Would You Rather be a Data Analyst or Data Scientist? How does it feel to be in one of these roles? Find out here.

Matt Przybyla
Jun 29, 2020

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.

Data Scientist

Data scientists vary vastly from data analysts. While some tools and languages can overlap between the two roles, you can expect to work with different people and spend more time researching bigger projects like machine learning model creation and deployment. Data analysts can tend to work alone on their projects; for example, working with a Tableau dashboard to present findings can take one person, but a data scientist can incorporate several other engineers and product managers to ensure the model is solving the business problem and that the code is correct, powerful, and efficient.

  • Who do you work with?
— different from a data analyst, you will work with stakeholders for some of the project but will turn to data engineers, software engineers, and product managers for other aspects of your model and its results.

  • Who do you share your findings with?
— you can expect to share your findings with stakeholders, but also with some engineers who will need to know what the end product is so that they can, for example, build a UI (user-interface) around your predictions.

  • How fast do you need to work on a project?
— perhaps the biggest difference between how these roles feel and operate is the amount of time you have allotted to each project. Whereas data analytics is more fast-paced, data scientists can take weeks or months to finish a project. Because there are processes like data collection, exploratory data analysis, base model creation, iterations, model tuning, and output of results, data science models and projects can take longer.


Photo by Markus Winkler on Unsplash [2].

As a data analyst and data scientist, you can expect to share common tools like Tableau, SQL, and even Python, but the experience from each role can prove to be vastly different. Everyday work for a data analyst involves more meetings, more face-to-face interactions, soft skills, and quicker turnaround on projects. Data scientist work can involve longer processes, interactions with engineers and product managers, and overall, a predictive model that looks at classifying new observations or events in time, whereas data analytics focuses on the past and current state.

I have written a more in-depth article on the specifics for these roles. You can find that article here [2]:
Data Science vs Data Analysis. Here’s the Difference. What are the main differences and similarities between data scientists and data analysts? Read below for an outlined…towardsdatascience.com

I hope you found this article interesting and useful. Thank you for reading!

“Would You Rather be a Data Analyst or Data Scientist? How does it feel to be in one of these roles? Find out here.”
– Matt Przybyla twitter social icon Tweet

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