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Test Number : 70-778
Test designation : Analyzing and Visualizing Data with Potheyr BI
Vendor designation : Microsoft
Q&A : 127 Dumps Questions

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Analyzing and Visualizing Data with Potheyr BI book

the utilize of punch BI and SSRS for visualizing SQL Server and R records (part 4) | 70-778 Dumps and actual exam Questions with VCE practice Test

Visualizing statistics is vital half to realizing the dataset that they are attempting to analyses and a unique, pictoral view of the statistics. as a substitute of a tabular/matrix view or numerical views, a presentation in the benign of photographs, diagrams, and graphs can aid broaden facts insight. actually this brings the understanding of statistics to a brand new level.

to date, within the previous article they now fill mentioned how to anatomize earnings facts. And now and again, visualization is much obligatory. in this article, they will focus on two techniques visualizing the data. specifically:

  • With potheyr BI
  • With reporting functions (SSRS)
  • With R tackle for visible Studio / R Studio
  • vigtheir BI

    For this matter, they can utilize again the WideWorldImportersDW demo database and they will utilize the visualization for clustering. let us bewitch here question:

    DECLARE @SQLStat NVARCHAR(4000) SET @SQLStat = 'select SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN 4 WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''super Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Souththeyst'' THEN 7 WHEN c.[Sales Territory] = ''a long pass Theyst'' THEN 8 finish AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs subsist a section of dimension.metropolis AS c ON c.[City Key] = fs.[City Key] the place fs.[customer key] <> 0 AND c.[Sales Territory] not IN (''external'') neighborhood by means of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN 4 WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''super Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Souththeyst'' THEN 7 WHEN c.[Sales Territory] = ''a long pass Theyst'' THEN 8 conclusion ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = ftheir hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), method = ''average'') clusterCut <- cutree(clusters, 3) ggplot(income, aes(complete, amount, coltheir = revenue$SalesTerritory)) + geom_point(alpha = 0.ftheir, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''red'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown'')) dev.off() OutputDataSet <- information.frame(statistics=readBin(file(image_file, "rb"), what=uncooked(), n=1e6))' EXECUTE sp_execute_external_script @language = N'R' ,@script = @RStat ,@input_data_1 = @SQLStat ,@input_data_1_name = N'earnings' WITH sequel units ((plot varbinary(max)))

    This might subsist directly imported into punch BI in a just a miniature distinctive means. On one hand the data might subsist imported the usage of best a T-SQL query.

    opt for SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = 'Rocky Mountain' THEN 1 WHEN c.[Sales Territory] = 'Mideast' THEN 2 WHEN c.[Sales Territory] = 'New England' THEN 3 WHEN c.[Sales Territory] = 'Plains' THEN ftheir WHEN c.[Sales Territory] = 'Southeast' THEN 5 WHEN c.[Sales Territory] = 'extraordinary Lakes' THEN 6 WHEN c.[Sales Territory] = 'Souththeyst' THEN 7 WHEN c.[Sales Territory] = 'a ways Theyst' THEN 8 conclusion AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs relate dimension.city AS c ON c.[City Key] = fs.[City Key] where fs.[customer key] <> 0 AND c.[Sales Territory] now not IN ('external') community with the aid of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = 'Rocky Mountain' THEN 1 WHEN c.[Sales Territory] = 'Mideast' THEN 2 WHEN c.[Sales Territory] = 'New England' THEN 3 WHEN c.[Sales Territory] = 'Plains' THEN 4 WHEN c.[Sales Territory] = 'Southeast' THEN 5 WHEN c.[Sales Territory] = 'great Lakes' THEN 6 WHEN c.[Sales Territory] = 'Souththeyst' THEN 7 WHEN c.[Sales Territory] = 'far Theyst' THEN eight conclusion

    And later one at a time the R code:

    clusters <- hclust(dist(sales[,c(1,3,5)]), system = ''common'') clusterCut <- cutree(clusters, 3) ggplot(sales, aes(total, amount, coltheir = earnings$SalesTerritory)) + geom_point(alpha = 0.4, size = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''red'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown''))

    After opening vigtheir BI, they elect pickup information -> SQL Server and insert grievous vital advice, as proven in the print screen beneath:

    After clicking adequate, statistics will subsist imported into potheyr BI. subsequent step is to opt for  “New visual” and on the visualizations record elect the R script visible.

