Computer scientist William S. Cleveland laid out his vision for data science as an academic discipline in a 2001 article. This proposal consisted of six areas of concentration for degree candidates, including interdisciplinary data analysis and computing with data. The two decades since Cleveland’s article saw the integration of data science into our daily lives.
Data scientist skills incorporate advanced principles of mathematics and computer science. This discipline is still in its early stages of development, offering room for scientists to drive future trends. Specialized skills for data scientists depend on a practitioner’s chosen career, ranging from sports to finance.
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The Rapid Growth of Data Science
The emergence of data science as a distinct field followed five decades of work by mathematicians and computer scientists.
John Tukey was a noted mathematician responsible for bringing terms like bit and software into popular use. His 1962 article, “The Future of Data Analysis,” broke the titular area of study away from statistics. He defines data analysis - a component of data science - as:
“...procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
This description sounds familiar to data scientists tasked with handling massive volumes of information. Data science evolved alongside computing technology from Tukey’s article into the 21st century. The following milestones contributed to this slow but steady growth for the field:
- 1977: Founding of the International Association for Statistical Computing
- 1996: The International Federation of Classification Societies includes data science in its annual conference title
- 1997: Professor Jeff Wu advocated for statistics to be retitled data science at the University of Michigan
A 2012 Harvard Business Review article called data science “the sexiest job of the 21st century.” The discipline quickly jumped from university classrooms to corporate boardrooms as data became a valuable commodity. Practitioners should keep this rapid ascent in mind as they consider what data scientist skills are needed for the future.
Future Data Science Trends
Businesses, universities, and other stakeholders face obstacles to adopting data science practices. Raw data sources are incomprehensible - and, therefore, useless - without the right data scientist skills.
Anaconda’s survey of 4,200 practitioners identified the following challenges to using data models for operational purposes:
- Meeting IT security standards (27%)
- Re-coding models from Python or R to other languages (24%)
- Managing dependencies and environments (23%)
- Re-coding models from other languages to Python or R (23%)
Solving these challenges will define trends in the data science field. Professionals with dynamic data scientist skills drive these solutions.
Chief data scientists surveyed by Wing VC noted the importance of growing role definitions, including:
- Full-stack generalists (59%)
- Decision scientists (39%)
- Data specialists (33%)
- Data translators (22%)
Automated machine learning (AutoML), cloud computing, and other advancements ensure analytical quality. These tools also decrease workloads for data scientists increasingly tasked with interpreting data for stakeholders.
Pierre Pinna of IPFCconline foresees “the desire to advance in the interpretability of models” with hybrid AI and deep neural networks.
Data Scientist Skills Sought by Employers
A key skill for data scientists is programming in Python.
Based on Anaconda’s 2021 State of Data Science Report, the language remains the most popular option among practitioners. Respondents always or frequently used the following programming languages:
- Python (63%)
- SQL (35%)
- R (27%)
- HTML/CSS (24%)
Programming is an essential skill for data scientists but only one competency for professional success.
Data expert Gregory Piatesky-Shapiro asked practitioners what skills they lacked but wanted to cultivate. The most common responses showed the versatility needed for professional success:
- Deep Learning
- Machine Learning
- Natural Language Processing
Increasing adoption of data science in the corporate world means practitioners need to be familiar with business practices. Anaconda found enterprises most sought big data management and business analytics skills from data professionals.
Effective data scientists view their skills as ever-growing rather than static to stay ahead of new trends.
Learn more about Baylor's University's Online Data Science track
Demand for Data Science Professionals
The data science profession offers scientists a growing list of challenges and opportunities.
Integrating data-driven decisions into our daily lives means data scientists are in high demand and well-compensated. A data science degree is also an excellent investment to raise the ceiling on future salaries.
Experienced practitioners who move into management and executive positions are rewarded for their data scientist skills. The U.S. Bureau of Labor Statistics (BLS) listed a median salary of $98,230 for data scientists in May 2020. The top 10% of earners in this profession earned $165,230 per year on average.
Job opportunities for computer scientists - including data scientists - are expected to grow 22% from 2020 to 2030. A handful of sectors will help drive this demand for skilled data scientists. TechTalks identified the most likely drivers of job growth for data experts:
- Supply Chain Management
Data science is still in its early stages, with plenty of opportunities on the horizon that will sustain demand.
Anaconda’s 2021 State of Data Science Report found 34 industries employing respondents. This distribution is approximately 50% of the 69 industries categorized by The Global Industry Classification Standard. Academic and technology employers only formed 17% of this total, leaving a lot of growth for data science employment in other sectors.
Build on Your Data Scientist Skills
Baylor University’s 100% Online Masters in Computer Science degree program offers a Data Science track that prepares graduates for new opportunities. Experienced faculty members challenge students to build data scientist skills in completely virtual courses.
The Online Masters in Computer Science Data Science concentration sets graduates on the path to career success with courses in:
- Applied Data Science
- Cloud Computing
- Data Mining and Analysis
- Data Visualization
Baylor University's national reputation strengthens graduate resumes seeking senior Data Scientist positions. U.S. News & World Report ranked Baylor No. 75 in Best National Universities in 2021, placing the university among the top 5% in the nation. Additionally, Baylor was ranked:
- No. 34 in Most Innovative Schools
- No. 82 in Best Value Schools
- No. 111 in Best Colleges for Veterans
Find out how Baylor University’s Online Masters in Computer Science degree track in Data Science can help accelerate your data science career.