What Is a Data Scientist? Salary, Skills, and How to Become One

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.

What does a data scientist do?

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

• Deploy data tools such as Python, R, SAS, or SQL in data analysis

• Stay on top of innovations in the data science field

Data analyst vs data scientist: What’s the difference?

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.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

Data scientist salary and job growth

A data scientist earns an average salary of $122,499 in the United States as of April 2022, according to Glassdoor [1]. 

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. 

How to become a data scientist

Becoming a data scientist generally requires some formal training. Here are some steps to consider.

1. Earn a data science degree.

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.

Already have a bachelor's degree?

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.

2. Sharpen relevant skills.

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:

  • Python

  • R

  • SQL

  • SAS

• 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:

  • Tableau

  • PowerBI

  • Excel

• 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 in 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 for data scientists.

3. Get an entry-level data analytics job.

Though there are many paths to becoming a data scientist, starting in a related entry-level job can be an excellent first step. Seek positions that work heavily with data, such as data analyst, business intelligence analyst, statistician, or data engineer. From there, you can work your way up to becoming a scientist as you expand your knowledge and skills.

Loading...

Advice for New Data Scientists

What is Data Science?

IBM Skills Network

4.7 (61,919 ratings)

 | 

740K Students Enrolled

Course 1 of 10 in the IBM Data Science Professional Certificate

Enroll for Free

4. Prepare for data science interviews.

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 you may encounter technical and behavioral questions. Anticipate both, and practice by speaking your answer aloud. Preparing 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 you didn’t know how to solve. What did you do?

Read more: SQL Interview Questions: A Guide for Data Analysts

A data professional at IBM offers his advice for aspiring data scientists:

Getting started

Becoming a data scientist might require some training, but an in-demand and challenging career can be waiting at the end. 

Are you just starting out in data science? Get a crash course in the basics with IBM’s Data Science Professional Certificate.

professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

4.6

(61,605 ratings)

168,931 already enrolled

BEGINNER level

Learn More

Average time: 5 month(s)

Learn at your own pace

Skills you'll build:

Data Science, Deep Learning, Machine Learning, Big Data, Data Mining, Github, Python Programming, Jupyter notebooks, Rstudio, Methodology, CRISP-DM, Data Analysis, Pandas, Numpy, Cloud Databases, Relational Database Management System (RDBMS), SQL, Predictive Modelling, Data Visualization (DataViz), Model Selection, Dashboards and Charts, dash, Matplotlib, SciPy and scikit-learn, regression, classification, Hierarchical Clustering, Jupyter Notebook, Data Science Methodology, K-Means Clustering