Would You Rather be a Data Analyst or Data Scientist?

Would You Rather be a Data Analyst or Data Scientist?

How does it feel to be in one of these roles? Find out here. Everyday work for a data analyst involves more meetings, more face-to-face interactions, soft skills, and quicker turnaround on projects.

Table of Contents

  1. Introduction
  2. Data Analyst
  3. Data Scientist
  4. Summary
  5. References


After working as both a professional data analyst and data scientist, I thought it would be insightful to highlight the experience of each position along with some key differences in how they feel day-to-day. Ultimately, I hope my article can help you decide which role fits best for you. If you are already in one of these positions, perhaps you would like to switch to the other one. Some people start as data analysts then move on to becoming a data scientist, whereas, as a less popular route but still somewhat prominent, is going from a non-senior level data scientist position to a senior data analyst. For each position, there are several concepts and overall experiences that are important to know as you make your next big career move.

Below, I will highlight how it feels to be a data analyst as well as a data scientist. I will raise common questions about each role and answer them accordingly from what I have experienced — in addition to some close peers in each field.

Data Analyst

If you want to describe data from the past or currently, while presenting key findings, shifts and trends, and finally, visualizing data to stakeholders, then a data analyst position is best suited for you. While there is some overlap between the two positions, which I have highlighted in another article (linked at the end of this article) that covers the differences and similarities between the skills of these two roles, I wanted to now take some time to go over how it feels to be a data analyst versus a data scientist. It is essential to know what to expect for your day-to-day in this field. You can expect to work with different people, communicate differently (more), and move faster than a typical data scientist.

Therefore, the feeling you have from each respective role can be vastly different from one another.

Below, I will raise some common questions, along with their corresponding responses — shedding some light on the data analyst experience.

  • Who do you work with?

— you will work with primarily stakeholders in the company who are requesting data to be pulled, visualizations of insights, and reports. Communication can be expected to be both verbal and digital through the use of tools like email, Slack, and Jira. You will focus on the people and the analytical side of the business, not on the engineering and product part of your company (from my experience).

  • Who do you share your findings with?

— you will share your findings with most likely the same people from above. However, if you have a manager, sometimes, you will report to them and they will relay and share your findings to the appropriate stakeholders. You may also have a process where you gather requirements, develop a report, and communicate that out to stakeholders. You may use tools like Tableau, Google Data Studio, Power BI, and Salesforce for reporting. These tools can often be connected to easy to access data sources like a CSV file, while some require more technical work through advanced querying of a database with SQL.

  • How fast do you need to work on a project?

— you will work on projects considerably faster than a data scientist. You can have several data pulls (queries) or reports per day, and larger visualizations and insights on a weekly basis. Since you are not building a model and predicting (usually), you will turn around results faster as they are more descriptive and ad-hoc.

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