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

At their core, data scientists are problem solvers. Because of the major insights they can offer businesses, they're also in demand. According to the US Bureau of Labor Statistics (BLS), careers in data are poised to grow rapidly in the coming years, making this a lucrative career choice with significant growth potential [1].

In this article, we'll discuss what data scientists do and how to become one. Afterward, if you're interested in pursuing a career as a data scientist, consider enrolling in the IBM Data Science Professional Certificate. You'll learn the tools, languages, and libraries used by professional data scientists, including Python and SQL.

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Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Data Science, Generative AI, Predictive Modelling, Data Analysis, Model Selection, Data Visualization, Python Programming, Pandas, Numpy, Dashboards and Charts, Matplotlib, dash, Relational Database Management System (RDBMS), Cloud Databases, Jupyter notebooks, SQL, regression, Clustering, SciPy and scikit-learn, classification, Machine Learning, CRISP-DM, Data Mining, Methodology, K-Means Clustering, Github, Jupyter Notebook, Data Science Methodology, Rstudio, Deep Learning, Big Data, Quering Databases, Data Generation, Interviewing Skills, Resume Building, Career Development, Job Preparation

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

Thanks to this work, a data science career can be intellectually challenging and analytically satisfying while also putting you at the forefront of new technologies.

Data scientist vs. data analyst: 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 greater responsibility and are generally considered more senior than data analysts. 

Data scientists are often expected to formulate their own questions about the data, while data analysts might support teams that already have set goals. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data.

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

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Google Data Analytics

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Data scientist salary and job growth

A data scientist earns a median salary of $108,020, according to the US Bureau of Labor Statistics [2]. Demand is high for data professionals—data scientists occupations are expected to grow by 36 percent in the next 10 years (much faster than average), according to the US Bureau of Labor Statistics (BLS) [2].

How to become a data scientist

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

1. Earn a 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. About 51 percent of data scientists have a bachelor's, while 34 percent have a master's [2].

If you're considering a degree, majors like computer science, statistics, or data science (if available as a specific program) are great foundations. If you already have a bachelor's degree, a master's degree can be an excellent way to pivot into the field. At the master's level, you'll gain a firm understanding of statistics, machine learning, algorithms, modeling, and forecasting.

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2. Or learn about data science in other ways.

If a degree is not a commitment you're ready to make just yet, consider alternative types of education to bolster your knowledge and build your skill set. Individual courses, specializations, and professional certificates are all great ways to grow familiar with the subject matter while working with tools that will strengthen your technical abilities.

3. 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 boot camp. 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: Even the most brilliant data scientists won’t be able to affect 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.

If you're proficient in Python, broaden your skill set with DeepLearning.AI's Data Engineering Professional Certificate. You'll build skills in the five stages of the data engineering lifecycle, including generating, ingesting, storing, transforming, and serving data.

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DeepLearning.AI Data Engineering

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4. Explore entry-level data analytics jobs.

Though there are many paths to becoming a data scientist, starting 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.

5. 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 data science 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

As with the other courses I took on Coursera, this program strengthened my portfolio and helped me in my career.

— Mo R., on taking the IBM Data Science Professional Certificate

Learn data science on Coursera

Data scientists use their knowledge of data types, techniques, and tools to ask– and answer– impactful questions. Build the skills you need to build your data career with one of these courses on Coursera:

To prepare for a career as a data scientist, take the IBM Data Science Professional Certificate. Build foundational knowledge needed for an entry-level role, such as importing, cleaning, and analyzing data, and learn how to use tools, libraries, and languages like Python and SQL.

To learn how to use MATLAB for data science, enroll in MathWorks' Practical Data Science with MATLAB Specialization. Learn how to analyze large data sets, create and evaluate machine learning models, and use interactive tools to investigate data with MATLAB.

If you want to master fundamental AI concepts, try Stanford and DeepLearning.AI's Machine Learning Specialization. Explore how to build machine learning models, apply best practices to machine learning development, and build and train your own neural network.

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Data Science, Generative AI, Predictive Modelling, Data Analysis, Model Selection, Data Visualization, Python Programming, Pandas, Numpy, Dashboards and Charts, Matplotlib, dash, Relational Database Management System (RDBMS), Cloud Databases, Jupyter notebooks, SQL, regression, Clustering, SciPy and scikit-learn, classification, Machine Learning, CRISP-DM, Data Mining, Methodology, K-Means Clustering, Github, Jupyter Notebook, Data Science Methodology, Rstudio, Deep Learning, Big Data, Quering Databases, Data Generation, Interviewing Skills, Resume Building, Career Development, Job Preparation

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