Basic Statistics For Data Science You Need To Know

With more and more people aspiring to become Data Scientists, it is important to determine which are the basic statistics for data science.

One of the things that you need to bear in mind is that even though you don’t need to be the top expert in the statistics field, you need to have a good knowledge about it. Specifically the basic statistics for data science.

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While math plays a crucial role in the field, statistics is even more important for any Data Scientist. So, you need to make sure that you have a good knowledge of the most important basic statistics for data science.

So, here are the basic statistics for data science that you need to know and understand:

#1: Statistical Distributions

Statistical distributions are very important for data scientists. Even though there are different statistical distributions that you need to know and understand, two of the most important ones are:

• Poisson Distribution

As one of the most important distributions in statistics, it is very important that you understand the Poisson distribution.

This distribution is usually used to determine the number of events that are likely to occur in a specific time interval. One practical example of how this distribution is used in the real life is when it is used to determine the loss in manufacturing.

Discover everything you need to know about the ANOVA F value. 

• Binomial Distribution

One if the things that you need to know about binomial distributions is that they can only be used for discrete values. Nevertheless, this is the type of distribution that keeps being used in statistics and that should help you with data science as well. In addition, most binomial distributions can be represented using a chart like the one that you see above. As you can easily see, the shape of this chart is very similar to the typical normal distribution curve.

The list of important distributions goes on and on. While these two are crucial, there are others that you should consider taking a deeper look  at as well:

• Discrete Uniform Distribution
• Geometric Distribution
• Negative Binomial Distribution
• Hypergeometric Distribution

Take a look at a practical insight of an F test. 

#2: Theorems And Algorithms

When we are talking about the basic statistics for data science, we can’t forget about important theorems and algorithms. From the simplest ones to the most complex, there are a lot of theorems and algorithms in the statistics world. However, since we are only looking at the basic statistics for data science, here are the most important ones:

• Bayes Theorem

This theorem is one of the most well-known statistical theorems. Simply put, this theorem simplifies very complex concepts by using just a couple of variables. The “conditional probability” is supported by the Bayes Theorem and it tells you that by solely using the given data points, you will be able to determine or predict the probability of any hypothesis.

• ROC Curve Analysis

In case you don’t know, ROC stands for Receiver Operating Characteristic and it is very used in Data Science.

One of the best applications of the ROC Curve Analysis is in predicting how well a test will perform by measuring its fall-out rate versus its overall sensitivity. So, as you can imagine, this analysis is crucial to determine the viability of any model.