Data science is a rapidly growing STEM field. It plays an important role in key areas of technology such as artificial intelligence, machine learning, and predictive modeling. The field is also experiencing a fast-paced evolution and it can be challenging to keep up with all the jargon used. One of the most often misunderstood concepts is the difference between a data analyst and a data scientist.
Data Analyst Versus Data Scientist
Like many roles in data science, there is some overlap in terms of skills, education, and responsibilities between these two jobs. In addition, as the field continues to evolve, the definitions vary, especially between organizations. Nonetheless, there are some generally accepted differences between the two disciplines.
What is Data Analytics?
Data analytics is the processing, organizing, and analysis of datasets to identify trends, solve problems, and help organizations make decisions. Data analysts tend to be relatively hands-on with the data, using tools such as SQL to run queries and discover patterns. Ultimately, the goal of an analyst is to turn collected data into usable insights.
What is Data Science?
Data science is an interdisciplinary field that focuses on designing data modeling processes, creating predictive models, and developing algorithms. In short, data scientists tend to be focused on developing the science and structure behind the data analysis rather than the analysis itself. They are less hands-on with the data set and use critical thinking, business intuition, problem-solving, and mathematics/statistics.
How Roles fit in the Data Team
It’s important to note the roles and responsibilities of people working with data can vary depending on the industry, location, and even the organization. In general, the analyst’s role is to figure out what’s happening in addition to how or why it’s happening. They will gather information about the past and use it to infer various insights.
Conversely, the scientist’s role is more focused on the future and what could happen. Data scientists use data modeling and frameworks to develop ways to extrapolate future data points. Furthermore, they often spend a lot more time working with the theory and science of data rather than the gathered data itself.
Career Path: Analyst Versus Scientist
Typically, data analysts have a background in a quantitative field such as mathematics or statistics. However, they may also come from other fields such as business and learn the tools and techniques necessary for analysis. According to O*Net, more than three in four analyst jobs require a bachelor’s degree and about 14% require a master’s degree.
Data scientists traditionally have extensive statistical or mathematical knowledge and training. Many of them hold advanced degrees including master’s degrees and doctorates. However, there are also some data scientists with less traditional education but who have proven their abilities through experience.
These career outlooks are promising. According to the Bureau of Labor Statistics, the outlook for Computer and Information Research Scientists is excellent. For example, between 2020 and 2030, there will be a 22% growth in jobs with an average of 3,200 openings per year within that time frame.
Necessary Skills: Analyst Versus Scientist
The skills needed to be a data analyst and a data scientist are similar in a lot of ways. Both roles need to have some understanding of how the other works. Nonetheless, there are some clear differences in the skills required. The key skills for an analyst include:
- Strong understanding of statistics
- Data cleaning and wrangling
- Knowledge of languages such as Python, SQL, and R
- Exploratory data analytics
- Data visualization and reporting
A data scientist needs to understand most of these skills but tends to have a greater focus on math and modeling. The key skills for a scientist include:
- Strong understanding of statistics, linear algebra, calculus, and probability
- Data wrangling and modeling
- Versed in Python, SQL, R, SAS, MATLAB, and Spark
- Machine learning and artificial intelligence
- Data visualization, particularly using Power BI and Tableau
Get Started on Your Data Science Career
Are you interested in working as a data analyst or data scientist? INE can help you learn the skills you need through in-depth data science courses and hands-on training.
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