Key Differences between a Data Scientist and a Data Analyst

Data is into the bigger picture in contemporary time. Millions of bytes of data are being created every day and companies are regularly using it to enhance their business efficiency. There are statistics and reports which clearly depicts that opportunities for Data Scientist and Data Analytics are going to increase enormously in the future. Candidates who are interested in the field of Data Science and Data Analysis find it confusing sometimes to differentiate a Data Scientist job with a Data Analyst one. A sort of ambiguity prevails over the roles and responsibilities. Both of the roles requires working with data but there are some key differences that differentiate a Data Scientist with a Data Analyst. To clear the ambiguity over the matter and to make the readers clear of these two roles, I have come up with this article so that after reading this article you will be able to clear this confusion and if you are already interested in exploring these fields than you could take a better decision into landing to either of these two jobs.

We will start first with the role that a Data Scientist and a Data Analyst has in an organization.

Working as a Data Analyst and a Data Scientist will require you to analyze data, draw out useful information which is required by your business, analyze patterns and draw useful insights. While as a Data Analyst you will be working mostly with Structured Data, you should be expert working and analyzing with Unstructured data as well while working as a Data Scientist. Much of your efforts will be required in that. You can understand Structured Data as the data in a specific format like you can take the example of some your files that you have stored in a specific format at a directory which is obviously easy to analyze than in comparison to the number of files of different formats that you have saved anonymously in a messy way in a directory which is hard to analyze.

While working as a Data Scientist you will analyze data sets which are more complex and large in number as compared to Data Analyst job. This makes Data Scientist role a bit higher than an Analyst job.

A Data Analyst analyzes a particular set of Data, finds the hidden aspects, and solve the problems faced by the business. He analyzes and finds key areas where the business needs to be paid attention to. He analyzes and finds the patterns that are useful to the business. This follows the right decisions and strategies which takes a business closer to its goal. Working as a Data Scientist you will also do all of these things but it will also require you to predict about the future as well. Like predicting what the scenario of the future be like by analyzing the past and present trends. This obviously requires more skills.

Let's discuss the skills now which are required by a Data Scientist and a Data Analyst

Working a Data Scientist requires your skill of using programming languages like Python and R which are also most sought by companies today in their candidates. Having a good knowledge of Machine learning models, Data Visualization, Relational and non-relational database, and cloud will serve good for you. You need to have a strong command in Mathematics, Statistics, Data Visualization, Relational and non-relational database, Programming languages (Python, and R which are desired and preferred by most) to excel while working as a Data Analyst. Having strong communication skills, Business skills, curiosity (especially in a Data Scientist role) will make things easier for you while serving as a key role in the organization as a Data Analyst or a Data Scientist.

Source: HOB

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