Data science has emerged as one of the hottest and most in-demand fields over the past decade. As companies and organizations collect more data and look to gain insights and value from it, the need for data scientists has skyrocketed. This has led many to wonder – is a data science degree respected and valuable in today’s job market? Some quick answers to key questions provide context.
What is data science?
Data science is an interdisciplinary field focused on extracting insights from data. Data scientists use mathematics, statistics, programming, data visualization, and machine learning to analyze large, complex datasets. The goal is to uncover patterns, trends, and knowledge that can inform business or research decisions.
What do data scientists do?
Data scientists work on collecting, cleaning, and organizing large sets of structured and unstructured data. They analyze data to detect trends, develop models, and create visualizations to communicate insights. Data scientists build machine learning algorithms, design experiments, and translate results into solutions for business, research, or social problems.
What skills are required?
Data science requires skills in:
– Programming languages like Python, R, SQL
– Statistical and mathematical concepts
– Data mining, modeling, and analysis
– Machine learning techniques
– Data visualization
– Communication and storytelling
What are common degree options?
Common undergraduate degrees include:
– Bachelor’s in Data Science
– Bachelor’s in Statistics or Mathematics
– Bachelor of Science in Computer Science
Many have graduate degrees like:
– Master’s in Data Science
– Master’s in Applied Statistics
– MBA with Data Science Concentration
Some have PhDs in fields like statistics, computer science, analytics, or operations research.
Is a data science degree respected?
The short answer is yes – a bachelor’s or master’s degree in data science is generally very well respected and valued in today’s job market. Here are some reasons why:
High demand for data science skills
As data has become a key strategic asset for companies, demand for data science skills has soared. LinkedIn’s 2020 Emerging Jobs Report named Data Science as the #1 emerging job for 2020. There is a significant talent gap, with supply of candidates not meeting the demand for roles.
Competitive salaries
According to Glassdoor, the average base salary for a data scientist in the US is $117,345. Below are average salaries for common data science roles:
| Data Scientist | $117,345 |
| Machine Learning Engineer | $114,856 |
| Business Intelligence Developer | $68,208 |
| Data Engineer | $108,578 |
These highly competitive salaries demonstrate how the market values data science skills.
Job growth
The Bureau of Labor Statistics projects over 16% job growth for data science related roles like statisticians and operations research analysts from 2019 to 2029, much faster than the average for all occupations. There is expected to be demand for hundreds of thousands of new data science professionals.
Data science drives business impact
Data science delivers tremendous business value through insights that inform strategic decisions and new intelligent products or systems. Companies increasingly view data analytics as a competitive advantage and require data talent.
Companies specifically recruiting data science grads
Major technology companies like Facebook, Amazon, Apple, Netflix, Google, Microsoft as well as consulting firms like BCG and McKinsey all have dedicated on-campus recruiting for data science roles. They target and compete for data science graduates of bachelor’s and master’s programs.
Universities launching data science institutes and degrees
Seeing the emerging importance of data science, universities like MIT, Berkeley, Harvard, Columbia, NYU, Northwestern, and many others have all launched dedicated data science degrees and research institutes in recent years. The creation of these programs align with job market demand.
What can you do with a data science degree?
There are diverse career paths and roles for data science degree holders, including:
Data Scientist
Analyze data to detect insights and trends. Build machine learning models. Communicate findings to inform business decisions.
Data Analyst
Clean, process, and perform analysis on company data. Develop reports, visualizations and dashboards.
Business Intelligence Developer
Design and develop BI tools and big data infrastructure. Create systems to organize and analyze data.
Data Engineer
Build infrastructure for data pipelines, warehousing and integration. Ensure quality data collection and availability.
Machine Learning Engineer
Develop and optimize machine learning models. Implement models into applications and systems.
Quantitative Analyst
Apply data modeling to analyze financial markets, risks, or investment strategies.
Marketing Analyst
Leverage data analysis to inform marketing campaigns, strategies and initiatives.
Operations Research Analyst
Use advanced analytics methods to optimize operations and make business decisions.
Challenges of data science degrees
While data science degrees are highly valuable, there are some challenges to consider:
Fast pace of change
Data science is an emerging field with new technologies, techniques, and tools emerging rapidly. Degree programs must keep up with these fast-changing developments. Lifelong learning is required.
Combination of diverse skills
Data science combines statistics, programming, critical thinking, analytics, communication, and business acumen. Mastering this diverse toolkit of abilities poses a challenge.
Lack of clear career path
As a relatively new field, data science roles are still being defined and evolving. The skills needed for various roles and optimal career paths for grads are still being figured out.
Shortage of qualified faculty
With rapid growth of data science programs, schools face a shortage of PhDs qualified to teach analytics and specialized technologies like machine learning. Programs vary based on faculty expertise.
Limited math/statistics prerequisites
Some schools offer data science degrees without requiring calculus, linear algebra, and statistics prerequisites. This can leave graduates underprepared for advanced analytical roles.
How to choose the right data science degree
When researching data science degree programs, here are some key considerations:
Math/statistics requirements
Look for programs that require calculus, linear algebra, probability, and advanced statistics. These form the bedrock for data modeling and analysis.
Computer science requirements
Python and R programming experience is invaluable. Courses in data structures, algorithms and software development prepare you for real analytics applications.
Machine learning curriculum
Machine learning has become central to data science. Look for coursework in supervised and unsupervised learning, neural networks, and modern techniques.
Analytics methodology
Ensure courses that teach the processes for data mining, modeling, experimental design, and communicating data insights.
Hands-on opportunities
Programs that offer real-world projects, hackathons, internships and business partnerships provide applied experiences.
Faculty expertise
A Ph.D. level faculty with deep expertise in analytics, statistics and computer science should guide programs. View faculty profiles.
Program reputation
Established university programs with committed resources and partnerships carry more weight.
Conclusion
In summary, a data science degree is generally well respected and provides valuable preparation for in-demand careers. However, prospective students should carefully assess program curriculum, faculty qualifications, hands-on learning, and overall reputation when choosing a degree. A program that builds advanced analytical, statistical, computer science, and business skills will offer graduates the most opportunities. With proper training, data scientists are positioned to provide immense value as companies increasingly rely on data to drive decisions and create intelligence.