Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new advances in technology. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions. Here’s a closer look at what they are and do—and how to become one.
Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting.
A data scientist might do the following tasks on a day-to-day basis:
Find patterns and trends in datasets to uncover insights
Create algorithms and data models to forecast outcomes
Use machine learning techniques to improve the quality of data or product offerings
Communicate recommendations to other teams and senior staff
Stay on top of innovations in the data science field
The work of data analysts and data scientists can seem similar—both find trends or patterns in data to reveal new ways for organizations to make better decisions about operations. But data scientists tend to have more responsibility and are generally considered more senior than data analysts.
Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.
Many data scientists can begin their careers as data analysts or statisticians.
A data scientist earns an average salary of $122,499 in the United States as of April 2022, according to Glassdoor .
Demand is high for data professionals—data scientists and mathematical science occupations are expected to grow by 31 percent, and statisticians by 33 percent from 2020 to 2030, says the US Bureau of Labor Statistics (BLS) [2, 3]. That’s much faster than the average growth rate for all jobs, which is 8 percent.
The high demand has been linked to the rise of big data and its increasing importance to businesses and other organizations.
Becoming a data scientist generally requires some formal training. Here are some steps to consider.
Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field.
Consider getting a master’s in data science. At a master’s degree program, you can dive deeper into your understanding of statistics, machine learning, algorithms, modeling, and forecasting, and potentially conduct your own research on a topic you care about. Several data science master’s degrees are available online.
If you feel like you can polish some of your hard data skills, think about taking an online course or enrolling in a relevant bootcamp. Here are some of the skills you’ll want to have under your belt.
Programming languages: Data scientists can expect to spend time using programming languages to sort through, analyze, and otherwise manage large chunks of data. Popular programming languages for data science include:
Data visualization: Being able to create charts and graphs is a significant part of being a data scientist. Familiarity with the following tools should prepare you to do the work:
Machine learning: Incorporating machine learning and deep learning into your work as a data scientist means continuously improving the quality of the data you gather and potentially being able to predict the outcomes of future datasets. A course in machine learning can get you started with the basics.
Big data: Some employers may want to see that you have some familiarity grappling with big data. Some of the software frameworks used to process big data include Hadoop and Apache Spark.
Communication: The most brilliant data scientists won’t be able to affect any change if they aren’t able to communicate their findings well. The ability to share ideas and results verbally and in written language is an often-sought skill in data scientists.
Though there are many paths to becoming a data scientist, starting in a related entry-level job can be a good first step. Seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, it’s possible to work your way up to becoming a scientist as you expand your knowledge and skills.
With a few years of experience working with data analytics, you might feel ready to move into data science. Once you’ve scored an interview, prepare answers to likely interview questions.
Data scientist positions can be highly technical, so it’s possible you’ll encounter both technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Being prepared with examples from your past work or academic experiences can help you appear confident and knowledgeable to interviewers.
Here are a few questions you might encounter:
What are the pros and cons of a linear model?
What is a random forest?
How would you use SQL to find all duplicates in a data set?
Describe your experience with machine learning.
Give an example of a time you encountered a problem that you didn’t know how to solve. What did you do?
A data professional at IBM offers his advice for aspiring data scientists:
Becoming a data scientist might require some training, but an in-demand and challenging career can be waiting at the end.
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1. Glassdoor. "Data Scientist, https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm." Accessed April 17, 2022.
2. US Bureau of Labor Statistics. "Occupational Outlook Handbook: Data for Occupations Not Covered in Detail, https://www.bls.gov/ooh/about/data-for-occupations-not-covered-in-detail.htm." Accessed April 17, 2022.
3. US Bureau of Labor Statistics. "Occupational Outlook Handbook: Mathematicians and Statisticians, https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm." Accessed April 17, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.