Describes how edwards-magee.com exited and shortened the market in January 2008 and went long in the gold market in 2003 Presents a powerful and simple system to replace Dow Theory Contains new patterns and methods, integrates new charts, as well as offers expanded material on Magee's Basing Points Procedure Expands coverage of pragmatic portfolio theory as a viable alternative to modern portfolio theory
Hands-On Data Science with R Dr Graham Williams, PhD (ANU, Machine Learning), BSc (Maths, Hons) Chief Data Scientist, Togaware and Australian Taxation Office Adjunct Professor, Australian National University and University of Canberra International Visiting Professor, Chinese Academy of Sciences Our goal is to provide introductory material to cost effectively kick start an organization's entry into Data Science. To that end, we introduce the use of R for doing Data Science. In addition to the extensive material available on our web site we provide a unique offering of in-situ hands-on training . We offer traditional out-of-office training courses, but we find more effective learning can occur hands-on in-situ. We offer one of the world's leading Data Scientists to work alongside and mentor your staff over one or two weeks. We work confidentially on actual projects, with training "on-the-job" provided by a professional with 30 years experience in the industry and author of the best selling book on Data Mining with Rattle and R . Contact Togaware Training at training@togaware.com for details. Our on-line resources, including Hands-On Data Science , weave together a collection of freely available and open source tools for the Data Scientist. The tools are all part of the R Statistical Software Suite. Each chapter is made up of multiple pages, but each page within a chapter is a one page guide that covers a particular aspect of the topic (hence also refered to as the OnePageR guide). They are a great place to start, before engaging our hands-on training experts. Hands-On Data Science can be worked through as a hands-on guide and then used as a reference guide. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before. Many chapters also have a lecture pack and a laboratory session where a number of tasks can be completed. The R code sitting behind each chapter is also provided and can be easily run standalone to replicate the material presented in the chapter. The material is always under development ! Chapters will change (and hopefully improve) regularly. Links preceded with a * are more well developed. All of the material is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License allowing access to everyone for any purpose (except commercial) and is provided at no cost. You can assist in helping cover the costs of providing this material through a $40 contribution using PayPal. Your support encourages further development of this resource as does feedback, suggestions, and ideas , which are always welcome. Refer to the Data Mining Survival Guide or my book on Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R) for related material. Many of the initial chapters were developed and tested whilst visiting the Shenzhen Institutes of Advanced Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used across the chapters is available for download as data.zip . Enjoy! Getting Started as a Data Scientist An Introduction to Data Mining: * Lecture Introducing Data Science, Rattle and R: * Lecture - * Chapter - * R Rattle to R: * Chapter - * R R for the Eager Data Scientist A Template for Preparing Data: * Chapter - * R A Template for Building Models: * Chapter - * R Case Studies: * Chapter - * R Basic R Tips and Tricks Chapter - R Dealing With Data Reading Data into R: * Chapter - * R Exploring and Summarising Data: * Chapter - * R Visualising Data with GGPlot2: * Chapter - * R Transforming Data: * Chapter - * R Descriptive Analytics Cluster Analysis: * Lecture - Chapter - R Association Analysis: * Lecture - Chapter - R Predictive Analytics Decision Trees: * Lecture - * Chapter - * R - * Rattle Ensembles of Decision Trees: * Lecture - * Chapter - * R Support Vector Machines Neural Networks Naive Bayes: Chapter - R Multivariate Adaptive Regression Splines: Chapter - R Evaluating Models: * Chapter - * R Scoring (R) PMML (R) Exporting Models for Deployment Advanced Analytics Text Mining: * Chapter - * R Social Network Analysis: Chapter -R Genetic Programming: Chapter -R Advanced R Strings: Chapter , R Dates and Time: * Chapter - * R Spatial Data * Chapter - * R Big Data * Chapter - * R Exploring Different Plots: Chapter - R Writing Functions: Chapter - R Parallel Processing: Chapter - R Environments: * Chapter - R Expert R Packaging (R) Pulling it Together into a Package Doing R with Style: * Chapter - * R Literate Data Science with KnitR: * Lecture - * Chapter - * R
