Many beginners in data science are often confused about what job role they should prepare for. People who are starting to learn data science have either an engineering background, a statistical background, or a non-computer background. Each person has their own set of skills and knowledge, so we can say that there are different roles in data science for professionals from different educational backgrounds. If you want to know how to choose a data science job based on your education and skills, this article is for you. In this article, I will walk you through a comprehensive guide on how to choose a data science job for yourself based on your education and skills.
Here’s How to Choose a Data Science Job
Below is how you can choose a data science job according to your skills and educational background.
If you are from an Engineering Background
If you are an engineering student or someone who has some experience working as a Software Engineer in the industry, you should choose the role of a Data Engineer or a Machine Learning Engineer. These two roles are different, but both are a good fit for you if you have an engineering background.
The work of a data engineer is to collect and prepare the raw data that is used later by a data analyst, data scientist, and machine learning engineer. And the work of a machine learning engineer is to develop and maintain machine learning models. Making the machine learning model available as a final product is also a responsibility of a Machine Learning Engineer.
If you have completed your Engineering and want to work as a data science professional, you should prepare for the role of a Data Engineer. And if you are an experienced software engineer who wants to work as a data science professional, then the role of a Machine Learning Engineer is perfect for you.
If you are from a Non-IT Background
If you come from a non-IT background with a good understanding of statistics, the Data Analyst role is perfect for you. The role of a data analyst is to tell the story behind the numbers. That is why statistics is one of the most important skills for a data analyst.
If you are from a non-IT background with no knowledge of statistics and want to work as a data science professional, still I will recommend preparing for the role of a Data Analyst by introducing yourself to statistics and a programming language like Python or R.
The Data Analyst role is perfect for someone with a statistical or non-IT background. You can also see it as a profession that will help you enter the field of data science.
If you have Strong Problem-Solving Skills
If you have a background in math and programming and have strong problem-solving skills, you should prepare for the role of Data Scientist. The role of a Data Scientist is to solve business problems by understanding the data generated by the business. Of course, you need a good knowledge of mathematics and programming, but if you are a good problem solver, you should prepare for the role of Data Scientist.
If you come from non-IT background and want to prepare for the role of a Data Scientist, I would recommend that you first enter the Data Science field as a Data Analyst, and then you can prepare for the role of a Data Scientist after gaining experience.
Summary
So this is how you can choose a data science job for yourself according to your educational background. Below are some of the points that you should take away from this article:
- If you are an engineering student or someone who has some experience working as a Software Engineer in the industry, you should choose the role of a Data Engineer or a Machine Learning Engineer.
- If you come from a non-IT background with a good understanding of statistics, the Data Analyst role is perfect for you.
- If you have a background in math and programming and have strong problem-solving skills, you should prepare for the role of Data Scientist.
I hope you liked this article on how to choose a Data Science job according to your skills and educational background. Feel free to ask valuable questions in the comments section below.
