tableau scorecard

product form it is trying to provide behind the name. And the wheels, the business intelligence systems, what's the problem? There are many commercial BI products, foreign more well-known products, such as Tableau, Qlikview, Power BI, domestic claims have been agile, such as Yonghong BI, sail soft Finebi and BDP. In addition, there are products from the Internet industry Company's new recruits, such as Aliyun's quick BI, NetEase's netease

If each row of a matrix is strictly monotonically incremented, we call the matrix the young Tableau. For the young matrix (a[m][N]), it usually involves two questions: (1) How to find an element x in the young matrix. (2) How to find the number of K large in the young matrix. 2. The solution The young matrix is a very ingenious data structure, which can be used as a heap and as a balance tree. (1) Problem 1 solution "Programme One" For the young mat

While there are few BI suite on the market, such as MicroStrategy and Pentaho, they have been competing with SAP's business Objects BI Suite. Many of the more leading BI tool vendors, such as tableau or qlikview, may be able to surpass SAP in some areas, but some of the features of SAP BI Suite are still not achievable. The biggest advantage of SAP BI Tools is its deep integration with other SAP applications, including SAP Business Suite, ERP, and E

tools The previous tools are based on a prior model and have been used to detect known patterns and rules. Automatic Processing of complex, heterogeneous, and large-scale data often fails, therefore, we need to visualize the data analysis process to better explore patterns and find problems. Currently, the best data analysis visualization tools on the market are tableau and ggplot2 in R. These products have the following advantages: It is closely r

; 'syni ', 87 'oases' => 'oases', 88 'octopus' => 'octopi ', // octopuses 89 'ovum' => 'ova ', 90 'ox '=> 'oxen', 91 'paralysis' => 'paralyses ', 92 'parenthesis' => 'parentheses ', 93 'person' => 'people ', 94 'phenomenon' => 'phenomena', 95 'potato '=> 'potates', 96 'radius' => 'radii ', // radiuses 97 'scraise '=> 'scafs', // scarves 98 'self' => 'selves ', 99 'series' => 'series ', 100 'sheep '=> 'sheep', 101 'shelf' => 'shelves ', 102 'sciss' => 'sciss', 103 'species' => 'species ', 104 'st

There are many visual tools, such as tableau, a variety of JS framework, I personally feel it should be the best to learn JS, because JS does not need the environment, every computer has a browser, and like matplotlib need a development environment such as python, or more troublesome, But after all, using Python to process the data, this article is to write some of their own study notes, of course, know what the most important painting (I do not know-

and quantitative)This is big data. If you have a quantitative reasoning background and a degree in math or statistics, then you're half done. Plus, with some experience using statistical tools such as R, SAS, Matlab, SPSS, or Stata, you'll be able to lock in these jobs. In the past, many quantitative engineers have chosen to work on Wall Street, but with the rapid development of big data, it is now necessary to have a large number of geeks with a quantitative background.SqlThe data-centric lang

need. Leaflet is designed as an easy-to-use, performance-friendly tool. Thanks to HTML5 and CSS3, it supports all major computer and mobile platforms. If you also want to expand the app, there are a number of plugins available to install.URL please poke Download–leafletTableau PublicIt's an easy-to-operate app that can help you create a visually stunning piece of work. Just open the data and explore it with Tableau's desktop version. Then, store the visualizations in your 1GB

MOLAP Cube Seamlessly integrates with other BI tools, such as tableau, while MicroStrategy and Excel will soon be available Job Management and monitoring Compression and encoding support Incremental update of Cube Leverage HBase coprocessor for query latency Approximate Query Capability for distinct Count (Hyperloglog) Easy-to-use Web management, build, monitor, and query the cube interface Security capability to s

When "web Analytics" develops into "data intelligence", there is no doubt that data analysts should also have some changes in their work, in the past, primarily web-centric and channel-specific tactics, while in the future they need to be responsible for more strategic, business-oriented and (large) data expertise.The primary focus of data analysts should not be on lower-level infrastructure and tool development. The following points are the opportunity areas for data analysis:1. Processing: Mas

, thanks to open source projects such as Impala, published by Cloudera, SQL has been reborn as a common language for the next generation of Hadoop-sized data warehouses. 7. Data visualization (visualization) Big Data may not be easy to understand, but in some cases attracting eyeballs through fresh data is still an irreplaceable method. You can always use multivariate or logistic regression analysis to parse data, but sometimes using a visualizer like Ta