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Data Scientist vs Data Analyst: Understanding the Difference

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Data Scientist vs Data Analyst: Understanding the Difference

While both proficient data scientists and data analysts work with different types of data, the primary difference lies in their purpose of working with it. A data analyst examines large sets of data to know trends, make charts, and make visual presentations to enable business clients to make strategic data-driven decisions. On the contrary, a data scientist is responsible for designing and creating new methods of data modeling based on prototypes, predictions, algorithms and analysis.

Still, confused about data scientist vs data analyst? Here is a detailed overview.

Working in the Field

A data analyst’s responsibility varies from one industry and company to another, but basically, they use available data to draw sensible insights and resolve issues. Their job is to analyze a properly defined set of data using a multitude of tools to find solutions to tangible business issues, such as reasons for dropped sales over the last year, why a particular advertisement was more effective in certain regions while failing in others, etc.

A data analyst can work as a business analyst, sales analyst, advertising analyst, foreign strategy analyst, pricing analyst, marketing analyst, and others too. The best data analyst is the one who has technical know-how as well as communication abilities with clients and colleagues.

On the other hand, a data scientist is responsible for making predictions with the help of algorithms, statistical models, and making queries. The major point of difference between the two job roles lies in heavy coding. A data scientist uses a myriad of tools to arrange data sets and create their own frameworks and automation systems.

Data Scientist vs. Data Analyst – Background

A data analyst usually has a background in statistics and mathematics. Some of them also supplement their background by learning the tools required to make number-related decisions. Data analysts looking forward to advancing their career may further pursue higher qualifications in the field, such as a Master’s degree in Analytics.

As far as background of data scientists are concerned, they have statistical and mathematical knowledge, along with substantive qualifications and skills of hacking. Some data scientists may also hold a Master’s degree in Data Science.

Skillset and Tools

Data modeling, data warehouse, data mining, SQL, SAS, statistical analysis, data analysis, management and reporting of data and others are the top skills of a data analyst. While a data scientist is more specialized in software development, object-oriented programming, python, Hadoop, machine learning, Java, data mining, data warehouse, etc.

Roles and Responsibilities

The main task of a data analyst is to design and maintain databases and data systems with the help of statistical tools. These help interpret data sets, prepare reports, effectively communicate patterns and trends, and make predictions as per the appropriate findings.

A data scientist is typically responsible for designing processes for data modeling, making algorithms, predicting models, and extracting the information required to solve complex issues in an organization.

To conclude, both data scientists and data analysts have deceptively similar job titles; however, they may vary in their educational background and roles and responsibilities. Both have high demand in the industry, and the choice of data scientist vs. data analyst depends on the task to be performed.

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