Continuing On This SQL Epic Data Adventure
Welcome back fellow SQL enthusiasts to the second part of this article in this series. Is SQL up to Snuff when it comes to Statistical Analysis?
Statistical Functions
Summary statistics are essential for understanding the central tendency, variability, and distribution of your data. Here's how you can calculate summary statistics using SQL
Mean
Calculated by summing up all values in a column and dividing by the total number of values.
Median
The middle value of a sorted dataset. If the dataset has an odd number of values, the median is the middle one. If it has an even number of values, the median is the average of the two middle values.
Mode
The value that appears most frequently in a dataset.
Standard Deviation
It measures the dispersion of values from the mean. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.
Variance
It measures how much the values in a dataset vary from the mean.
Coefficient of Variation (CV)
The ratio of the standard deviation to the mean. It is expressed as a percentage and used to compare the variability of datasets with different units or scales.
Range
The difference between the maximum and minimum values in a dataset.
Quantiles
Divide a dataset into equal parts.
Interquartile Range (IQR)
The difference between the upper quartile (Q3) and the lower quartile (Q1) in a dataset.
Percentiles
The percentage of data points that fall below a given value in a dataset.
Z Score
Measures the number of standard deviations a data point is from the mean of a dataset. It's useful for understanding how far away a particular value is from the mean in terms of standard deviation units.
Math Functions
Addition (+)
Adds two numbers together.
Subtraction (-)
Subtracts one number from another.
Multiplication (*)
Multiplies two numbers together.
Division (/)
Divides one number by another
Exponentiation (^ or POWER())
Raises a number to the power of another number.
Square Root (SQRT()):
Calculates the square root of a number.
Exploratory Data Analysis (EDA)
With all of the hard work out of the way, we can now move on to putting the knowledge to use by using EDA and how it's essential for gaining insights into your dataset and laying the groundwork for further analysis.
In my opinion, EDA is one of the most critical steps in the data analysis process. It allows you to explore and understand the characteristics of your data, and if some housekeeping is in order before diving into more complex analyses. EDA can be broken down into these steps.
Summary Statistics
Calculate summary statistics for numeric variables, such as mean, median, standard deviation, minimum, maximum, and quartiles.
Data Visualization
Create visualizations to explore the distribution of your data and identify patterns or outliers. Common plots include histograms, box plots, scatter plots, and density plots.
Handling Missing Values
Check for missing values in your dataset and decide on an appropriate strategy for handling them. You may choose to impute missing values, remove rows or columns with missing data, or leave them as-is depending on the context.
Explore Relationships
Investigate relationships between variables using scatter plots, correlation matrices, or pair plots. Look for trends, patterns, or dependencies that can provide insights into your data.
Categorical Variables
For categorical variables, examine the frequency distribution of each category using bar plots or pie charts. This helps understand the distribution of different categories and their relative proportions.
Feature Engineering
Create new variables or transform existing ones to better capture the underlying patterns in your data. This might involve creating interaction terms, binning variables, or applying transformations like logarithms.
Outlier Detection
Identify outliers in your data that may skew your analysis. You can use visualization techniques like box plots or statistical methods such as Z-score or IQR (Interquartile Range) to detect outliers.
EDA Allows you to answer these questions through the process:
What is the distribution of each variable?
Are there any missing values or outliers?
Are there any patterns or trends in the data?
Are there any relationships or correlations between variables?
Are there any interesting subsets or clusters within the data?
Overall, EDA provides a comprehensive overview of your dataset, helping you make informed decisions and guiding you toward further analysis or modeling. It's a crucial step in the data analysis process that empowers you to extract meaningful insights and drive actionable outcomes from your data.
That's it for this part. In the final part, we will be going over each step of EDA using SQL
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