Experience is a great teacher. And in my experience, the beginning has always been tough.

As a BI consultant & trainer, I have worked with various people: from the frontline employees to CEOs. Our interactions gave me great insights into the multiple challenges they face.

I have put together a few pages from my experience and learning; covering Power BI, Excel, Power Query, Power Pivot, Tableau, data analytics & data visualization.

The purpose of the blog is to get you started.

I hope you’ll find them helpful.

Vivek

Statistics Simplified is the series to express statistics in layman terms.


Any data set should be analyzed for its central tendency and variation. Why variation? What benefits will we get by looking at variation?

Let us consider this scenario: you have come across a river which can be crossed on foot as there is no bridge. You do not know swimming, and the current in the river is calm. There is a board at the river’s bank denoting the average depth as 3 feet.


You are 5.8 feet tall.


Will you cross the river?

In our day to day lives, we usually look at the average for performance comparison and decision-making.


It is a major flaw of our thought process as we ignore another critical aspect of data property: variation.



And we call such thought process as “Flaw of Averages”.

Had there been additional details like maximum depth: 8 ft., then would you have crossed the river?



Considering the variation in the data helps in the wiser decision.

What is the variation?

It is a measurement of the distance between the data points within a given data set.


Measures of Variation

Popular ways to measure variations are Standard Deviation, Inter-Quartile Range (IQR), and Range.

· Range: Difference between maximum & minimum value.

· Standard Deviation: Average distance of data points from each other.

· Inter Quartile Range (IQR): Difference between 75th percentile and 25th percentile, where percentile is the position of data points when arranged in an order. Median is the 50th percentile.


Also see: Central Tendency


#vivran

Statistics Simplified is the series to express statistics in layman terms.


A single value that attempts to describe a set of data by identifying the central position, within that set of data.


One point in the data set which balances the entire data set.


Central Tendency is also known as Measure of Central Location or more accessible, average.

Measures of Central Tendency

There are three measures of central tendency: Mean, Median, and Mode

The most popular measure of central tendency is Arithmetic Mean, which is also represented with the formula AVERAGE in Excel.


Depending on the data type, we use an appropriate measure of central tendency


Also see:

Variation

Mean vs. Median

Trim Mean

Geometric Mean



#vivran

Statistics Simplified is the series to express statistics in layman terms.


Identifying data types is crucial in data analytics. Wisdom says that we should know the data type before we start the data analysis process. And the reasons are apparent. If we understand the data type, then we can apply appropriate mathematical aggregations and statistical tests.

We categorize data into two primary categories: Qualitative & Quantitative

We can understand data types by the following example:

Discrete data types primarily contain count and percentages.

The fundamental difference between a continuous and a discrete data type is that continuous data type is always associated with a unit or a scale, e.g., kilogram, meter, centimeter, degree Celsius, years.

Each data type has its level of measurement:


And depending on the data type, we can decide on the underlying mathematical aggregations:


Also see: Central Tendency & Variation



#vivran

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