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Statistics vs. Data Science: What's the Difference?
Statistics vs. Data Science: What's the Difference?

August 24, 2022

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As a result of the extensive overlap between the domains of statistics and data science, many definitions of one discipline could also serve to define the other. But in actuality, there are some significant differences between the fields. Quantitative data collection and interpretation are the goals of the mathematically oriented discipline of statistics. On the other hand, data science is a multidisciplinary subject that uses scientific methods, procedures, and systems to extract knowledge from data in various formats. Numerous academic fields, including statistics, are used by data scientists. But there are differences across the fields in terms of their methods, the kinds of issues they look at, and many other things.

  • The process of creating and comparing models

 

A modeling technique that emphasizes the predicted accuracy of the model is used to solve numerous data science problems. Data scientists select the most accurate model when evaluating the predicted accuracy of several machine learning techniques.

 

Building and statistical testing models are done differently by statisticians. The data is reviewed to see if it is compatible with the assumptions of that model, which is typically the starting point in statistics (for example, linear regression). The model is made better by resolving any of the model's erroneous presumptions. Modeling is complete when all presumptions are verified and there are no violations.

Statisticians enhance a single, straightforward model to better fit the data, unlike data scientists, who compare numerous ways to get the optimal machine learning model.

 

  • Quantifying uncertainty

Statisticians, as opposed to data scientists, place a considerably greater emphasis on measuring uncertainty. Quantifying the precise link between each predictor and predicted outcome is necessary for the statistical model-building process. This relationship's uncertainty is quantified if there is any. Rarely does machine learning go through this phase?

  • Big data

Since they are too large to be stored on a single computer, data scientists frequently work with enormous databases. Though such data can occasionally be found in statistics, they are rarer than common. In the past, statistics has been considerably more concerned with what can be discovered from minuscule amounts of data.

 

The emphasis on little data helps to clarify why statisticians must quantify uncertainty. It is simple to mistake signals for noise when there is little available data. It is also impractical for data scientists to verify assumptions due to the sheer size of the data that is frequently investigated by data science.

  • The types of problems that are studied

Making predictions and maximizing database search are two common topics in data science issues. In contrast, the issues that statistics studies are typically more concerned with making generalizations about the world. Finding the most reliable methods of measurement, data collection, and uncertainty analysis are all part of this process.

 

The ultimate goal of statistical analysis is frequently to determine what causes what by quantifying uncertainty. In contrast, the purpose of data science analysis is typically related to a particular database or predictive model.

  • The backgrounds of the people who work in the fields

The majority of data scientists have engineering credentials. Math departments typically train statisticians in their fields.

  • Language

Some of the most significant variations in language usage between each field are shown in the following table. This post is widely referenced in this table.



 

Statistics

 
   

Estimating

 

Data point/observation

 

Classification

 

Covariate/predictor/independent variable

 

Response/output/dependent variable

 

Dummy variable/indicator coding

 



 

Data Science

 

Learning

Example/instance

Supervised learning

Feature

Label

 

One-hot coding

 

Conclusion: What is the Difference?

 

So what exactly distinguishes data science from statistics? Modeling techniques, data quantity, problem kinds studied, field personnel's backgrounds, and terminology all vary between the fields. The two disciplines are connected, though. In the end, knowledge extraction from data is the goal of both statistics and data science.



 


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