Data science is one of the fastest-growing tech-based careers out there, and learning skills in such an area is the perfect way to switch or grow your career. After all, all industries require data scientists. For the layman, it is a data scientist’s job to analyze information. They take a multidisciplinary approach to data and conclude by utilizing statistics, human behavior analysis, software engineering, machine learning, data intuition, and experimental science. In addition, they find new insights to provide solutions for problems and ensure the best possible outcome.
That said, a data science degree is not for everyone. It requires a high aptitude in the mathematics, statistics, and programming fields. However, nobody will stop you from enrolling in a data science degree program if you have what it takes. Once you complete your degree, you can apply your craft in a vast array of roles. Companies such as Microsoft, Apple, Google, and many others require data scientists. So, you won’t be short of career opportunities after completing a data science degree. With that in mind, let us look at some career choices for data science degree holders. Some of these career choices include:
Table of Contents
It is a data architect’s role to develop blueprints for a company’s data management system. In layman terms, they design and deploy applications and solutions. These include creating databases that allow employees and business owners to access critical information correctly and on time. Besides designing databases, they also have to identify ways to improve the functionality and performance of a company’s existing data systems while managing, protecting, centralizing, and maintaining them.
To pursue the data architect career path, a bachelor’s degree in data science is an absolute necessity. However, a master’s degree in business analytics or data analytics will improve your chances of employment. The average salary of a data architect is around 121,461 dollars per year.
The data analyst’s role is to mine data from secondary and primary resources and then reorganize it in a format that business owners and managers can easily read and understand. Moreover, they collaborate with organizational leaders, engineers, and programmers to search for process improvement opportunities while recommending system modifications. They also prepare reports for business leaders that communicate predictions, patterns, and trends utilizing relevant data. In addition, data analysts analyze data sets using statistical tools through predictive and diagnostic analysis techniques.
To become a data analyst, you must obtain a bachelor’s degree in data science. However, employers look kindly at individuals who have also acquired a master’s in statistics, finance, computer science, or business analytics. According to the BLS, the average salary of a data analyst is around 86,200 dollars annually.
Data engineers search for new data trends and create algorithms to ensure raw data is suable by their organization. They need to have in-depth knowledge of developing dashboards, optimize data retrieval, and compile reports and other visualizations for business owners and managers. This particular data science-related role requires excellent technical skills, including SQL database design and numerous programming languages. Moreover, they also need top-notch communication skills. They have to collaborate with other departments to ensure that business leaders gain a competitive advantage.
Typically, you must have a bachelor’s degree in computer engineering, physics, applied mathematics, or computer engineer to apply for the most entry-level positions. That said, the payoff is excellent. In fact, according to Payscale, the average annual salary of a data engineer is around 92,519 dollars.
Machine learning engineer/scientist.
A machine learning engineer is an IT specialist who designs and researches self-sustainable AI systems to automate predictive business models. They also create and develop AI algorithms capable of making and learning predictions. A machine learning engineer works in a data science team that comprises administrators, data analysts, data scientists, and data architects. They link AI and machine learning systems with data scientists who focus on model-building and statistical work. They also have to organize, assess, and analyze large data amounts while optimizing learning algorithms and models.
To apply your craft as a machine learning engineer, individuals need to obtain a bachelor’s degree in analytics, machine learning, or computer science. According to Glassdoor, the average salary of a machine learning engineer/scientist is around 135,196 per year.
In short, an enterprise architect’s role is to maintain and update an organization’s network and IT services. In addition, they have to oversee, improve, and upgrade enterprise hardware, software, and services. To be effective in such a role, you will have to remain up-to-date with the latest technologies and networking trends while looking for software and hardware that might enhance a business’s operational side.
To work in such a role, you must have at least a bachelor’s degree in information technology, data science, or computer science, followed by a few years of relevant work experience. That said, individuals who earn a master’s degree in enterprise architecture will drastically improve their employment chances. According to Glassdoor, the average annual salary of an enterprise architect is around 148,061 dollars.
In the end, data scientists are required in every field out there, ranging from government organizations to healthcare facilities to law firms to everything in between. Where big data is involved, data scientists are sure to be present. So, to break into the data science field, consider the career choices mentioned above and the educational requirement for every career choice. Doing so will ensure you fulfill all the requirements before you apply for a particular job position!