Data Analyst vs. Data Scientist: Key Differences Explained

Data analysts and data scientists are two up-and-coming professional careers. Yet, a lot of people mix up what each role entails. Some of this is due to how new data science is as a field of study. The rest is down to different companies defining the data analyst and data scientist roles. So, what does a data analyst and a data scientist do?

What does a data analyst do?

A data analyst typically gathers data which helps identify trends to help business leaders make strategic decisions about their company and products they sell. ‘They focus on performing statistical analyses to solve problems and find solutions to questions’ says Tammy Wehr, an analytics blogger at Draft Beyond and Research papers UK. Data analysts use query tools, which look for data sets within SQL and non-SQL databases. A data analyst position is part of an interdisciplinary team within an organisation. Some day-to-day tasks that data analysts do are:

  • They deliver reports-sometimes daily, weekly, or monthly.
  • Examining patterns within the data sets they make queries on
  • Consolidating data-The most technical aspect of an analyst’s job is collecting the data itself. Consolidating data is the key to this position. They work to develop routines that can be automated and easily changed up for use in other areas.

A data analyst uses all these tasks to help develop new processes and systems for collecting data and compiling their findings to improve business.

What does a data scientist do?

A data scientist first and foremost, are problem solvers. Due to the need to make sense out of the vast quantities of data, there was a need for professionals who possessed analytics skills, statistical skills, machine learning skills but combined with business acumen and customer/user insights. Thus, data scientist jobs were created. Data scientists seek out and determine the questions that need answers and then try to come up with different approaches to try and solve the problem. Some of the data-related tasks that data scientists do are:

  • Pulling, merging, and analysing data
  • Using a wide variety of tools like Tableau, Python, Impala, Hadoop, Excel etc. to develop and test new algorithms.
  • Simplifying data problems and developing predictive models
  • Building data visualisations
  • Writing up results and creating proof of concepts

A data scientist is expected to use the above tasks to directly deliver business impact, and they require strong data visualisation skills.

Educational requirements for data analyst and data scientist positions.

Following a career path of either a data analyst or data scientist requires at minimum a bachelor’s degree in mathematics, computer science or statistics. Analysts can also pursue a degree in business with a concentration in analytics. However, data scientists tend towards more advanced degrees. ‘As of August 2020, a Burtch Works study of salaries of data scientists and predictive analytics professionals (PAP’s), at least 94% of data scientists who participated in the study had either a master’s or a doctorate and 86% of PAP’s’ explains Dale Shetler, a data scientist at Writinity and Last minute writing.

What are the similarities and differences between both job titles?

Each role looks at and analyses data which factors into making business decisions. Sometimes, depending on the industry and location where they work, there is some job overlap between both fields. A data scientist might fulfil some of the job requirements that an analyst might do. Both roles require basic mathematical knowledge, understanding algorithms, good communication skills and software engineering. However, each job role applies this knowledge differently.

Data analysts make sense out of data that already exists, whereas a data scientist works on new ways of capturing and analysing data that is to be used by the analysts themselves. Another difference is analysts tend to work on specific questions about the organisation’s business. Data scientists tend to work at the CEO level, developing new ways of asking and answering the big important questions within that industry. Data analysts are usually proficient with relational database and statistical software, as well as business intelligence programs. Data scientists use Python, Java, and machine learning to analyse data.

Although data analysts and data scientists have some of the same skills, there are key differences in the work they do.


WRITTEN BY Ashley Halsey

Ashley Halsey is a professional writer at Research paper writing services and Gum Essays. She has been involved in many projects throughout the country. When she is not running after her two children, she enjoys traveling, reading, and attending business training courses.

PHOTO BY Scott Graham