At Byte Academy, we teach a variety of courses within the field of Data Science. From our full-time immersive to our mini-courses in topics like Machine Learning and Data Visualization, we're consistently asked about what a career transition into Data Science looks like. To provide some guidance to our prospective students and blog readers, we've asked our own Data Scientist, Lesley Cordero, for her own perspective on frequently asked questions.
I keep looking at data science job postings, and there are so many job titles. What’s the difference between data engineers, data scientists, and data analysts?
This is actually a tricky question because most companies have their own guideline as to what constitutes each role. Below I've provided my own thoughts on what these roles tend to constitute, influenced by how Byte Academy defines them internally.
Data Analyst: Data Analysts have the highest range in role differences and can include analysts who focus on excel and numerical operations, as well as engineers who code analytics with Python or R. The focus of Data Analysts is usually on analytics rather than building data infrastructure or prediction models.
Data Engineer: Data Engineers are similar to typical software engineering roles, but with a specialty in data science processes. Depending on their skillset, they might be an engineer given tasks to build tools (maybe a web scraper, an API) or infrastructure (big data systems) or implement a model with existing modules.
Data Scientist: Data Scientists require the highest level of specialty. These positions require a high level of programming and statistical skills because they need to work critically and analytically on data-driven projects. They work on deciding what data to use, what tools to utilize for data preparation and cleaning, and what approaches to take for analytics and possible prediction.