Nearly a decade ago, the Harvard Business Review published an article titled “Data Scientist: The Sexiest Job of the 21st Century” citing a role for which demand was high but supply of talent was scarce. Universities, including University of Maryland Global Campus (UMGC), were just starting to launch degrees in data science.
While data science is a degree widely offered today, the employment situation is still a “job seeker’s market.” According to the U.S. Bureau of Labor Statistics, demand for data scientists will grow 31 percent from 2019 to 2029, making it one of the fastest-growing occupations, with the potential for 11.5 million data science jobs to be created by 2026.
What Is Data Science?
Data science involves the analysis and model building of an immense scale of data using Artificial Intelligence (AI) approaches that include Machine Learning (ML) and deep learning methods. These methods are used by businesses to evolve and boost growth as technology and information shift and advance.
Data science is useful in practically every career field, from digital entertainment to the development of effective drugs and vaccines to save lives. Data science is even used in the creation of “smart cities,” where AI and ML are used in pollution control and prevention, efficient energy management, and integrated systems managing the complexities of urban transportation.
Netflix has consistently used data science to drive decision-making. The popular streaming service began as a DVD mailing service. As technology advanced, their data led them to a business model based on streaming. Other companies, such as Disney, HBO, and Apple are following suit and focusing more on streaming as well. They compete by building ML customer preference models and continue to gather research to improve their product and stay ahead of the competition.
What Skills Are Required for a Data Science Career?
Data science is an evolving, multidisciplinary area of study that requires competencies in the following areas to begin a successful career:
- Knowledge in the business and/or industry sector you wish to pursue
- Data analytics
While writing code still has a prominent place among data scientists, the rise of software, particularly in the realm of data visualization, has made low and no-code data science much more prevalent. An understanding of the mathematics underlying data science is important, particularly for advanced machine learning and artificial intelligence applications. What is equally important are communication skills and the ability to translate the complexity of data science to all stakeholders. Since data science has applications in nearly every area of business and industry sector, there is an incredibly wide range of choice for specialization.
What Will You Study in a Data Science Program?
A good data science program will cover a range of technology, analytics, model learning, and more. You will learn to manage and manipulate data and create visualizations to extract meaning from data. In more advanced classes, you will build predictive models using different ML techniques, applying AI and Natural Language Processing methods to gain insights from text, images, and videos. From your analysis, you will make strategic data-driven recommendations that directly impact business outcomes. At the same time, you will acquire fundamental knowledge and skills in data science that will equip you to adapt to future changes in tools, technology, and the marketplace. Be prepared for constant changes and updates in software and tools used, such as:
- Open Source – SQL, Python, R, Hadoop, Spark
- Proprietary – Tableau, Power BI, Qlik, SAS
- In the Cloud – AWS, Azure, IBM Cloud, Google Cloud
How to Begin a Career in Data Science
Do your research.
Before jumping head-first into a degree, you might consider taking a Massive Online Open Course (MOOC) to see if this is the right field for you. When researching a degree program, determine whether the program is strongly aligned to one technology or whether you’ll be exposed to a variety of technologies throughout the course of your studies. Keep in mind that like all technological fields, data science changes quickly and a particular technology that is used this year may not be next year. Look for programs that offer practical industry-based projects that address real-world problems. If you are considering an undergraduate degree, make sure your prospective school offers various levels of coursework in data science. For example, some undergraduate programs are built around three levels of data science credentials:
- General education courses (entry level)
- Teach how to read data
- Give the student a data literacy foundation
- Help everyone regardless of industry
- Embedded certificate
- A strong differentiator for job opportunities
- A great way to continue along the data science pathway
- An excellent supplement to any major where courses in the certificate will concentrate on the low math/low code software-based approach
- B.S. in Data Science
- Represents the deep dive into data science
- Includes advanced courses in machine learning and artificial intelligence requiring additional mathematics and skills in programming
Leverage your existing knowledge base and experience.
Knowledge and experience in a business area and/or industry sector are extremely important in data science. Your previous experience in human resources, retail, or government can be valuable in your job search. For example, you could join a data science firm that specializes in HR, or you may become a data scientist within a retail company. If you want to pursue a new career field entirely, consider how the skills you've obtained from experience will supplement your newly acquired knowledge and skills in data science.
Get tangible data science experience.
When researching a data science degree program, look for a program that features practical industry-based projects. Capstone courses and internships are great ways to get practical experience. Check that schools offer no-cost lifetime career services to help with networking and applying for internships. Join student associations or industry-based education groups. Find out if the data science program has instructors who are employed professionals in the field. These instructors will not only bring the latest data science into the classroom, but they’ll also be a valuable resource for employment trends and advice.
Be prepared to be an active, non-stop learner.
Data science is constantly evolving. This field provides the opportunity to work at the cutting edge of technology and its applications. Employers will look for evidence of a sincere desire to learn in a candidate’s resume and during an interview.
According to a recent survey by the Bureau of Labor Statistics (BLS), a worker will have an average of 12 jobs in his/her lifetime and the median time workers had been with their employer was 4.1 years. We live in an era where constant job searching, networking, and upskilling have become the norm. Whether it’s one course, a software and skills-centric certificate, or a full bachelor’s or master’s degree, data science provides many options to join in this exciting, evolving career field, offering employment opportunities in nearly every industry sector.
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