用like的缺点是 按字精确匹配 scan则是要清楚所选词出现的具体位置 proc import dbms=excel out=ser datafile='F:\raw material\_69 of 70.xls' replace; run; data res; set ser; where job like '%manager%';/*严格按字匹配*/ run; data res2; set ser; where upcase(scan(job,2))='MANAGER';/*需要制定具体的位置，如第二个词*/ run; data res3; set ser; if upcase(substr(job,)) proc print data=res2; run;
结果不是 jo 186 . ja 2121 a joan 4695 . . 3567 fi joan 4698 m John 5463 accouting 而是 Obs Name empid department 1 Jill 1864 2 Jack 2121 accouting 3 3567 finance 4 Joan 4698 marketing 5 John 5463 accouting 因为指针是按照变量排序移动的 proc import dbms=excel out=one datafile='F:\raw material\_52 of 70_1.xls' replace; run; proc import dbms=excel out=two datafile='F:\raw material\_52 of 70_2.xls' replace; run; data all; merge one(in=o) two(in=d); by empid; if (o and not d) or (d and not o);/*其中有一个数组无效in=.,成立；注意指针下移是按照empid的排序的*/ run; proc print data=all; run;
当a b 为数值型变量时始终无法读入？？ f ilename tot 'F:\raw material\_35 of 70.txt'; data two; length a b $ 1; infile tot dsd; input a $ @1 b $ @3; ; run; proc print data=two; run;
Here Is How The World's Biggest Bond Funds (And Others, Just Not You) Get Advance Notice Of What The Fed Is About To Do Submitted by Tyler Durden on 09/30/2010 14:10 -0400 Bill Gross Bond Borrowing Costs Federal Reserve Gross Domestic Product Hyperinflation Monetary Policy PIMCO Reuters Total Return Fund Reuters has just released a stunning special report detailing how the Fed leaks all important, non-public, and ever so material, information to private parties. From the report : On August 19, just nine days after the U.S. central bank surprised financial markets by deciding to buy more bonds to support a flagging economy, former Fed governor Larry Meyer sent a note to clients of his consulting firm with a breakdown of the policy-setting meeting. The minutes from that same gathering of the powerful Federal Open Market Committee, or FOMC, are made available to the public -- but only after a three-week lag. So Meyer's clients were provided with a glimpse into what the Fed was thinking well ahead of other investors. His note cited the views of "most members" and "many members" as he detailed increasingly sharp divisions among the officials who determine the nation's monetary policy. The inside scoop, which explained how rising mortgage prepayments had prompted renewed central bank action, was simply too detailed to have come from anywhere but the Fed. A respected economist, Meyer charges clients around $75,000 for his product, which includes a popular forecasting service. He frequently shares his research with reporters, though he kept this note out of the public eye. Reuters obtained a copy from a market source. Meyer declined to comment for this story, as did the Federal Reserve. By necessity, the Fed spends a considerable amount of time talking to investment managers, bank economists and market strategists. Doing so helps it gather intelligence about the market and the economy that is invaluable in informing the bank's decisions on borrowing costs and lending programs. But a Reuters investigation has found that the information flow sometimes goes both ways as Fed officials let their guard down with former colleagues and other close private sector contacts. Frankly, we stopped right there, very much disgusted that we have been proven correct yet again when we asked rhetorically if " Bill Gross just confirmed on live TV that he has an "advance look" at non-public fed data ?". Now we know how it is that Bill Gross knew all too well that the Fed would lower its GDP expectations to 2% three weeks ahead of the minutes release. It also explains why PIMCO is ever so precise in going on margin in purchasing either bonds or MBS. **** it. This is beyond disgusting, but that is to what this bull***** country has devolved: leaking the most important decisions made on "behalf of the middle class" so that a few multi-billionaires can make a few extra soon to be worthless dollars. We will indicate if and when Pimco goes on margin next when the Total Return Fund posts its holding distribution next in mid October, telegraphing what the Fed has told it about the November FOMC meeting, but frankly at this point it is irrelevant. It is now obvious that the Fed realizes all too well that all is lost and just feeding its wealthy clients (that's right, these people are the Fed's CLIENTS ) the last remaining scraps before it pulls the hyperinflation switch. 26640 reads Printer-friendly version Send to friend Similar Articles You Might Enjoy: Watch As David Einhorn Makes A Mockery Of One-Man Fed "Expert Network" Larry Meyer Further QE3 Composition Hints As PIMCO Raises MBS Holdings To One And A Half Year High Hoenig Says Fed Should Raise Rate To 1% By End Of Summer PIMCO Treasury Holdings Plunge To Two Year Low, Cash Holdings Surge, Total Return Fund AUM At Lowest Since June 2010 Did Bill Gross Just Confirm On Live TV He Has An "Advance Look" At Non-Public Fed Data?
标签: material经管大学堂：名校名师名课
京公网安备 11010802022788号
论坛法律顾问：王进律师
知识产权保护声明
免责及隐私声明
