Every Excel user has a favourite tip. So we decided to bring some method, data, and science to bear in this hotly debated topic, and to come up with the Top 10 Excel Features. We consulted the research of 30 of the world's leading Excel experts as well as our own internal Excel experts. After Jason highlights 'Type' as what he wants to break the data down by, Excel will create a new worksheet with a pivot table for each type of beer. Hide or unhide subtotals. We've already mentioned how Excel can save you some serious number crunching. But, this doesn't just apply to the total sum of digits.
- Advanced Excel Functions Tutorial
- Useful Resources
- Selected Reading
The Excel Database functions work with an Excel Database. This typically takes the form of a large table of Data, where each row in the table stores an individual record. Each column in the Worksheet table stores a different field for each record.
The Database functions perform basic operations, such as Sum, Average, Count, etc., and additionally use criteria arguments, that allow you to perform the calculation only for a specified subset of the records in your Database. Other records in the Database are ignored.
- Selected Reading
The Excel Database functions work with an Excel Database. This typically takes the form of a large table of Data, where each row in the table stores an individual record. Each column in the Worksheet table stores a different field for each record.
The Database functions perform basic operations, such as Sum, Average, Count, etc., and additionally use criteria arguments, that allow you to perform the calculation only for a specified subset of the records in your Database. Other records in the Database are ignored.
Database Functions
The following table lists all the Database functions −
What Is Advanced Excel Wikipedia Software
| S.No. | Function and Description |
|---|---|
| 1 | DAVERAGE Averages the values in a column of a list or database that match conditions you specify. |
| 2 | DCOUNT Counts the cells that contain numbers in a column of a list or database that match conditions you specify. |
| 3 | DCOUNTA Counts the nonblank cells in a column of a list or database that match conditions you specify. |
| 4 | DGET Returns a single value from a column of a list or database that matches conditions you specify. |
| 5 | DMAX Returns the largest number in a column of a list or database that matches conditions you specify. |
| 6 | DMIN Returns the smallest number in a column of a list or database that matches conditions you specify. |
| 7 | DPRODUCT Multiplies the values in a column of a list or database that match conditions you specify. |
| 8 | DSTDEV Estimates the standard deviation of a population based on a sample by using the numbers in a column of a list or database that match conditions you specify. |
| 9 | DSTDEVP Calculates the standard deviation of a population based on the entire population, using the numbers in a column of a list or database that match conditions you specify. Sftp box net. |
| 10 | DSUM Adds the numbers in a column of a list or database that match conditions you specify. |
| 11 | DVAR Estimates the variance of a population based on a sample by using the numbers in a column of a list or database that match conditions you specify. |
| 12 | DVARP Calculates the variance of a population based on the entire population by using the numbers in a column of a list or database that match conditions you specify. |
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Data Crunching is a method in information science which makes the preparation of automated processing of large amounts of data and information (Big Data) possible. Data Crunching consists of preparing and modelling a system or application that is used: The data is processed, sorted and structured to run algorithms and program sequences on it. The term crunched data therefore refers to data that has already been imported and processed in a system. Similar terms include data munging and data wrangling - these refer more to manual or semi-automatic processing of data, which is why they are significantly different to data crunching.
General information on the subject[edit]
The ultimate goal of data processing is deeper insight into the matter that should be conveyed with the data, such as in the field of business intelligence, so that informed decisions can be made. Other areas where data crunching applies are medicine, physics, chemistry, biology, finance, criminology, or web analytics. Depending on the context, different programming languages and tools are used: While Excel, Batch and Shell programming were used earlier; languages like Java, Python or Ruby are preferred today.
Functionality[edit]
Data crunching, however, does not refer to exploratory analysis or the visualization of data – that is done by special programs which are tailored to their area of application. Data crunching is more about correct processing, so that a system can do something with the records and the data format. Data crunching is therefore an upstream process of data analysis. This process, as the data analysis itself, can be iterative when the output of the crunching process includes new data or errors. This means that the program sequences may be repeated until the desired result is achieved: an accurate, correct data set that can be further processed directly or imported and does not contain any errors or bugs.
Practical relevance[edit]
Most data crunching tasks can be simplified into three steps. First, the raw data is read in order to convert it into a selected format as the next step. Finally, the data is output in the correct format, so it can be further processed or analyzed.[1] This trichotomy has the advantage that the individual data (input, output) can also be used for other scenarios.
Some applications of data crunching are:
Ms Excel Wikipedia
- Further processing of inherited data within a program code.
- The conversion of one format to another, for example, plain text to XML data records.
- The correction of errors in data sets, whether spelling errors or program errors.
- Extraction of raw data in order to prepare for subsequent evaluation.
As a rule, a lot of time can be saved with data crunching because the processes do not need to be performed manually. Therefore, particularly with large data sets and relational databases, data crunching can be a significant advantage. However, appropriate infrastructure is necessary to have the computing power for such operations. A system like Hadoop, for example, distributes the computer load across multiple resources and performs arithmetic processes on computer clusters. It uses the principle of division of labor.
Importance for Online Marketing[edit]
Problems in the areas of online marketing, web design, and web analytics can often be solved with data crunching. Large online shops rely on these effective methods. For example, if 10,000 records from a relational database are supposed to be automatically converted to a different format so that relevant products from the frontend can be displayed, data crunching is the method of choice. Especially in the face of Big Data, crunching of large amounts of data is of central importance. The more data that must be processed, the more time can be saved with data crunching.[2]
References[edit]
- ↑Top Ten Data Crunching Tips and Tricks onlamp.com. Accessed on 03/20/2015.
- ↑Data Crunching: Solve Everyday Problems Using Java, Python, and More media.pragprog.com. Accessed on 03/20/2015.
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