Discover the quickest way to Standardize Initials Release For Free

Aug 6th, 2022
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How to Standardize Initials Release For Free

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hi im steve friedman with rockstar excel this is the bonus video to standardizing names in excel here ill tell you how to convert names into first initial last name format if you havent watched the main video about standardizing names already please make sure to do so im linking to it in the video description were starting with first and last names in separate columns if youre starting with your names in a different format see the main video on how to convert them into this format from here you want to use the left function which takes the leftmost characters in a text string so you do equals left parentheses then select this and then comma 1 close parentheses and quote period space close quote and and then the last name then just fill this down and youre done so thats easy enough now obviously you cant convert from this format to any of the others because you dont have the full first name but if youre starting with this format and you want to extract the last name you can

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Data standardization is about making sure that data is internally consistent; that is, each data type has the same content and format.
The two most discussed scaling methods are Normalization and Standardization. Normalization typically means rescales the values into a range of [0,1]. Standardization typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance).
The best normalization technique is one that empirically works well, so try new ideas if you think theyll work well on your feature distribution. When the feature is more-or-less uniformly distributed across a fixed range. When the feature contains some extreme outliers.
In statistics, Standardization is the subtraction of the mean and then dividing by its standard deviation. In Algebra, Normalization is the process of dividing of a vector by its length and it transforms your data into a range between 0 and 1.
If your dataset has extremely high or low values (outliers) then standardization is more preferred because usually, normalization will compress these values into a small range.
Standardization does not change the skew of the distribution. What it does is transforming the values so it has =0 and 2=1. The actual distribution shape remains unchanged.
Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks. Standardization assumes that your data has a Gaussian (bell curve) distribution.
I will start this post with a statement: normalization and standardization will not change the distribution of your data. In other words, if your variable is not normally distributed, it wont be turn into one with the normalize method. normalize() or StandardScaler() from sklearn wont change the shape of your data.
Standardization shifts and scales a distribution; it does not change its shape. For instance, here is a histogram of Poisson distributed variables with parameter =ln2, and its standardization as per above. The standardized variables have mean 0 and variance 1, but they are certainly not normally distributed.
The difference is that: in scaling, youre changing the range of your data, while. in normalization, youre changing the shape of the distribution of your data.

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