Data Science Career Guide Overview
So, you’re interested in pursuing a career in data science? It’s easy to see why!
As an exponentially expanding number of industries see the benefit of using analytical data to improve business practices, big data and data science career opportunities are exploding. In fact, employment for statisticians in data science related careers is projected to grow 33.8% from 2016 to 2026, according to the Bureau of Labor Statistics (BLS), described as the fastest occupational growth in the mathematical area of the industry.
Data science related occupations are likely to enjoy excellent job prospects, as many companies report difficulties finding highly skilled workers. That means there’s more demand for data science professionals than there is supply, which is good news for data science students and professionals. As a result of this shortage, you’ll find that there is a wealth of different avenues that a data science career can take. While it’s always good to have options, it can sometimes be difficult to understand how these careers differ and what kinds of skillsets and educational backgrounds are required for each. This can present a challenge for those just starting out in the world of data science.
That’s where our Data Science Career Guide comes in! The goal of this resource is to introduce the different types of careers in the data science field and summarize what qualities a good candidate for these roles needs to possess.
For more comprehensive information about detailed roles, education and skills required, prospective salary information, and the job outlook going forward, click on each individual career link.
The term “data scientist” is a relatively new one in comparison to some of the other roles mentioned here. Since all the roles discussed below encompass the wider field of data science, the specific job title of “data scientist” can sometimes be perceived as an elevated synonym for “data analyst” but that’s not the case. Data scientists must be a mathematician, a computer scientist, and a business strategist all at once, necessitating expertise in several different disciplines. This complex skill set means that data scientists need to consistently have one foot in the information technology sector, and another planted firmly in the business world. That’s part of what makes them in such high demand.
Like many of the roles discussed, data scientists must have a combination of analytic, machine learning, data mining, and statistical skills in addition to experience with algorithms and coding. Data scientists also have expertise in the following programs: R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark. But one of the most important skills that a good data scientist possesses is the ability to explain the significance of data in a way that can be easily understood by others. Polished verbal and written communication skills are key for this role, in a way they may not be for many others listed below.
Often, data scientists will find themselves called upon to express their findings and analysis to their superiors, colleagues on different teams, and even company stakeholders who may (but just as often may not) be able to follow understand the technical jargon that data science professionals likely take for granted. The Harvard Business Review rates this lack of easy communication between data teams and non-technical stakeholders as one of the toughest barriers facing the field. “Data teams know they’re sitting on valuable insights but can’t sell them,” HBR says. “They say decision makers misunderstand or oversimplify their analysis and expect them to do magic, to provide the right answers to all their questions. [Executives] don’t see tangible results because the results aren’t communicated in their language.” This is core to what a good data scientist will be able to do. Explaining what information has discovered and what needs to be done about it now that they have, all in a comprehensive and easily digestible way. Not always an easy task.
For more information, check out our Data Scientist Salary Guide.
A data analyst’s role is to collect, process, and perform statistical data analyses with the goal of helping companies make better business decisions. Data analysts are most often responsible for transforming data sets into usable forms, such as reports or presentations. Depending on the industry, this may involve gleaning insight from consumer data sets, making strategic recommendations based on dense financial data, or simply organizing messy data into a more accessible format. A qualified data analyst will have a solid understanding of R, Python, HTML, C/C++ and SQL. These positions are often on the lower end of the organizational chart; however, those are who just entering the data science field will find these roles to be some of the easiest to qualify for and you will have ample opportunity to learn and advance into higher level roles. Our Data Analyst Salary Guide more specifically breaks down this information
Data engineers are the designers, builders, and managers of the information or big data infrastructure. They assist in developing the architecture that helps analyze and process data in a manner best suited for their organization. It is their role to also make sure those systems are performing smoothly. Data engineering differs from other data science careers in that it is focused on the systems and hardware that facilitates a company’s data activities, rather than analysis of the data itself. A data engineer has a background in software engineering as well as skills in the following languages: SQL, HIVe, Pig, R, Matlab, SAS, SPSS, Python, Java, and Ruby. Their duties also involve providing the company with valuable data warehousing solutions. This role is considered a senior position and requires an advanced degree and years of experience.
The business analyst is often less technically oriented but has a deep knowledge of the different business processes and embodies business intelligence. The role of the business analyst is to improve business processes by serving as a liaison between business and IT with a clear directive to focus on advancing strategic business objectives. Most business analysts are focused on producing usable deliverables, such as reports and presentations, that can be easily understood by others in the organization who are not data scientists themselves. Business analysts possess the basic skills of data visualization tools and data modeling, however their educational background is in business. The duties of a business analyst are very similar to those of a data analyst. Business analysis is an excellent career choice for someone that has a solid foundation in numbers and an active interest in business management or development. Our Business Analyst Salary Guide provides a more in-depth breakdown of these facets.
