Datatron Blog

Stay Current with AI/ML

Blog Category

Data ScienceData ScienceData ScienceData Science

Basic Statistics You NEED to Know for Data Science

The purpose of this is to provide a comprehensive overview of the fundamentals of statistics that you’ll need to start your data science journey. There are many articles already out there, but I’m aiming to make this more concise.

Measures of Central Tendency

Mean: the average of a dataset.
Median: the middle of an ordered dataset; less susceptible to outliers.
Mode: the most common value in a dataset; only relevant for discrete data.


Measures of Variability

Range: the difference between the highest and lowest value in a dataset.
Variance (σ2): measures how spread out a set of data is relative to the mean.


Standard Deviation (σ): another measurement of how spread out numbers are in a data set; it is the square root of variance.
Z-score: determines the number of standard deviations a data point is from the mean.

formula z

R-Squared: a statistical measure of fit that indicates how much variation of a dependent variable is explained by the independent variable(s); only useful for simple linear regression.

variation formula

Adjusted R-squared: a modified version of r-squared that has been adjusted for the number of predictors in the model; it increases if the new term improves the model more than would be expected by chance and vice versa.

Measurements of Relationships between Variables

Covariance: Measures the variance between two (or more) variables. If it’s positive then they tend to move in the same direction, if it’s negative then they tend to move in opposite directions, and if they’re zero, they have no relation to each other.


Correlation: Measures the strength of a relationship between two variables and ranges from -1 to 1; the normalized version of covariance. Generally, a correlation of +/- 0.7 represents a strong relationship between two variables. On the flip side, correlations between -0.3 and 0.3 indicate that there is little to no relationship between variables.

correlation formula

Probability Distribution Functions

Probability Density Function (PDF): a function for continuous data where the value at any point can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample. (Wiki)
Probability Mass Function (PMF): a function for discrete data which gives the probability of a given value occurring.
Cumulative Density Function (CDF): a function that tells us the probability that a random variable is less than a certain value; the integral of the PDF.

Continuous Data Distributions

Uniform Distribution: a probability distribution where all outcomes are equally likely.
Normal/Gaussian Distribution: commonly referred to as the bell curve and is related to the central limit theorem; has a mean of 0 and a standard deviation of 1.

T-Distribution: a probability distribution used to estimate population parameters when the sample size is small and/r when the population variance is unknown (see more here).
Chi-Square Distribution: distribution of the chi-square statistic (see here).

Discrete Data Distributions

Poisson Distribution: probability distribution that expresses the probability of a given number of events occurring within a fixed time period.
Binomial Distribution: a probability distribution of the number of successes in a sequence of n independent experiences each with its own Boolean-valued outcome (p, 1-p).


Moments describe different aspects of the nature and shape of a distribution. The first moment is the mean, the second moment is the variance, the third moment is the skewness, and the fourth moment is the kurtosis.


Probability is the likelihood of an event occurring.
Conditional Probability [P(A|B)] is the likelihood of an event occurring, based on the occurrence of a previous event.
Independent events are events whose outcome does not influence the probability of the outcome of another event; P(A|B) = P(A).
Mutually Exclusive events are events that cannot occur simultaneously; P(A|B) = 0.
Bayes’ Theorem: a mathematical formula for determining conditional probability. “The probability of A given B is equal to the probability of B given A times the probability of A over the probability of B”.

P A B Formula


True positive: detects the condition when the condition is present.
True negative: does not detect the condition when the condition is not present.
False-positive: detects the condition when the condition is absent.
False-negative: does not detect the condition when the condition is present.
Sensitivity: also known as recall; measures the ability of a test to detect the condition when the condition is present; sensitivity = TP/(TP+FN)
Specificity: measures the ability of a test to correctly exclude the condition when the condition is absent; specificity = TN/(TN+FP)


Predictive value positive: also known as precision;the proportion of positives that correspond to the presence of the condition; PVP = TP/(TP+FP)
Predictive value negative: the proportion of negatives that correspond to the absence of the condition; PVN = TN/(TN+FN)

predictive value

Hypothesis Testing and Statistical Significance

Check out my article ‘Hypothesis Testing Explained as Simply as Possible’ for a deeper explanation here.

Null Hypothesis: the hypothesis that sample observations result purely from chance.
Alternative Hypothesis: the hypothesis that sample observations are influenced by some non-random cause.
P-value: the probability of obtaining the observed results of a test, assuming that the null hypothesis is correct; a smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.
Alpha: the significance level; the probability of rejecting the null hypothesis when it is true — also known as Type 1 error.
Beta: type 2 error; failing to reject the null hypothesis that is false.

Steps to Hypothesis testing:
1. State the null and alternative hypothesis
2. Determine the test size; is it a one or two-tailed test?
3. Compute the test statistic and the probability value
4. Analyze the results and either reject or do not reject the null hypothesis (if the p-value is greater than the alpha, do not reject the null!)

And that’s it! If I find that I missed a lot of important topics later in my journey, feel free to comment and let me know 🙂

For more articles like this one, check out

Thanks for Reading!

Here at Datatron, we offer a platform to govern and manage all of your Machine Learning, Artificial Intelligence, and Data Science Models in Production. Additionally, we help you automate, optimize, and accelerate your ML models to ensure they are running smoothly and efficiently in production — To learn more about our services be sure to Book a Demo.


Datatron 3.0 Product Release – Enterprise Feature Enhancements

Streamlined features that improve operational workflows, enforce enterprise-grade security, and simplify troubleshooting.

Get Whitepaper


Datatron 3.0 Product Release – Simplified Kubernetes Management

Eliminate the complexities of Kubernetes management and deploy new virtual private cloud environments in just a few clicks.

Get Whitepaper


Datatron 3.0 Product Release – JupyterHub Integration

Datatron continues to lead the way with simplifying data scientist workflows and delivering value from AI/ML with the new JupyterHub integration as part of the “Datatron 3.0” product release.

Get Whitepaper


Success Story: Global Bank Monitors 1,000’s of Models On Datatron

A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.

Get Whitepaper


Success Story: Domino’s 10x Model Deployment Velocity

Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.

Get Whitepaper


5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

Get Whitepaper

Our Latest *** RELEASE ***

Datatron 3.0

00 Days
00 Hours
00 Mins
00 Secs

Datatron continues to innovate and improve data scientist workflows with our latest release, which includes a JupyterHub Integration, Kubernetes Management and more Enterprise-grade features.

See the Release Notes!