS as their first bread earning software. Also I think SAS needs to be better priced in Business Intelligence- it seems quite cheap in BI compared to Cognos/IBM but expensive in analytical licensing.

If you are a new stats or business student, chances are – you may know much more R than SAS today. The shift in education at least has been very rapid, and I guess R is also more of a platform than a analytics or data mining software.

I like a lot of things in R- from graphics, to better data mining packages, modular design of software, but above all I like the can do kick ass spirit of R community. Lots of young people collaborating with lots of young to old professors, and the energy is infectious. Everybody is a CEO in R ’s world. Latest data mining algols will probably start in R, published in journals.

Which is better for data mining SAS or R? It depends on your data and your deadline. The golden rule of management and business is -it depends.

Also I have worked with a lot of KXEN, SQL, SPSS.

DMR: Can you tell us more about Decision Stats? You have a traffic of 120′000 for 2010. How did you reach such a success?

AO: I don’t think 120,000 is a success. Its not a failure. It just happened- the more I wrote, the more people read.In 2007-2008 I used to obsess over traffic. I tried SEO, comments, back linking, and I did some black hat experimental stuff. Some of it worked- some didn’t.

In the end, I started asking questions and interviewing people. To my surprise, senior management is almost always more candid , frank and honest about their views while middle managers, public relations, marketing folks can be defensive.

Social Media helped a bit- Twitter, Linkedin, Facebook really helped my network of friends who I suppose acted as informal ambassadors to spread the word.
Again I was constrained by necessity than choices- my middle class finances ( I also had a baby son in 2007-my current laptop still has some broken keys :) – by my inability to afford traveling to conferences, and my location Delhi isn’t really a tech hub.

The more questions I asked around the internet, the more people responded, and I wrote it all down.

I guess I just was lucky to meet a lot of nice people on the internet who took time to mentor and educate me.

I tried building other websites but didn’t succeed so i guess I really don’t know. I am not a smart coder, not very clever at writing but I do try to be honest.

Basic economics says pricing is proportional to demand and inversely proportional to supply. Honest and candid opinions have infinite demand and an uncertain supply.

DMR: There is a rumor about a R book you plan to publish in 2011 :-) Can you confirm the rumor and tell us more?

AO: I just signed a contract with Springer for ” R for Business Analytics”. R is a great software, and lots of books for statistically trained people, but I felt like writing a book for the MBAs and existing analytics users- on how to easily transition to R for Analytics.

Like any language there are tricks and tweaks in R, and with a focus on code editors, IDE, GUI, web interfaces, R’s famous learning curve can be bent a bit.

Making analytics beautiful, and simpler to use is always a passion for me. With 3000 packages, R can be used for a lot more things and a lot more simply than is commonly understood.
The target audience however is business analysts- or people working in corporate environments.

Brief Bio-
Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R besides analytical software like SPSS and SAS.

Related Articles

America's Data Book: Census Abstract 2011

U.S. Census Bureau Regions, Partnership and Da...
Image via Wikipedia

An excellent summary of 2011 Census Statistical abstract was given by NYTimes at

http://www.nytimes.com/interactive/2011/01/07/us/CENSUS.html?hp

Like more white people now enjoy jazz than black people now (presumably who have got rap music), but not details enough on ahem country music

The Data book is at http://www.census.gov/compendia/statab/

What is the Statistical Abstract?

The Statistical Abstract of the United States, published since 1878, is the authoritative and comprehensive summary of statistics on the social, political, and economic organization of the United States.

Use the Abstract as a convenient volume for statistical reference, and as a guide to sources of more information both in print and on the Web.

Sources of data include the Census Bureau, Bureau of Labor Statistics, Bureau of Economic Analysis, and many other Federal agencies and private organizations.

Tables of Interest

1060 – Shopping Centers–Number and Gross Leasable Area [Excel 31K] | [PDF 59K]

1170 – Flow of Funds Accounts-Liabilities of Households and Nonprofit Organizations [Excel 41K] | [PDF 66K]

1172 – Amount of Debt Held by Families-Percent Distribution [Excel 29K] | [PDF 66K]

1173 – Ratios of Debt Payments to Family Income [Excel 857K] | [PDF 64K]

327 – Law Enforcement Officers Killed and Assaulted [Excel 34k] | [PDF 468k]

From the last table you can see , while the number of officers killed or feloniously killed decreased by 20% in past five years, the number of officers assaulted by firearms grew by 20% in the same period.

Related Articles