In data analytics understanding key metrics is important for making sense of datasets. Bad at math? Dont worry tools like SQL and Excel can help! In this post, we'll break down five foundational metrics mean, median, mode, standard deviation, and percential that are essential for data analyst tool belt. These metrics will help you interpret and analyze data more effectively, whether you're working with SQL or Excel. Let's dive in!

Mean (Average)

• Definition: The sum of all data points divided by the number of points.

• Example: Calculating the average sales in a month.

• SQL Query: SELECT AVG(sales_amount) FROM sales WHERE month = 'August';

• Excel Formula: =AVERAGE(range)

Median

• Definition: The middle value in a list of numbers.

• Example: Finding the median salary of employees in a company.

• SQL Query: This can be trickier, so explain how SQL requires a slightly different approach using row numbering.

• Excel Formula: =MEDIAN(range)

Mode

• Definition: The value that appears most frequently in a dataset.

• Example: Finding the most commonly purchased product.

• SQL Query: Use GROUP BY and ORDER BY COUNT() to find the most frequent item.

• Excel Formula: =MODE.SNGL(range)

Standard Deviation

• Definition: A measure of the amount of variation or dispersion of a set of values.

• Example: Checking how consistent monthly sales are compared to the average.

• SQL Query: Some databases support the STDDEV() function.

• Excel Formula: =STDEV.P(range)

Percentile

• Definition: A value below which a given percentage of observations fall.

• Example: Finding the 90th percentile of test scores.

• SQL Query: Some databases support PERCENTILE_CONT().

• Excel Formula: =PERCENTILE.INC(range, k)

Thank you for reading! I hope this post helped you understand some the essential metrics every data analyst should know. Stay tuned for more as we work through our data analytics journey together!