Data Science Vs Data Analytics : What’s the difference?
Introduction: Data Science Vs Data Analytics
With everyone being so confused about Data Science & Data Analytics, we decided to write a Post that would explain how the field originated, the differences between the two and what you should be opting for.
Let’s go back to 80s when everything was done using calculator and trial and error methods. It all changed as Microsoft Excel was introduced in 1985 & Business Analytics was born. Excel has been a great choice to have basic database management but the things were to change soon as smartphones & Internet came into the picture.
Can You guess how much data is being created each day?
It’s 2.5 quintillion bytes each day as mentioned here.
Companies then began to realize that this huge data is a Gold Mine. Not only it can help them to understand things visually and make strategies, but they can build Models around this data to achieve automation. And every Big Company has realized that it may be the tool to use to beat competitions and stay a leader in their domain.
And if you are planning to be a part of this field, it becomes mandatory to understand the differences between the two and choose the career that suits you.
Also, it’s quite possible that you may have your company or you are planning to start a one. Knowing the difference between the Job roles would allow you to recruit the right person to get the things done.
Let me clear up terms like data science, Big Data, Data Analytics & Data Science in the simplest possible manner before giving a bigger picture.
Data Science is a science or study of data.
Big Data is a theoretical concept for defining problems that arise because of the large size of data where traditional data handling tools are not capable enough.
Data Analytics is a bunch of tool and techniques to perform analysis of data (big and small).
Data scientists do all these above tasks but they also deployable models to solve the business problems. Have you ever seen ” Recommended for You ” section in Amazon or other shopping sites which sometimes exactly seem to know what would you like to buy? It’s a Data Science Model at Finest.
In A Nutshell
If data science is the house that holds the tools and methods, data analytics is a specific room in that house. It is related and similar to data science, but more specific and concentrated.
Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.
Data analytics is generally more focused than data science because instead of just looking for connections between data, data analysts have a specific goal in minding that they are sorting through data to look for ways to support.
Data scientists, on the other hand, design and construct new processes for data modelling and production. In addition to performing and interpreting data studies and product experiments, these professionals are tasked with developing prototypes, algorithms, predictive models, and custom analysis.
Data scientists also use a variety of techniques to comb through data, including data mining and machine learning—key differentiators between the two roles.
A Data scientist needs to create these insights from chaos, which involves structuring the data in the right manner, mining it, making relevant assumptions, building correlation models, proving causality, and searching the data for signs of anything that can deliver business impact throughout.
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What are the Differences?
Let’s examine a couple of differences between the two domains so that you understand things better.
- Normally a Data scientist is expected to formulate the questions that will help a business and then proceed in solving them, while a data analyst is given questions by the business team and pursues a solution with that guidance.
- The data scientist role calls for strong data visualization skills and the ability to convert data into a business story. A data analyst is normally not expected to transform data and analysis into a business scenario and roadmap.
- “Data scientists are quite different from data analysts; they’re much more technical and mathematical.
- Data analysts love numbers, statistics, and programming. As the gatekeepers for their organization’s data, they work almost exclusively in databases to uncover data points from complex and often disparate sources. Data analysts should also have a comprehensive understanding of the industry they work in while the data scientist’s job requires them to have a blend of math, statistics, and computer science, they also need to be interested in, and knowledgeable of, the business world. Data scientists should have strong presentation and communication skills, according to Robert Half Technology (RHT). They’re tasked with gleaning risks, trends, and opportunities from the data, then relaying these findings in nontechnical ways to executives.
- With respect to salaries, Data Scientists do better than Data Analysts. Stagnation may happen in Data Analyst field after working for a couple of years and people generally move to other career domains like Data Science thereafter. Refer this article to get a better clarity.
Which One To Go For?
It would depend a lot on your educational & Professional background, personal interests and the career path that you wish to walk onto which would decide Data Science Vs Data Analytics.
Do you like statistics and numbers? Or do you wish to go for a challenging career that would allow you to build and deploy complex data models?
While Data Analyst is a very specific and defined domain, it’s almost the opposite of Data Science. Not only you need to have professional degrees like PhD but you must be a multi-skilled person too who is ready to take upon any possible challenges.
Data analysts love numbers, statistics, and programming. As the gatekeepers for their organization’s data, they work almost exclusively in databases to uncover data points from complex and often disparate sources. Data analysts should also have a comprehensive understanding of the industry they work in.
While the data scientist’s job requires them to have a blend of math, statistics, and computer science, they also need to be interested in, and knowledgeable of, the business world. Data scientists should have strong presentation and communication skills. They’re tasked with gleaning risks, trends, and opportunities from the data, then relaying these findings in nontechnical ways to executives.
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Hope you found the information Useful. Feel Free to comment on any questions or queries.