Data Analyst or Data Scientist?

For every individual either thinking to choose a career or maybe already graduated there is always question which title is better and how.

If you’re interested in a career working with big data and crunching numbers, there are two paths you may want to consider — becoming a data analyst or a data scientist. What is the difference between data analysts and data scientists?

As the world relies increasingly on data in many aspects of business, research and the economy, both data scientists and analysts are in demand with salaries typically above the national average.

Data Analyst

A data analyst typically works as part of an interdisciplinary team to determine the organization’s goals and then manage the process of mining, cleaning and analyzing the data. The data analyst uses programming languages like Python/R or SAS, visualization tools like Power BI or Tableau.

Data Scientist

Compared to a data analyst, a data scientist may be more focused on developing new tools and methods to extract the information the organization requires to solve complex problems. It’s also beneficial to possess business intuition and critical-thinking skills to understand the implications of the data. Some in the field might describe a data scientist as someone who not only has mathematical and statistical knowledge but also the skills of a hacker to approach problems in innovative ways.

Differences and Similarities

If you love numbers and statistics as well as computer programming, either path could be a good fit for your career goals. An analyst typically works on answering specific questions about the organization’s business. A data scientist may work at a more macro level to develop new ways of asking and answering important questions.

Although each role is focused on analyzing data to gain actionable insights for their organization, they’re sometimes defined by the tools they use. It helps data analysts to be proficient with relational database software, business intelligence programs and statistical software. Data scientists tend to use Python and machine learning to manipulate and analyze data.

Roles and Responsibilities

Data Analysts:

  • Data analysis and forecasting using Excel.
  • Creating dashboards using business intelligence software.
  • Performing various descriptive, diagnostic, predictive or prescriptive analytics.

Data Scientists:

  • Data mining using APIs or building ETL pipelines.
  • Data cleaning using programming languages .
  • Statistical analysis using machine learning algorithms such as natural language processing, logistic regression, kNN, Random Forest or gradient boosting.
  • Creating programming and automation techniques, such as libraries, that simplify day-to-day processes using tools like Tensorflow to develop and train machine learning models.
  • Developing big data infrastructures using Spark and tools such as Pig and Hive.

Each role analyzes data and gains actionable insights to make business decisions. Data analysts use SQL, business intelligence software and SAS, a statistical software, while data scientists use Python and machine learning to make sense of data.

Job Market

Career Growth

Hope you find this article interesting and all the best in your job search!



Data Science student @Flatiron-School

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