What is the difference between SAS and Data Science ?
To know the difference between SAS and data science you must know about what are they?
First I shall try to explain you the details explanation and definition of SAS and data science
What is SAS?
SAS (previously “Statistical Analysis System”) is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. Know more about SAS Software
What is Data science?
Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
Know more about data science from here.
What is the difference between SAS and Data Science ?
SAS is a software which is used for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics
On the other hand,
Data science is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms.
Thanks for your questions.
There is a far difference between these two. Data analysts are supposed to perform analysis or generating insights from data. Analysis can be descriptive or predictive. SAS/Excel/SQL/R are most widely used softwares in analytics industry.
SAS Programmer – More focus is there on programming in SAS (very clear from the title). Generally happens when the statistical part is well defined, or needs a rework.
Data Analyst – More inclined towards generating insights from the data, querying and preparing reports, and can also involve a bit of machine learning. This also requires programming in R/Python/SAS , however its not just programming.
This example may clarify you: For example, Suppose you are a bank officer. You are asked to work on a customer attrition problem. By ‘customer attrition’, i mean customers leaving the bank. You have three years historical data for customer attrition. SAS Programmer would write a SAS program to compute average customer attrition for different bank products and present it to the higher management. He would automate the process so that the same task would be performed very quickly in future.
Whereas data analyst would write a SAS program to calculate the attrition for different products and would also see whether there is any trend in the attrition numbers.
As as an example, whether or not the attrition variety increased or decreased. that square measure the merchandise whereby attrition variety increased? And that square measure the merchandise the quantity declined. He would even be interested to visualize the profile of attritors (who left the bank). whether or not the attritors square measure high worth customers or middle /low worth customers. good analyst would take it to consecutive level and would develop a prophetical model for client attrition. He would establish the patterns of client attrition and would develop a system to cut back client attrition. however regarding automation? good analysts additionally write macros to change the repetitive task.
Data Science as defined involves more than just analytics, analytics being just an instance in the workflow of every data scientist. Data Science has a broader perspective. It involves Data wrangling, Data acquisition, Data storage, Data Analysis, Machine Learning/ Deep Learning. SAS only suffices for the analytics part
While Python is a fully-fledged language with appropriate libraries for data science and machine learning, has powerful visualizations and most importantly is open source or free while SAS is a commercialized brand that makes it costly.
SAS is only centered around the analytics power derived by global enterprises for monitoring their huge datasets and providing good feedback and building appropriate predictive models for the growth of business however lacks behind the usability of Python in the domain of Data Science.
SAS being a paid software, you will always be dependent upon the kind of software and data set you are exposed to work on. Hence python should be your choice of language if you want to make a career in the field of data science.
SAS stands for Statistical Analysis Software. It is an integrated machine of software products developed through SAS Inc. for advanced analysis, multivariate analysis, commercial enterprise intelligence, data management, and predictive analytics.
Data Science largely refers to the ways in which the data is cleaned, organized, presented, modeled, extracted, processed, and several other things. This disciple also draws from many other subjects such as Mathematics, Statistics, computer science, programming, etc. Therefore Data Science encompasses various skill sets and specializations within the category.
Big Data can be defined as a part of the Data Science umbrella. Big Data is a special application in Data Science that deals with the logistical challenge of large tracts of unorganized data. Big Data cannot be tabulated, graphed, and charted. It needs to be cleaned in order for it to be represented pictorially.
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- SAS is commercial software, so it needs a financial investment, whereas R is open source software, So, anyone can use it.
- SAS is the easiest tool to learn. So, people with limited knowledge of SQL can learn it easily; on the other hand, R programmers need to write tedious and lengthy codes.
- SAS is relatively less frequently updated, whereas R is an open-source tool, continuously updated.
- SAS has good graphical support, whereas the Graphical support of the R tool is poor.
- SAS provides dedicated customer support, whereas R has the biggest online communities but no customer service support.
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