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Carola Oriane May 3, 2021 Spreadsheet

He grossed $2,000 a week for his bosses, and earned slightly less than $500 for himself. Still, the wages kept him in seeds, bowling shoes, stick pins, and a Platinum Buddy Holly Fan Club Membership. Lester‘s favorite word was ”crapola,” and he applied it to the ball bearing factory‘s antiquated data processing system in coats as thick as the olive drab membrane clinging to the smudgy glass before him. ”You piteous piece of crapola!” he‘d hiss at the computer when error messages flashed across its screen or its ancient system locked under the demand of crunching numbers to the tenth decimal point. ”Some day I‘ll throw your sorry ass into one of those melting pots out there!”

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Now switch to your Excel spreadsheet and select the entire data table which you have created as the basis of your invoice. Either click the ‘copy‘ button or simply right click and select ‘copy‘, then transfer to your Word document and place the cursor where you wish to paste the spreadsheet. What you do next really depends upon which version of Microsoft Word you are using. In Word 2007 you need to click the drop down arrow on the ‘paste‘ button, which is the first icon on the ‘home ‘ribbon. Now you need to select ‘paste special‘ and in the dialogue box choose ‘paste link‘ and select Microsoft Excel Worksheet Object. If you are using Microsoft Word 2003, click ‘edit‘ on the menu bar and select ‘paste special‘. The resulting dialogue box is the same as for Word 2007.

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Spreadsheets such as Microsoft Excel are well suited to tasks involving the manipulation of small amounts of related data. Working out a budget, producing visual reports, organizing lists and calculations that involve many variables are all tasks well suited to a spreadsheet. There are some data related tasks however that spreadsheets such as Microsoft Excel are not suited for. Tasks involving the processing and combination of large sets of data for example are generally not well suited to spreadsheets. There is another technology with a long history and theoretical background that specializes in these sorts of tasks. That technology is relational databases. The most common way people insert data into and extract data from relational databases is via the language of Structured Query Language.

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The former hangs like a transparent curtain four feet above the floor and shrink-wraps itself to anyone bold enough to attempt passing through. The latter represents a fragrant blend of beer, cheap cologne, and unkempt toilets, and assaults an unsuspecting visitor‘s nose like an aggressive index finger. By Saturday, the fragrance would be pungent enough to cause mere mortals to speak in tongues. Lance led the way with Lester in tow, dodging around dark figures that emerged from the nicotine and odoriferous fog. Lester had difficulty keeping up, licking the lenses of his glasses and tie-drying them as they wove their ways toward diffused light they assumed was the bar area where lusty women awaited.

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Whilst Excel cannot clean or structure all of your data for you it does come with some useful functionality for manipulating and analysing clean and structured data sets. This in-built functionality includes pivot tables, sorting and filtering. Filtering alone is a powerful tool and can help to quickly isolate data based on specified criteria. But what happens if your data is clean but not very structured (a common problem). For instance what if you, a client or your team is using colours, fonts or some kind of formatting to classify data in an Excel spreadsheet. In short, you wont be able to filter the data, because Excel‘s in-built filtering logic requires rules based on numbers, dates and text only. It will not perform filtering based on formats. In addition Excel filtering only applies down rows. It will not perform filtering across columns.

As a set of general rules data is most useful when things like text fields hold only names as well as meaningful and validated codes, categories and classifications. Text notes and other free form text should be isolated to a dedicated notes field and thus separated from other numeric data. Numeric fields should hold only numeric values (numbers, dates, %‘s and in the correct quantum or magnitude with no text prefixes, suffixes, spaces, text elements or text notes present. You must also be careful that numeric data is not stored as text and it should be internally consistent in terms of the correct format so that it can be used in calculations or for comparison and queries. Finally, addresses should be separated out into multiple fields such as street address, town /suburb, state / province, postal code and country to allow for geographic analysis and mail outs if required. Fixing up a data set to meet these criteria is called data scrubbing, cleansing or massaging. This data cleansing process can be very time consuming even for an experienced Microsoft Excel user, database engineer, business analyst or computer programmer.

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