A marketing analyst studies information to better assist companies in making informed decisions about market opportunities. The goal is to determine which product a company should produce and how to sell it. A market analyst uses statistical, math, and analytical skills while interpreting large data sets. This career is more of an entry level job in data science.
Thanks to the increasing importance of big data, data architect roles are becoming more common. This position creates the blueprints for data management systems to integrate, centralize, protect, and maintain data sources. They understand the languages of SQL, XML, Hive, Pig, and Spark as well as the skills of warehouse solutions, systems development, and database architecture. It is a natural evolution from data analyst to database designer, combining both skill sets. The position requires an advanced degree and many years of experience.
Data and analytics managers lead data science teams. It can be thought of as a luxury position, as not all businesses and organizations have a large enough data team to require a specific managerial position outside the confines of the other data science roles. For larger organizations that have a large team of data professionals, though, a data and analytics manager can be key to making sure everything runs smoothly.
These management positions not only possess data science technical skills, but also leadership and project management experience. They manage a variety of positions including but not limited to data engineers, data scientists, and data analysts and will often be promoted to their role. They also often serve as the spokesperson for the department, which can require the same level of polished verbal and communication skills that make for a successful data scientist. This role is a senior position that requires an advanced degree as well as many years of supervisory experience. In some businesses and organizations, the data team may even be run by a C-level executive role, often known as the chief data officer.
Business intelligence analysts gather data in a variety of ways, some of which include mining a company’s computer data through software; reviewing competitor data and industry trends to develop an understanding of where the company stands in the overall picture; and identifying ways in which they can improve and reduce costs. This position requires an advanced degree and years of experience as a business analyst.
A data mining specialist is responsible for identifying patterns and relationships to help a company predict future behaviors. Through the process of transforming data into insights, a data mining specialist can help businesses make more intelligent, data driven decisions. To accomplish this, a data mining specialist uses statistical software to help research, mine data, and model relationships.
A statistician is someone who works with mathematical techniques to help analyze and interpret data to solve real world problems. Statisticians can work in a variety of fields including (but not limited to) academia, government, healthcare, business, engineering, and marketing. A statistician can choose to work as a generalist but specialization within a specific field can also help them stand out to potential employers.
Machine learning is one subset of artificial intelligence (AI) that works with big data applications and is accomplished through advanced mathematics and software programming. By leaps and bounds, business has become the most prominent use of machine learning today. Customer-facing businesses of all types are employing machine learning to better understand customer tendencies and preferences so that they may apply marketing and advertising strategies that accurately and effectively target these tendencies. The goal of machine learning is to program computers to accept real world data from real people utilizing technology and determine from that data the person’s likes and tendencies. These results are then employed to place the most relevant advertisements in front of the customers. Machine learning engineers help manage all of that.
Machine learning engineering is not an entry-level career option. It takes years of experience in data science and software engineering (as well as an advanced college degree) to become qualified for a machine learning engineer role. Our machine learning engineer guide provides an overview of the machine learning engineer role and lists the steps required to begin and maximize career success, including a detailed list of job responsibilities, background, education, and experience required to be successful professionals. You can also discover basic salary information, and the future outlook for the ML engineering job market.
Database administrators are information technology professionals that ensure the optimal storage and access to an organization’s data. This is done by ensuring software programs are designed, managed, and maintained to permit rapid access whenever and however needed by authorized personnel. Database administrators also work with cybersecurity professionals to safeguard the data from unauthorized access and damage. For many smaller companies and organizations, this role will be combined with that of the database developer in one singular database-centric role.
At the center of almost every organization’s information storage and data mining operations is a talented database developer. Database developers, also known as database designers or database programmers, are responsible for the design, programming, construction, and implementation of new databases, as well as modifying existing databases for platform updates and changes in user needs. For many smaller companies and organizations, this role will be combined with that of the database administrator in one singular database-centric role.
For a comprehensive exploration of each of these highly nuanced and explosively expanding industry areas that fall under the broad umbrella of Data Science Careers, page through the collected articles in this Career Information Guide and some of the top degrees needed to pursue your area of interest and expertise and the projected return on investment per chosen specialization.
Continue with this guide. Explore the next career specialization: Data Scientist.