Do I need a Masters/PhD to become a Data Scientist?

Last Updated : 03 Jan, 2024

There are many companies present in the world who want people they are hiring to have an advanced degree which can be a Master’s or PhD but these all are done in big multinational companies that require experienced and highly skilled employees. Different criteria is followed by different companies for example, Large companies require certain degrees or certifications to hire quality employees so that they can utilize their resources well. But startups, don’t have any requirements because they are flexible and let people learn.

Do-I-need-a-Masters-PhD-to-become-a-data-scientist

Need of Masters/PhD to become a data scientist

In this article we will look at some of these questions and their answers in a little more detail.

Do Aspiring Data Scientists Need to Pursue a Master’s or PhD to Thrive in the Field?

Whether Data scientists need to pursue a Master’s or Ph.D. in data science completely depends upon the company they want to apply in because if they want to work with a startup then there is no need to apply for masters but if someone is planning for some big multinational companies then they must require a Master’s degree on their resume. So this can be people’s personal choice and also depends on the company they want to work in. A person who has completed their Bachelors’ in data science and have a lot of knowledge of the field can explore the advancement of Data Science through a master course in Data Science because it has many benefits and some of which are:

Advanced Job Roles: After getting a masters degree in data science people can apply for advanced roles in any company because this will enable people to use their depth knowledge of their subject and due to that they will get paid more money as well.
Higher Salary: People who have a Master’s or PhD degree with them then they can apply for a higher job salary role because of their experience in the subject and the knowledge that they gain during their academic year of the degrees.
Demand: Everyone has heard once in their life that data is very important for the future and everything like artificial intelligence is using data to perform their work . Data scientist demand is increasing day by day to analyze large data sets or make predictions or to find similar kinds of patterns in their data.
Better For Resume: Having a master degree can easily attract any employer because it shows the person is having some advanced skill and depth knowledge about that particular subject.
Networking: People when they take their masters or PhD degree, they always try to make connections with the teachers and other members of their class and till this time they have already met a lot of tech professionals.

You can also refer to our article – How to Become Data Scientist – A Complete Roadmap

Academic Route For Data Science

There are a lot of ways through which people can become a data scientist which can be through a master’s degree or PhD degree or else it can be some data science crash course or other tutorials present online. People who wants to start their career as a data scientist must have practical knowledge of these things:

Mathematics: It plays a very important role and especially Linear Algebra, Matrix or Calculus helps people in finding patterns of various data sets and this can also help them predict accurate results.
Probability: This subject is also important and it is considered as basic, if people want to gain command in machine learning. Good command in Probability can showcase that people can apply logistics clearly.
Statistics: For proper analyzing the data people must learn statistics which plays a very important role in analyzing the patterns and through which they give predictions.
Programming: People must learn the programming concepts clearly which includes any of the languages like python or R and data structures and algorithms in that language to implement their ideas.
Machine Learning: Python has many libraries present and due to this reason it is considered as the easiest programming language. People must know about supervised and unsupervised learning along with various python libraries so that they can make a model.

Along with all these necessities there are certain more things on which people need to have a command like deep learning concepts or natural language processing and feature engineering concepts and many more to excel and become a good data scientist.

Alternative Path of being a Data Scientist

There are many people who got a good job without holding a professional degree and because of which there has been an increase in the total number of people in Data Science jobs. Today people are only getting jobs based on their practical knowledge and skills and not on any degree because of which there are different people from different backgrounds in this field.

Let us know more about how people can be a data scientist without having a degree or an alternate path of being a data scientist:

Self learning platforms: There are many websites present on the internet which provide the same data science course and by completing that course people will get a lot of knowledge of data science and with the help of projects that they will make during that time, people can apply in many companies.
Using Projects: People while creating different projects based on Data science can learn a lot about the practical implementation about this industry in detail and this will help them solve complex problems and build a strong resume.
Attending Boot Camps: Experts who take these sessions always add only industry related syllabus in their course and cover all the important topics which help people learn quickly and ensure quality of learning as well.
Change in Criteria: From recent years employers are hiring people based on their resume and practical skills of the student and not through type of degree. This is applicable for all the large companies as well as many startups who want quality employees.
Low Cost: If a person chooses to learn data science through these boot camps or courses then it will not only save their master’s degree time duration but money of that course as well.

There are normal ways through which people can be data scientist by having a Master’s degree or etc but nowadays people enter into this field using their skills and this has helped people in becoming a more skilled analyst in low cost and also it helps employers to use the creativity of people who belong to diverse fields.

Difference Between Skills and Experience?

Each and every data scientist need to have different skills and experience in their field which includes the following things:

Skills Required for Data-Science Role

People need to have skills in their particular field and for this they need to learn about some of the important programming languages like Python, some machine learning aspects like Tensorflow or some of the data visualization tools like Tableau and statistical softwares like SAS.

A data scientist needs to have a clear and in-depth vision about their job because a data scientist needs to perform many tasks before making predictions and this can be possible if they have these skills with some advanced skills as well. Every day a person needs to learn different concepts and because of which they are improving their skills and knowledge.

You can refer to this article – Top 7 Skills Required to Become a Data Scientist

Experience Need for Data Science

Getting practical experience by working in any company helps people learn in a better way like people who would love to work in a retail shop rather than just reading how to work in it. Some soft skills like teamwork, communication or leadership are some of the skills which require practical experience.

There are many skills that a person can learn from different courses but not every textbook gives a practical approach and a real time experience to students.

Conclusion

From the above blog, people can understand the importance of a Master’s degree and they can now decide whether to pursue that degree or not which depends on the type of company they want to work in. Also it is clear how people can become data scientists without actually having any professional degree by just approaching some alternative paths which are discussed above.

People can also understand how important it is to have a practical approach to any skill that they are learning which cannot be provided by any textbooks.