For the past year or two there has been a lot of buzz around words like Data Science, Analytics, Business Analytics, Big Data. Many new technologies and words have been popping out every day, adding confusion to the average career seeker in the tech industry.
I have been asked by many students and professional on multiple occasions about Data Scientist and Data Analyst roles, their similarities and differences. There is also a misnomer that both are same. Below is a summary of my thoughts on the same.
Let me begin by stating that Data Scientist and Data Analyst are two different career options or job roles. Although there is large intersection of the job role and skill sets their primary output differs based on the position or organization.
We will get into the detail, before that i would suggest that you look more inward into your existing skills on various areas like Math, Stats, technology and business understanding (domain knowledge). This is required as it is pertinent that that we look forward to a career or learning that could complement the existing strengths rather than getting onto a road that is more popularly taken.
Once you have done this analysis let us look at Data Science and Data Analysis a little more in detail for you to analyze. Data Science or a Data Scientist is the buzz going around currently with the projections and predictions all tending positive globally.
A Data Scientists primary responsibility to work on Data, analyze the same using relevant math and stat to it, apply the business logic or domain knowledge and provide valuable analysis information in support of businesses to make viable decisions. There will be a requirement to create apt reports, graphs or visualize the analysis in a presentable manner.
The goal is to turn data into information, and information into insight. – Carly Fiorina, Former CEO of Hewlett Packard
From the above it is a given inference that Data Scientists necessarily need to acquire skill and knowledge to an advanced level on Mathematics, Statistics and Computational Methods.
Industry or business expertise on one or more domains eg., Banking, Pharma, Research etc., (indicative only this list can go into multiple industries).
Technology knowledge and expertise on tools like SPSS, Stata, Matlab Advanced programming on Java, Python, R, SAS and Mahout. Visualization tools like Qlikview or Tableau etc. Summarizing the same a Data Scientist is the key factor for businesses today in making sense of the large volumes of business Data or information and transforming this into viable/ actionable Business Intelligence.
What gets measured, gets managed – Peter Drucker
As a deviation to the Data Analysts primarily role is to understand the input of Data, storage of the same, ironing out the integration process, transforming the same into a pattern, enable seamless streaming and analyzing the same.
Data Analysts need skills on the below to be good at the job role or excel in careers.
- Understanding of databases and data warehouses (NoSQL would be great advantage)
- Understanding of Big Data and Hadoop ecosystem or similar concepts.
- Understanding of analytics as a science, the underlying techniques and logic
Programming skills on Python, R could be largely helpful specially so at the advanced level
Visualization tools will also add to the skill set. If you come from a background which is more technology and technical experience it would be a great choice to be onto Analytics. If you are someone with a mix of business/domain or functional experience and technology it would be good to get into Data Scientist roles.
In any case you will need to have expertise on core science and math. Alongside the core Data Science concepts please focus on getting multi skilled on Data processing, storage. You are needed to work on Data or Data sets that speak in volumes about real time industrial data providing various use cases for analysis.
Look forward to a place to learn which provides all of the above. Great advantage would be to get mentored by an active Data Scientist or analyst who also trains as part of his/her career. Never choose to train with makeshift facilities as this is not simple technology, theory and practice cycle.
For reference on a comprehensive analytics training program please visit Big Data Analytics Full Stack Professional Program.
I have attempted to put thoughts from my experience and did not attempt to share some links for further learning or guiding to specific websites as it there are many thoughts that can confuse.
Hope this simple approach will help in deciding further, would be happy to help in detail.
Authored by – Anantha Bhaskar
VP & Trainer, Skill Sigma