Use the Data “Science Spectrum”

Use the Data Science Spectrum to find the right data science career path for you

Non-data scientists can be targeted. Data-related roles can be more effective than applying for data Science Spectrum roles. This is especially true for non-data background candidates. These roles are more common than those in data science positions, and have lower technical requirements and experience.

This raises the question: Which data-related jobs are the best for data science? It all depends on your interest in data science.

The Data Science Spectrum

Data science is often viewed as a mixture of computer science and statistics. Roles in the profession vary based on how important each discipline is to a specific position. This is the data science spectrum.

For example, a data science job closer to statistics might be focused on statistical and machine-learning techniques to extract insights from data. A computer science job might concentrate more on the development of machine learning models that can be deployed as part of an AI system.

Roles within the spectrum can be varied depending on technical skills and qualifications.

I also include data-related roles within the spectrum, which, while not data science positions, do have some overlap with them and can help you get started in your data science career.

Data science roles can exist at all points of the spectrum. Non-data science roles are frequently misnamed “data scientist” and vice versa. Therefore, it is possible to make a mistake by judging data-related jobs based on job titles alone.

Two career options in data science are possible uforia science by grouping the seven roles listed above based on their position on the data science spectrum and ranking them according to the technical requirements.

Career Path #1: Statistics and Data Science

An Insights Analyst position can be a great way to get a head start in data science for those who have a background in mathematics or statistics.

Insights analyst positions are often advertised under the title of “data analyst”, although they may be called “data scientist” in some cases. They typically focus on extracting, manipulating, and analyzing data to produce insights and reports. These skills are similar to those required by data scientists at the statistics end.

Insights analysts do not need to have the same level of statistical and machine learning knowledge as data scientists. This is the key difference between true data science roles and insights analyst positions. These skills can be acquired through work projects or additional study, but it doesn’t mean they are impossible to learn.