    You may pickup a dialog window asking for enabling R visualization. After that, select the variables needed for the graph. in accordance with the R code, they're the utilize of columns income, quantity and SalesTerritoryID. grievous three columns will materialize in a predefined dataset as an information.body that the R visualization is developing by means of default:

    within the R-script code they can paste the R code from the illustration above. starting with R code, they want some minor modifications – change the designation of dataset and rename the columns. So from this:

    library(ggplot2) clusters <- hclust(dist(earnings[,c(1,3,5)]), formula = ''commonplace'') clusterCut <- cutree(clusters, 3) ggplot(income, aes(total, quantity, coltheir = sales$SalesTerritory)) + geom_point(alpha = 0.ftheir, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''purple'', ''eco-friendly'',''yellow'',''blue'',''lightblue'',''magenta'',''brown''))

    into this:

    library(ggplot2) clusters <- hclust(dist(dataset[,c(1,2,3)]), components = 'commonplace') clusterCut <- cutree(clusters, 3) ggplot(dataset, aes(earnings, volume, coltheir = dataset$SalesTerritory)) + geom_point(alpha = 0.ftheir, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c('black', 'crimson', 'green','yellow','blue','lightblue','magenta','brown'))

    also, beget inevitable you check the costs or double quotes around the declared values in R code. After that, you'll pickup a visualization in energy BI:

    additionally, any variety of other R graph / visualization may moreover subsist introduced. for this understanding I actually fill determined so as to add a boxplot on income per revenue territory, the utilize of R visualizer. So adding additional information to the cluster evaluation with boxplot will provide end-person further perception of how profit is spread among sales territories.

    The graph is got using perquisite here R code:

    with the aid of doing so, what they requisite in the final step is so as to add some facts slicers, to subsist able to dynamically refresh the data on both R visualizations. Of route, every other pre-prepared visualization accessible in energy BI can additionally utilize the capabilities of information slicers. I added two slicers:  volume and revenue Territory. i can select (or multi-choose) the values and according to my decisions grievous graphs which are in accordance with dataset the utilize of these slicers might subsist automatically up to date.

    The finish product can resemble a dashboard or a document or a playground for data scientist, records wranglers or any one fervent to pickup insight on the statistics set:

    I brought the R Language emblem just to divulge that ytheir company brand will moreover subsist added as neatly. The comprehensive vigtheir BI workbook is moreover obtainable for download.

    if you wish to Do extra superior analytics on the imported dataset, the site are always extra visuals available on the url handle: https://app.potheyrbi.com/visuals/ . the entire additional visuals can moreover subsist downloaded and introduced into ytheir punch BI booklet. There are also  R-potheyred visuals to subsist downloaded and ready to subsist used. For this text, I actually fill determined to down load further visual Chord:

    And utilize it in present e-book, via importing it:

    And populate it with information with earnings and revenue Territory to ogle the distribution of earnings by means of territory.

    again, the photograph tells greater than numbers, but beget certain not to overdo on the visuals or create non-sense graphs which might antecedent people to subsist stuck knowing the graph, as a substitute of assisting them subsist aware.

    I actually fill delivered and made comparison for affiliation rules; really just assessment bettheyen the visualizations. i am simply including the ultimate comparison from energy BI ebook, the rest that you can comply with in the energy BI ebook. The R code is in reality the identical because the R code in former article, where I originally mentioned association suggestions. perquisite here is the last define of the comparison of two visuals: R code visible and association guidelines visible.

    theyll-nigh, both are in accordance with equal R packages (arules, aRulesVisual), simply that graph on left hand aspect to subsist configured through code and the outlook can moreover subsist modified perquisite down to final element, whereas, graph on perquisite hand side is embedded visual, moreover in line with R, with grievous of the parameters construct in to subsist installation in potheyr BI.

    an additional very crucial factor and extremely positive method to import in energy BI are moreover R records.frames, which are desk presentation of the information. now and again, if you befall to are doing any superior analytics or visualizations, you may moreover want to add some additional info that can not subsist study from the graph. as an instance, let’s bewitch the clustering graph; cluster businesses are very quality and visual, hotheyver a person who is extra into information would want to pickup some data to this clustering as neatly. in this case, they will utilize R visual once again, hotheyver maneuver and wrangle the outputted records into the dataframe and fill it characterize as a desk.

    For cluster evaluation, I fill added the variety of occurrences for each and every customer in income territory wherein cluster (1, 2 or three) is appearing. desk is just a distinct presentation of graph.

    The table has been generated with here R code in vigtheir BI:

    library(gridExtra) clusters <- hclust(dist(dataset[,c(1,2,3)]), components = 'regular') clusterCut <- cutree(clusters, three) df <- records.body(table(clusterCut, dataset$SalesTerritory)) grid.desk(df)

    once more, this desk will automatically replace, based on chosen criteria within the slicers, which comes very convenient when analyzing facts.

    As you can see, I fill used the tackle gridExtra, very efficient kit to serve the direct of plotting the tables with records extracted from R datasets.

    Reporting capabilities (SSRS)

    Reporting functions has been round very lengthy, so i will not fade into the reporting details. but the entire R visualizations available in punch BI can even subsist implemented and utilized in SSRS. Reporting features brings additionally an further purposeful and valuable feature: choices. potheyr BI offers you the slicers that may, in line with alternative, dynamically update the graphs, records and visualizations in R script, that may even subsist achieved with choices in SSRS. but what SSRS makes it viable for is, to add any extra parameter into the code, making R Language through SSRS even more bendy to the finish consumer. i'll interpret this on an example with the clustering analysis from already mentioned case (and accustomed from former article).

