Want to become a data scientist? If so, you better plan on studying a lot. For most people, data science is a rather complicated subject and requires lots of analytical thinking. There are many types of data science to learn if you are wanting to become a data scientist, including statistical machine learning and Bayesian thinking as well as probability and other core concepts. If you are going to stay a step ahead of the competition in this industry, you are definitely going to need an advanced degree. Only eight percent of data scientists have a bachelor’s degree, while 44 percent have a master’s. The remaining 48 percent have a Ph.D.
Once you master data science and graduate with a degree, you will have several career options to choose from. For now, let’s take a close look at these career options, including their salaries.
Ideal Jobs for People With a Degree In Data Science
Business Intelligence Specialist
When you start looking for a job as a data scientist, you will notice most employers are found in the following industries:
As a business intelligence specialist, your primary responsibility is to comprise reports to help your employer stay ahead of competitors. You will likely use statistical tools as well as SQL and machine learning to develop reports and conduct analyses. Your findings are sent to the appropriate entities and you are responsible for making modifications to the reports if necessary. Your employer’s goals will determine whether you are to make modifications or begin a new project.
According to PayScale, the average annual salary for a business intelligence specialist is around $72,500.
Data and Analytics Manager
This is a more advanced role in that as a data and analytics manager you will be responsible for guiding a team of data scientists toward a predetermined goal. You will need to make sure the team’s priorities are in line with goal completion. In this role, it is helpful to possess strong technical skills and be well-versed in a variety of technologies, including SQL and SAS. Since you will be leading a team of other data scientists, you will need to have friendly social skills. As a leader, always remember that it’s not about controlling and forcing workers to follow you; instead, it is about leading by example.
According to Glassdoor, the average annual salary for a data and analytics manager is anywhere between $87,000 to $116,800.
Data mining specialists are often referred to as data engineers. Industries that commonly employ data engineers include the following:
There is a high demand for data engineers. Employers are quickly learning they have access to massive amounts of data thanks to the Internet and smart technology, and when this data is properly analyzed, it gives them a competitive edge; this is where data mining/engineering proves to be of the utmost value.
As a dining mining engineer, you will spend your time collecting massive loads of data and performing various analyses to it. If the data you collect is unstructured, you will be responsible for sorting through it and developing a structured infrastructure for it to be analyzed. If you are facing big data issues, you will need to implement various programming and scripting languages tools to develop appropriate solutions. A data engineer is often considered the jack of all trades because he or she is very familiar with several types of scripting languages and can perform an assortment of web development tasks.
It is important to point out that data engineering and data science are not considered one in the same. However, many people with data science degrees pursue the career of a data engineer. If you are one of these people, you will need to be well-versed in:
- SQL and other database solutions
- Hadoop based analytics
- ETL tools
- Data warehousing
- Machine learning
According to PayScale, the average annual salary for a data miner/engineer is upward of $90,000.
Machine Learning Engineer
For companies that sell data-related services, or data itself, there is a high demand for machine learning engineers. To be successful in this role, you will likely need to be distinguished in:
As a machine learning engineer, your primary goal is to produce data-driven products/services. You won’t spend much of your time answering questions related to the operational processes of your employer. Instead, you are more focused on customers because you are creating a product or service that is going to be sold to them.
You won’t likely find a job as a machine learning engineer within small companies. Employers that need to fill this type of position are typically extremely large and have the ability to collect and store massive amounts of data. Generally speaking, these are going to be multi-million and multi-billion dollar companies.
According to Glassdoor, as a senior machine learning engineer, you can expect to make more than $140,000 a year.
A statistician is often referred to by other titles, but this is a job that is perfect for people with a strong background in statistical theories. To excel in this line of work, you will need to have a mindset that is both logical and stats-oriented. You will also need to have a keen eye for collecting, storing and analyzing massive amounts of data in a way that it produces valuable knowledge for your employer. Because statisticians tend to have a quantitative background, they are able to quickly master new technologies and develop business-transforming insights.
According to PayScale, a statistician with five years of experience will make nearly $70,000 a year.
The rise of Big Data has quickly boosted the need for data architects. If you pursue this role as your career choice, you will spend your time developing blueprints for systems that can collect, store, protect, and analyze data. You are essentially an architect creating data management systems. Some employers may extend your duties by allowing you to work in the collection, storage, protecting and analyzing processes; however, many employers will just prefer for you to be in charge of creating the information management system blueprints.
According to PayScale, a data architect will make upward of $112,000 a year.
Many industries have a high demand for data scientists. Most data scientists, however, work in the technology and marketing industries. It is important to note that as a data scientist, you will likely wear more than one hat, meaning your job responsibilities will vary. You may find yourself creating blueprints for data management systems one day, and then the next day you may be giving a presentation on why you think money needs to be invested in developing your blueprints. Also important to note is that you will need to earn an advanced degree to stay ahead of the competition in this field. In fact, you should plan on spending anywhere from eight to 12 years in college.