Here’s what you need to know about the most common data career paths!
There’s never been a better time to break into the booming data industry! In 2021, InformationWeek reported that the market for data science and analytics jobs is heating up: "This is the hottest market we've ever seen for data and analytics pros," Jon Linn, Business Development Manager at Burtch Works, told IW.
There’s a lot of overlap in data jobs and skill sets, so it’s helpful to know their key differences when planning your career journey. Two of the most common career paths for data professionals are Data Analyst and Data Scientist. Here’s a breakdown of the roles, requirements, and salaries of a Data Analyst vs. Data Scientist:
Data Analyst vs. Data Scientist: Responsibilities
While both of these roles work with data, the key difference is what they do with it: using data, Data Analysts answer the question “what happened?” — whereas Data Scientists answer the question “what will happen?”
Data Analyst Responsibilities
- Gather, organize, and analyze data
- Identify key trends to help a company make data-driven decisions
- Create reports, visualizations, and dashboards using spreadsheets and Tableau
- Perform in-depth analyses using SQL and Python
- Develop rudimentary models to explain or predict trends
Data Scientist Responsibilities
- Gather, organize, and analyze data
- Develop data models and algorithms to help predict future trends
- Optimize business performance using data science tools/methods such as SQL, Tableau, Python, feature engineering, and machine learning
- Use mathematical techniques to optimize machine learning model performance
- Productionize machine learning models
Data Analyst vs. Data Scientist: Qualifications
Data Analytics is a more accessible field: anyone can become a Data Analyst with training, even if they have no related degree or prior experience. Juno College’s Data Analytics Bootcamp is a great example of an intensive program designed to help absolute beginners get the skills they need to land a job as a Data Analyst or a Junior Data Scientist.
Data Science roles tend to be more senior, reserved for those with experience in the industry and/or with Master’s or Doctoral Degrees in math, computer science, or statistics.
While both Data Analysts and Data Scientists have coding knowledge, Data Scientists have a more advanced understanding of coding and database languages. Both roles also require good communication skills, an understanding of business goals, and of course, an analytical mindset!
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Data Analyst vs. Data Scientist: Average Salary
According to GlassDoor Canada, the average annual salary for a Data Analyst is $62,306, with salaries increasing up to $83,341 for a Senior Data Analyst.
As Data Science roles generally require a higher level of expertise and experience, the average annual salary for a Data Scientist is $87,248, with salaries increasing up to $110,946 for a Senior Data Scientist.
Data Analyst vs. Data Scientist: Job Market & Opportunities
Companies of all sizes are realizing the impact of data-driven decision-making, resulting in a booming demand for data roles. The Government of Canada Job Bank reported a shortage of Data Analysts available for the number of job-related openings from 2019-2028 — meaning that the demand for data professionals is high!
In general, there are more Data Analyst roles than Data Scientist roles, as Data Science is more specialized. When a company starts to become interested in working with data, their first hire is usually a Data Analyst. Only once their data has been optimized would it be time to hire a Data Scientist, and even then, not every company will need one.
There are all kinds of opportunities available for both roles, across all industries: healthcare, tech, marketing, education, finance, and more! A Data Analyst can also transition into becoming a Data Scientist, and both roles can further specialize in specific areas within their industries.
Data Analyst vs. Data Scientist: Which Is Better?
Data Analyst and Data Scientist roles have lots of overlap in their daily duties and goals, and present exciting growth opportunities. They’re also both in high demand as our world becomes increasingly more data-driven!
When deciding which career path is best for you, it’s important to reflect on your prior experience, interests, ideal timelines, and personal and professional goals. Everyone’s path is different, and that’s something to celebrate!
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