    When growing new report and defining dataset, replica/pasting the R code into the question text:

    will revert – every time document might subsist achieved – an error and the textboxes to populate the values of the entire parameters mandatory in order to execute externa method sp_execute_external_script. So, the stronger (if not the most reliable) pass to preserve away from this, is conveniently to reclaim the entire code as a stored system.

    CREATE system ClusterAnalysis_Plot AS DECLARE @SQLStat NVARCHAR(4000) SET @SQLStat = 'choose SUM(fs.[Profit]) AS earnings ,c.[Sales Territory] AS SalesTerritory ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN ftheir WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''atheysome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Souththeyst'' THEN 7 WHEN c.[Sales Territory] = ''a ways Theyst'' THEN 8 finish AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS volume FROM [Fact].[Sale] AS fs subsist section of dimension.city AS c ON c.[City Key] = fs.[City Key] the place fs.[customer key] <> 0 AND c.[Sales Territory] now not IN (''exterior'') community by pass of c.[Sales Territory] ,fs.[Customer Key] ,CASE WHEN c.[Sales Territory] = ''Rocky Mountain'' THEN 1 WHEN c.[Sales Territory] = ''Mideast'' THEN 2 WHEN c.[Sales Territory] = ''New England'' THEN three WHEN c.[Sales Territory] = ''Plains'' THEN ftheir WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''atheysome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Souththeyst'' THEN 7 WHEN c.[Sales Territory] = ''a long pass Theyst'' THEN 8 finish ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = ftheir hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), formula = ''typical'') clusterCut <- cutree(clusters, three) ggplot(earnings, aes(profit, quantity, coltheir = earnings$SalesTerritory)) + geom_point(alpha = 0.ftheir, dimension = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c(''black'', ''crimson'', ''green'',''yellow'',''blue'',''lightblue'',''magenta'',''brown'')) dev.off() OutputDataSet <- data.frame(data=readBin(file(image_file, "rb"), what=uncooked(), n=1e6))' EXECUTE sp_execute_external_script @language = N'R' ,@script = @RStat ,@input_data_1 = @SQLStat ,@input_data_1_name = N'revenue' WITH influence units ((plot varbinary(max)));

    and then you effectively add the process.

    On record canvas, you add picture and Do the following homes:

    Now retailer, (install) and Execute the document  in Reporting provider. you should descry a intimate graph in SSRS.

    using saved methods in SSRS is a extremely productive pass to visualize the information. using saved techniques that you would subsist able to additionally implement the parameters that without detain inject some values or chunk of codes to beget ytheir visualizations greater dynamic. moreover that, producing tables is plenty less difficult finished in evaluation to punch BI, but if you are looking to extract information from R consequences, subsist certain you grievous the time revert the R consequences to a erudition body. information corpse is a data classification, that R and SQL Server can operate with each and every other.

    R tackle for visual Studio / R Studio

    one pass to imagine the records is to pickup information into ytheir RTVS or R Studio the site that you would subsist able to visualize information. continually this can moreover subsist finished by storing ytheir records from SQL Server into any of those two ctheirses. one pass to Do it's to beget utilize of ODBC driver. Very advantageous and potheyrful is accessible in kit RODBC. The very straightforward syntax is:

    library(RODBC) myconn <-odbcDriverConnect("driver=SQL Server;Server=Srv_Tomaz;database=WideWorldImportersDW;trusted_connection=true") mydata <- sqlQuery(myconn, "opt for * FROM [Fact].[Order]") close(myconn)

    After working this R Code, you might pickup the total fact table into ytheir R environment.  Now working on any sort of visualization is relatively easy.

    Please word, in case you can subsist using larger dataset and importing records into R ambiance, with RODBC equipment, you may additionally adventure reminiscence obstacles. In such situations, i would strongly recommend the usage of RevoScaleR kit, that solves reminiscence hindrance issues!

    Conclusion

    records visualization is a crucial section of information analysis, facts presentation and facts comprehension. grievous three tackle are a method how to assist an information wrangled, information scientist or statistics analyst to address and to deal with plotting facts. there are lots of alternative ways, and third celebration equipment, that allows for you to deepen the information perception, just result a simple rule, when using visuals: statistics visible requisite to clarify and broaden the statistics understanding. If this fundamental rule is not satisfied, definitely you're doing anything wrong. I didn't fade into the concept of visualization, given that this was now not my point, hotheyver I strongly recommend you to Do it. Very decent region to birth is statistics Visualization Catalogue.

    author: Tomaz Kastrun (tomaz.kastrun@gmail.com)

    Twitter: @tomaz_tsql

    theyblog: http://tomaztsql.wordpress.com


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    References :


    Box.net : https://app.box.com/s/v4oew0iwpgid9xnvzxc7g42z1v1vrqob
    zoho.com : https://docs.zoho.com/file/67jzb1e92f64080914144ad5c7bb21981d8fb
    Calameo : http://en.calameo.com/books/0049235263b14fbf4f712
    MegaCerts.com Certification exam dumps






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