If you’re struggling to see yourself as a data science professional, it may be because the field of data science can be rather broad. While many positions in data science work across the entire data science pipeline, some are more active in a particular area. Codeup prepares you to take any number of career paths throughout this pipeline. To help explain the differences, we’ll discuss where some of our alumni have ended up. Keep reading to see what appeals to you!
Data: Plan and Acquire
First up is the data portion of the data science pipeline, where we plan and acquire the data.
Business Analysts like Jesse Ruiz and Jeff Hutchins, as well as People Analytics Specialists like Tim Sotirhos help their companies or departments identify what questions need to be answered, and what data they need to answer those questions. This often involves researching or reviewing existing processes and strategizing how they can be improved. They may work closely with project managers to create an action plan.
Information: Process and Prepare
Then, in the information portion, we process and prepare the data for future exploration.
Data Analysts like Matthew Zapata aggregate and manage, that is, combine, process, prepare, and store, the data for easy access. They research and improve on data quality and safeguard against data loss or errors. They also explore the data for potential relationships between variables, usefulness, and outliers. Data Analysts may also acquire the data and/or analyze the data for potential insights, but typically do not build and deploy machine learning models.
Knowledge: Explore and Analyze
In the knowledge portion, data is analyzed and turned into meaningful statistics
This stage is typically combined with Information or Intelligence rather than existing on its own. In a given company, their Data Analysts may both prepare and explore data, or the Machine Learning Engineers will both explore and model the data. Data Scientists, the broadest career in data science, typically will do all three, like Ednalyn De Dios. Unlike other data professionals (unless otherwise trained), Data Scientists can work anywhere along this continuum.
Once we have that knowledge, we use the intelligence stage to turn it into future predictions with machine learning models.
When the data is ready, Machine Learning Engineers like Michael Moran and Eric Escalante write the algorithms necessary to build, deploy, and continue to improve upon machine learning models. A machine learning model is a file that uses an algorithm to identify patterns and trends from data.
Decision Science Analysts, like Jeff Roeder, use math, stats, and machine learning models to convert intelligence-driven insights to actionable recommendations. They answer the questions asked by Business Analysts earlier in the timeline to draw conclusions for their company.
Finally, the action stage lets us integrate these insights into tests, then continue to monitor and evaluate new data.
Security Data Analysts like Sara Pena need to be able to identify trends from automated models that they or someone else created. They meet with stakeholders to discuss their analysis and help develop tests and prototypes of their recommendations. They then own and execute projects related to continuous improvement of their company’s processes.
Where Do You Fit In?
It’s totally possible for YOU to take on any of these job titles! Maybe it’s time to explore these careers even further, especially if one excited you. While Codeup does prepare and train Data Scientists, you may find your niche and prefer one stage to another. Whatever you choose, Codeup will help you land the job you want not only with our expert, employer-driven curriculum but also with professional help and placement services. Click here to learn how we help you land a job you love.