If you are considering getting a bachelor’s degree in a tech discipline, then data science should be at the top of your list. It offers plenty of career options as well as high earning potential. Though it takes students an average of four, and in some cases, five years to complete, you will not regret the time commitment.
The purpose of this article is to help you better understand the career opportunities and earning potential available to those who’ve earned a data science bachelor’s degree.
Before We Begin
- The Bureau of Labor Statistics is forecasting 11.5 million new jobs by 2026. Yep, you read that right. The data science industry is in full boom. However, this is only one of the reasons why a data science bachelor’s degree is an excellent choice.
- The data science industry includes a wide array of career opportunities including data analyst, data scientist, and data engineer, and several data collection specialties that aid the development in artificial intelligence (AI).
While these job titles sound very similar, they all perform different job functions. However, across all of the career opportunities in data science, the average salary is just under the six figure mark at $96,000 annually on average. There are many careers in data science that have an average yearly gross income that is well above $100,000.
Hopefully, these stats on the data science industry alone are enough to make you feel confident about your data science major choice. However, if you decide that data science is not the right bachelor’s degree for you, but you are still interested in a career in data science, then there are a few different majors you can choose from instead.
If data science is two things: it is computer based and statistics heavy. Therefore, a bachelor’s degree that is suitable for data science will include course offerings in computer science, statistics, mathematics and statistics, business intelligence, and information technology.
You could also choose to major in a social science such as sociology, as much of the data that is collected by data scientists is based on society. This is especially true when it comes to the machine learning and AI fields, as the goal of both of these data science subfields is to drive machines to emulate humans. As long as your bachelor’s degree program prepares students in the discipline of data science and analytics, you will likely be in good shape to begin a career in data science post-graduation.
Regardless of which degree you chose, the data science industry has a large amount of career trajectories. Let’s take a closer look at some of these career opportunities, and the earning potential within each.
After you graduate from your data science bachelor’s program, you will likely end up in an entry-level data science role as an analyst. This role will involve combing through all of the data collected by mining programs engineered by senior data scientists.
Once the data is collected, analysts sort through and filter what is important, interesting, and non-relevant. To do this, you will use open-source database management programming like SQL and MongoDB. Within these data management programming environments, you will program rules that will allow you to better organize collected data.
Once you have gotten your feet wet in data science through an analyst position, you will be eligible to move to an engineering position. Here is where the real fun begins. As a data engineer, you will be responsible for developing data collection methods that gather targeted information.
Developing data collection systems and processes involves intense coding. You will need to know how to code in languages like Python and R to build these data collection systems. This is where the computer science skills you acquired during your studies will come in handy.
It is no doubt hard work, but it is also very rewarding. Once the data collection systems you build are firing, you will have countless lines of data streaming into your database. This is the information that tells the data scientist – the head honchos of data science teams – if their hypothesis is correct, and where it might need to be adjusted.
As a data engineer, it is up to you to make sure that this data is collected correctly, from the right sources, and on the right subjects. Doing this so will almost guarantee that you learn something new every day. You will collect data on any number of subjects, be it humans, animals, sports metrics, or business analytics. You name it, and there is someone, if not entire teams of people, who have collected data on it.
This is it. This is why you got your data science bachelor’s degree: to rise through the ranks and become a data scientist. As a data scientist, you will be in charge of the entire data collection and analysis operation. It will be up to you to decide what needs to be studied, how to study it, and when to know you have enough data.
You will lead the data science charge for your company. To do this effectively, you need to have a deep understanding of data collection systems, including how to build them, and which ones to use, and when to use them. You will also need a strong understanding and appreciation for database analysis. Data scientists need to know how to train analysts on what to look for in the data that is collected, and how to properly sort and organize the data so that the information you need is readily available.
Your social science skills will come in handy as a data scientist because you will need to decide who and what to study based on your ability to develop a strong and testable hypothesis. Likewise, your leadership ability will also be put to the test as you will be leading a team that consists of both data engineers, data analysts, and possibly even other data scientists.
Once you reach the level of a data scientist, you may find it beneficial to pursue a master’s degree in data science to further enrich your data science knowledge. A master’s of science in a subject such as sociology will go a long way towards increasing your earning potential, as well as your understanding and appreciation for your work. You could even opt for a master’s degree program in a subject that is relatable to data science, but not directly the study and collection of data.
Conversely, a masters degree in information systems, or computer science, will give you a deeper understanding of the technology components of your work, and how to more effectively deploy the programming of data collection and analysis tools.
Data Science Career Trajectory
You can consider the data science career trajectory in a way similar to that of a biology lab. In a biology lab, here is typically a head scientist who manages a team of field researchers responsible for collecting the biological data needed for a study. Below them are the field researchers and laboratory technicians. These are people in charge of organizing and maintaining the notes on the samples that are collected by the field researchers.
So, in a data science career, you will likely start as a data analyst (the lab tech), and grow into a data engineer (the field researcher), and eventually become a data scientist (the head scientist in charge of the study.)
Big Data Salaries
As with most corporate ladder structures, the earning potential in big data careers increases as you climb the ranks from data analyst, to data engineer, and finally to data scientist.
The average salary for a data analyst is about $60,000 annually. A data engineer makes about $92,00 annually. When you reach the point of a data scientist, you can look to earn as much as $185,000 per year.
The machine learning industry is incredibly interesting, and it is rapidly evolving. It is a very data-heavy subfield of the tech industry, and therefore employs plenty of highly skilled data science teams. As a data scientist in the machine learning industry, you will be in charge of the data collection on everyday human life. Machine learning engineers will in turn take the data that your team has collected and organize, and program machines how to think, react, and behave in ways that closely resemble humans.
Due to the data reliance of machine learning, data scientists and machine learning engineers work closely together in order to make sure the right information is being collected, and that it is being analyzed for the right results.
Natural Language Learning
The Natural Language Learning (NLP) process is a subset of the machine learning industry that focuses on teaching machines the human language. Since there are lots of different human languages and dialects, natural language engineers need tons of data on how different words in each language and dialect are written and pronounced.
The data collection process seems overwhelming. However, it is more exciting than overwhelming. By working on a data science team in the NLP subset, you will learn so much about human languages. You will actively be gathering information on languages like English, Spanish, German, Japanese, Mandarin, and countless others. These languages are spoken across different countries, continents, and cultures. There is arguably no better way to learn a new language.
The sheer amount of language data needed for this kind of programming also means that if you choose to follow this career path, you are always sure to have a job.
Additionally, if you find a home in the machine learning industry, then you may choose to get a master’s degree in a more technical subsect like mechanical engineering. A master’s in a heavy tech subject with a background and experience in big data collection will nicely complement each other. This combination will further ensure your upward career trajectory in data science and your earning potential.
For reference, the average salary for a machine learning engineer is $112,000 per year.
A Data Science Degree Checks Out
So there you have it. The ins and outs of a data science bachelor’s degree. The career opportunities, trajectories, as well as the high earning potential. The additional graduate degree that can complement a career in data science, and the data science subsets that you might want to consider as a career path.
Hopefully, the information discussed in this article will be enough to decide the right major for you. As was mentioned above, you do not necessarily need to earn a data science bachelor’s degree to have a robust career in data science. However, you should make sure that your major’s program offers extensive preparation in statistics, mathematics, and some social science courses. All of which are vital to a career in data science.
While your decision on which bachelor’s degree to pursue should ultimately be based on your time commitments, needs, and interests – on a scale of the most profitable, and most dynamic career paths accessible through a bachelor’s degree program, data science checks out.