Difference between Artificial Intelligence and Data Science
I think there are a lot of different types of career opportunities for people interested in data science. Right from data engineers to ML engineers to data scientists to machine learning scientists to data analysts and even business analysts. And there is often confusion about which role to pick. People often say I come from a certain background and am not sure which path to take and I think having a mentor really helps in these situations to figure out what opportunities to pursue given a particular background and what is the course of action to be taken to reach there.
While a lot of data is available online, in fact, to pick up data science, mentorships even help to understand you know keep track of progress and get a more high-level idea on what are the kind of project you want to do if you want to land in a certain kind of job and also what are the kind of domains you want to pick based on your experience. So I think having a mentor and having a little bit of that extra advice goes a long way in making the right decision.
So there are multiple types of roles in the data science industry and there is a lot of confusion around them, so let me clear that out.
Data science is a multidisciplinary field that involves skills from statistics, probability, machine learning, and software engineering, and combinations of these result in different titles and roles which might confuse people.
Broadly there are roles like data engineer, and infrastructure engineer, so their roles are not typically data science roles but they are more of a support system around data scientists. They help data scientist do their job. So when it comes to data science there are three categories, even though there is still confusion around them but gradually some clarity is coming up.
So data science role, there are three roles:-
The first role is a data analyst role, the data analyst is a person who is responsible for reporting, summarizing data, and using basic statistics in SQL and Python to help business decision-makers take those decisions, and also help data scientists do that projects. So companies do hire for these roles, and almost every company hires for this kind of role.
The second role is more of a data scientist which goes a little deeper into the problem. They work on problems like fraud detection, and recommender system where they work with much more scientific rigor than a data analyst. The typical skills which are required to do this thing are statistics, programming languages, machine learning and etc.
But these people don’t have a strong background in software engineering so in a product-based setup they pair up with a machine learning engineer to put that model into production which brings me to the third part which is the machine learning engineer. A machine learning engineer is a person who has a strong foundation in software engineering but also understands machine learning so that they can put the, in a product-based setup these people can put machine learning models into production and integrate with the core software.
So coming to the companies they hire for these kinds of roles, data analysts, data scientists, and machine learning engineers. Data scientists and data analysts are hired in both, services and product companies. Machine learning engineer is very specific, typically these people are hired only in product-based setups.
Many Positions are available that are related to data scientist role.
• Data analyst
• Data science/analytics manager
• Database administrator
• Big data engineer
• Data mining engineer
• Machine learning engineer
• Data architect
• Hadoop engineer
• Data warehouse architect
• Manager of market intelligence
Data Scientist Salaries in the U.S. 2023
• New York – $132,826
• New Hampshire – $128,704
• California – $127,388
• Vermont – $121,599
• Idaho – $120,011
• Massachusetts – $119,234
• Wyoming – $118,644
• Maine – $117,802
• Washington – $116,118
• Hawaii – $115,887