Introduction
Data Science and Analytics are the two most important aspects of any big data initiative for organisations.
Data Science is a new field of study, which is a combination of statistics, machine learning, data analysis and programming. The primary goal of Data Scientists is to find insights in large datasets that can be useful to an enterprise or organisation.
Analytics on the other hand refers to the process of examining and understanding data. It involves exploring huge volumes of raw information available on a particular topic or issue with the help of advanced analytics tools like SAS, SPSS etc.
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What is Data Science?
Data Science is a relatively new discipline that deals with extracting actionable insights from data. It’s about understanding the structure of data, finding patterns, and making predictions based on those patterns. A Data Scientist applies statistical methods, machine learning and other tools to construct models for effective decision-making.
Data Scientists are expected to play a central role in transforming organizations into Data-Driven Companies by leveraging their analytical skills to generate value from huge volumes of data generated by today’s business processes. Basically, they make sense out of large amounts of information in order to help organizations make better decisions faster than ever before.
The role generally involves analyzing large sets of structured or unstructured data (i.e., spreadsheets, log files) using statistical programming languages like R/Python/SAS etc., building predictive analytics models using existing frameworks such as Hadoop Analytics Cluster (HAC); creating reports on these analyses; communicating findings back into business units such as marketing and operations; helping build software solutions based on those findings (e.g., predictive algorithms).
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What is Analytics?
Analytics is a process of extracting insights from data to make better decisions. It gives you the ability to find out what works, what doesn’t and why it happened. Analytics can take your business to the next level by providing deep insight into your customers, competitors and industry trends.
It helps answer questions such as “What do my customers want?” or “How am I doing in terms of sales?”
How is Data Science and Analytics Related?
Data Science and Analytics are two most important pillars of any big data initiative. While Data Science is the process of extracting knowledge from data, Analytics is the process of analysing data to extract knowledge. This combined effort gives a better understanding of what’s happening with your business and makes it possible to predict future trends and make decisions based on them.
While you can have analytics without science, having just science will not give you accurate results as there may be no way to interpret them properly.
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How are Both Different From Each Other?
- Data Science is about extracting knowledge from data.
- Analytics is about using data to make decisions.
Both are different in the approach taken for gathering and analysing data, but both use the same set of tools to get the job done.
What are the Roles in Both Domains?
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Data Scientist
A data scientist is someone who uses their skills to mine the data, understand it and extract insights from it. They usually work with a team of engineers and analysts to create models that can be used for various purposes.
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Data Analyst
A data analyst works on getting information from various sources such as offline or online databases, spreadsheets, surveys and so on. They also use analytical tools like Excel/PowerPoint/Tableau etc., but mostly rely on statistical techniques to present their findings in a readable format.
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Data Engineer
A data engineer builds applications that collect and process data using technologies like Hadoop, Spark etc. while ensuring its quality so that it can be used by other teams such as analysts or scientists without any issues later down the line.*Data Architect
The architect’s role includes designing databases according to specific requirements so that they function efficiently within an organization’s infrastructure.”
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Which Domain has More Scope Between Data Science & Analytics?
Data Science is more of a research based field while Analytics is more of a practical field. In Data Science, you will have to do research on any topic and come up with new conclusions, whereas in Analytics, you are required to use the existing data sets and put them into usable formats.
Takeaway: if one wants to make a career in Data Science or Analytics, one has to have certain expertise in coding, mathematics, statistics and machine learning”
To sum up, if you want to make a career in Data Science or Analytics, one needs to have certain expertise in coding, mathematics, statistics and machine learning.
As mentioned earlier, Data science is a broad field of study and includes many different disciplines. Analytics is one such subset of data science and involves applying statistical methods on the data collected from various sources to generate insights for decision making. So if your aim is not just about understanding how things work but also about applying those insights for decision making then analytics may be what you are looking for!
