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Test Number : 70-778
Test denomination : Analyzing and Visualizing Data with Power BI
Vendor denomination : Microsoft
braindumps : 127 Dumps Questions

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Analyzing and Visualizing Data with Power BI book

the utilize of vigor BI and SSRS for visualizing SQL Server and R records (part 4) | 70-778 Dumps and existent exam Questions with VCE drill 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 kind 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 resolve earnings facts. And now and again, visualization is much obligatory. in this article, they will focus on two techniques visualizing the data. specifically:

  • With power BI
  • With reporting functions (SSRS)
  • With R rig for visible Studio / R Studio
  • vigour BI

    For this matter, they can utilize again the WideWorldImportersDW demo database and they will utilize the visualization for clustering. let us prefer 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] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long passage West'' THEN 8 nearby AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs subsist a Part 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] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long passage West'' THEN 8 conclusion ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = four hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), routine = ''average'') clusterCut <- cutree(clusters, 3) ggplot(income, aes(complete, amount, colour = revenue$SalesTerritory)) + geom_point(alpha = 0.four, 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 result units ((plot varbinary(max)))

    This might subsist directly imported into vigor BI in a just a dinky 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 four WHEN c.[Sales Territory] = 'Southeast' THEN 5 WHEN c.[Sales Territory] = 'extraordinary Lakes' THEN 6 WHEN c.[Sales Territory] = 'Southwest' THEN 7 WHEN c.[Sales Territory] = 'a ways West' THEN 8 conclusion AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS amount FROM [Fact].[Sale] AS fs associate 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] = 'Southwest' THEN 7 WHEN c.[Sales Territory] = 'far West' 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, colour = 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 vigour BI, they choose obtain information -> SQL Server and insert outright vital advice, as proven in the print screen beneath:

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

    You may obtain a dialog window asking for enabling R visualization. After that, select the variables needed for the graph. in accordance with the R code, we're the utilize of columns income, quantity and SalesTerritoryID. outright three columns will appear 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 denomination 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, colour = sales$SalesTerritory)) + geom_point(alpha = 0.four, 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, colour = dataset$SalesTerritory)) + geom_point(alpha = 0.four, measurement = 2.5) + geom_point(col = clusterCut) + scale_color_manual(values = c('black', 'crimson', 'green','yellow','blue','lightblue','magenta','brown'))

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

    additionally, any variety of other R graph / visualization may furthermore subsist introduced. for this intuition 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 necessity 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 outright graphs which are in accordance with dataset the utilize of these slicers might subsist automatically up to date.

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

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

    if you wish to finish extra superior analytics on the imported dataset, the residence are always extra visuals available on the url handle: https://app.powerbi.com/visuals/ . the entire additional visuals can furthermore subsist downloaded and introduced into your vigor BI booklet. There are also  R-powered 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 Look the distribution of earnings by means of territory.

    again, the photograph tells greater than numbers, but earn sure not to overdo on the visuals or create non-sense graphs which might occasions 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 between the visualizations. i am simply including the ultimate comparison from energy BI ebook, the repose that you can comply with in the energy BI ebook. The R code is in reality the identical because the R code in stale article, where I originally mentioned association suggestions. perquisite here is the ultimate define of the comparison of two visuals: R code visible and association guidelines visible.

    well-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 furthermore subsist modified perquisite down to final element, whereas, graph on perquisite hand side is embedded visual, furthermore in line with R, with outright of the parameters construct in to subsist installation in power BI.

    an additional very crucial factor and extremely positive routine to import in energy BI are furthermore R records.frames, which are desk presentation of the information. now and again, if you betide to are doing any superior analytics or visualizations, you may furthermore want to add some additional info that can not subsist study from the graph. as an instance, let’s prefer the clustering graph; cluster businesses are very property and visual, however a person who is extra into information would want to obtain some data to this clustering as neatly. in this case, they will utilize R visual once again, however 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 several presentation of graph.

    The table has been generated with here R code in vigour 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 rig gridExtra, very effective kit to serve the level of plotting the tables with records extracted from R datasets.

    Reporting capabilities (SSRS)

    Reporting functions has been round very lengthy, so i will not depart into the reporting details. but the entire R visualizations available in vigor BI can even subsist implemented and utilized in SSRS. Reporting features brings additionally an further purposeful and valuable feature: choices. power 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 workable for is, to add any extra parameter into the code, making R Language through SSRS even more bendy to the nearby consumer. i'll warrant this on an illustration with the clustering analysis from already mentioned case (and accustomed from stale article).

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

    will return – every time document might subsist achieved – an mistake and the textboxes to populate the values of the entire parameters mandatory in order to execute externa routine sp_execute_external_script. So, the stronger (if not the most reliable) passage to retain 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 four WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''awesome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a ways West'' THEN 8 nearby AS SalesTerritoryID ,fs.[Customer Key] AS CustomerKey ,SUM(fs.[Quantity]) AS volume FROM [Fact].[Sale] AS fs subsist Part 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 passage 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 four WHEN c.[Sales Territory] = ''Southeast'' THEN 5 WHEN c.[Sales Territory] = ''awesome Lakes'' THEN 6 WHEN c.[Sales Territory] = ''Southwest'' THEN 7 WHEN c.[Sales Territory] = ''a long passage West'' THEN 8 nearby ;' DECLARE @RStat NVARCHAR(4000) SET @RStat = 'library(ggplot2) image_file <- tempfile() jpeg(filename = image_file, width = four hundred, top = 400) clusters <- hclust(dist(revenue[,c(1,3,5)]), formula = ''typical'') clusterCut <- cutree(clusters, three) ggplot(earnings, aes(profit, quantity, colour = earnings$SalesTerritory)) + geom_point(alpha = 0.four, 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 finish the following homes:

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

    using saved methods in SSRS is a extremely productive passage to visualize the information. using saved techniques that you would subsist able to additionally implement the parameters that without slow inject some values or chunk of codes to earn your visualizations greater dynamic. moreover that, producing tables is plenty less difficult finished in evaluation to vigor BI, but if you are looking to extract information from R consequences, subsist sure you outright the time return the R consequences to a scholarship body. information carcass is a data classification, that R and SQL Server can operate with each and every other.

    R rig for visual Studio / R Studio

    one passage to imagine the records is to obtain information into your RTVS or R Studio the residence that you would subsist able to visualize information. continually this can furthermore subsist finished by storing your records from SQL Server into any of those two courses. one passage to finish it's to earn utilize of ODBC driver. Very advantageous and powerful 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 obtain the total fact table into your 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 recollection obstacles. In such situations, i would strongly recommend the usage of RevoScaleR kit, that solves recollection hindrance issues!

    Conclusion

    records visualization is a crucial Part of information analysis, facts presentation and facts comprehension. outright three rig are a routine 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 ensue a simple rule, when using visuals: statistics visible necessity to clarify and broaden the statistics understanding. If this fundamental rule is not satisfied, definitely you're doing anything wrong. I didn't depart into the concept of visualization, given that this was now not my point, however I strongly recommend you to finish it. Very decent region to birth is statistics Visualization Catalogue.

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

    Twitter: @tomaz_tsql

    weblog: 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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