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

A data scientist uses data to understand and explain the phenomena around them, and help organizations make better decisions. Learn more about this role and its salary, skills, and career path.

As data grows increasingly important to the way organizations make decisions, data scientists have become more common and in demand. Working as a data scientist can be intellectually challenging and analytically satisfying and put you at the forefront of digital transformation. Here’s a closer look at what it is to be a data scientist, their responsibilities, and the career path to becoming one.

What is a data scientist?

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

• Use deep learning and pattern recognition to automate problem-solving techniques 

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 there are distinctions. 

The data analyst might run the report that the data scientist designs having determined how the data is stored and can be manipulated and analyzed. Data analysts do their work with processed data. Data scientists will work with the raw data from many, disconnected sources to get it into a single database for the 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.

Data scientist salary and job growth

A data scientist earns an average salary of $91,442 in Canada as of February 2023, according to Glassdoor [1]. 

Demand is high for data scientists. The Job Bank predicts a “good” job outlook for data scientists in most provinces over the next three years. Prospects are “very good” in Quebec, “moderate” in PEI, and “undetermined” in Nunavut and the Northwest Territories for the same period [2].

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. Bringing a range of analytical and mathematical know-how (e.g., multivariable calculus and linear algebra) as well as statistics and computer science can help you 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

  • RapidMiner

  • R

  • SQL

  • Anaconda

• 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:

  • Excel

  • Tableau

  • PowerBI

• 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. Joining a data scientist community can be a great way to learn and share ideas about your field.

3. Get an entry-level data analytics job.

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.

4. Pursue a passion project.

Keeping in mind that there are a variety of fields related to data science, you might also start a special project of your own. This also demonstrates your passion for the field and interest in continuing your education to recruiters and potential employers. Search online to find ideas for data science projects for beginners. 

Doing your own data science can also help you narrow down your areas of interest while doing something personally useful. For example, if you’re searching for a new home, you might build a script that compiles the best posted real estate deals in real time and then pushes them to your email. 

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 interview questions. 

Data scientist positions can be highly technical, but it’s best to anticipate both technical and behavioral questions. Prepare examples from your past work or academic experiences to help you appear confident and knowledgeable.

Questions you might encounter include:

• 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?

Getting started

Becoming a data scientist might require some training, but you could be building up to an in-demand and challenging career. 

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