What is a Data Scientist?
| Liz Simmons Modified on July 6, 2022
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Data scientists use mathematical models to answer questions and solve problems in the real world. They apply advanced tools and techniques to find patterns, create visualizations, and draw conclusions from data sets. These scientists work for computer systems design companies, government agencies, and software publishers.
A data science professional typically needs a bachelor's degree, but many employers prefer data scientists with master's degrees. Certifications are not required to become a data scientist but can open the door to more and better career opportunities.
This guide explains how to become a data scientist and what to expect from a career in data science.
History of Data Scientists
Data science originated in the 1960s as an interdisciplinary field combining computer science, statistics, and mathematics. Many people credit mathematician and statistician John Tukey's 1962 paper "The Future of Data Analysis" as an important early development in the modern field of data science.
Data science draws heavily from statistics but emphasizes the role of technology and incorporates new concepts, like machine learning and artificial intelligence. The term "data scientist" was not coined until around 2008.
Requirements for careers in data science include increasingly advanced skills that extend beyond data analysis and programming to cover the entire data science life cycle. Many employers prefer job applicants with master's degrees — the constantly evolving field requires professionals who can keep up with new technologies.
What Does a Data Scientist Do?
A data scientist's primary goal is to use data to answer questions, make predictions, and solve problems. Data science professionals collect, clean, and analyze data. They use computer science techniques and tools to create algorithms, find patterns, ask questions, and launch experiments. Data scientists also write reports and deliver presentations based on their findings.
Organizations of all kinds collect increasingly large sets of data and can face difficulty deciphering its meaning or how to use it effectively. Data scientists use their advanced knowledge and skills to help companies make informed business decisions.
A successful career in data science requires excellent problem-solving, analytical, and communication skills. Data scientists need experience with big data analytics, SQL, R, and data mining. The following section explores the most important soft and hard skills for data scientists.
Key Soft Skills for Data Scientists
- Analytics: Understanding and interpreting data are at the heart of data science, so business and data analytics skills are essential to data scientists. Some employers seek skills in machine learning, a branch of analytics that creates performance prediction systems.
- Business Knowledge: Many companies seek data scientists who can interpret data and use it to inform business strategies for improving efficiency, productivity, and sales. Data scientists can specialize in the data science field of business analytics, in which old performance data guides present and future business moves.
- Communication: Most data scientists must communicate their findings, interpretations, and ideas to employers and colleagues. Consequently, clear writing and public speaking skills make data scientists more effective in collaborating with team members and superiors.
- Problem-Solving: Most companies, governments, and nonprofits turn to data scientists for their ability to solve problems with data-driven insights. These professionals can help organizations clarify which aspects of issues can be solved using data sets.
- Organization: Data scientists need strong organizational skills to address projects involving complex parts and data sets. Professionals who understand how to organize large repositories of raw data, for example, may know how to improve company outcomes by sourcing more representative amounts of data.
Key Hard Skills for Data Scientists
- Big Data Analytics: Big data analytics involves the analysis and application of large data sets to help companies understand consumer trends and market patterns. The term "big data" refers to data sets so complex that normal data processing software could not work with them.
- Data Mining/Data Warehouse: Data mining is the process of looking within a large set of data for previously unrecognized patterns or insights. A data warehouse is a system created specifically for data analytics.
- SAS: SAS is a suite of software products created specifically for data management and analysis for business insights. Use and mastery of SAS are foundational skills for data science professionals, helping them create value for their employers by addressing real-world problems.
- SQL: Short for Structured Query Language, this language works specifically with database management. All prospective data scientists should maintain a firm grasp on the latest trends and advances in SQL.
- R: R, a programming language, applies to statistics and graphics. Many computer science professionals consider R to be foundational knowledge.
A Day in the Life of a Data Scientist
Specific data scientist job duties vary by position, employer, and industry. A typical day in the life of a data scientist may include analyzing data, looking for patterns and trends, and developing new algorithms. These professionals may also need to communicate results through written reports or oral presentations to clients.
Data Scientist Salary and Career Outlook
The Harvard Business Review projects high demand for the profession. The data scientist career outlook continues to look very promising and offers high pay.
Data scientists earned an average annual salary of $108,660 in 2021, significantly higher than the national average of $45,760. Factors that might influence salary expectations include experience, level of education, location, and specialization.
Payscale data from May 2022 indicates that even entry-level data scientists with less than one year of experience made around $85,000, on average. Experienced data scientists with 20 years on the job earned an average income of more than $136,000 a year.
Average Annual Salary
How to Become a Data Scientist
A data scientist typically spends at least four full-time years earning a bachelor's degree in data science, computer science, or a related field. Many data science professionals spend an additional two full-time years completing master's degrees.
Some data science professionals complete data science bootcamps to pursue entry-level positions. A bootcamp provides intensive training and education in much less time than a typical college degree program. Data scientists can also earn certifications to increase their job opportunities.
Consult the links below to learn more about how to become a data scientist.
Similar Specializations and Career Paths
Graduates of data science programs and current data scientists can pursue other fields besides data science. A data scientist career or program develops many transferable hard and soft skills that apply to other areas of computer science, including computer programming, data analysis, communication, and problem-solving.
Below, we describe some similar specializations and career paths to consider.
Network architects specialize in the communication networks side of computer engineering. These systems may include local area networks, company-wide networks, and cloud computing. Their daily tasks include designing networks, analyzing data traffic, and upgrading hardware. They also troubleshoot any network and information security issues.
These professionals create and manage systems that store various types of data. Duties include designing new databases, ensuring data security, and updating permissions. Major employers include the computer systems design, education, and insurance industries, but database administrators and architects work in nearly all sectors.
Organizations employ these workers to protect their networks through oversight and implementation of security measures. They work as consultants for computer companies and in business and finance. Duties may include finding potential vulnerabilities, creating security-related reports, and helping computer users understand new security processes.
These analysts look at organizations' computer systems and find ways to improve them. Tasks include finding technologies to make systems more efficient and functional, recommending IT upgrades, and creating instruction manuals. Most work in computer systems design, finance and insurance, and management.
|Career||Required Education||Required Experience||Median Annual Salary||Projected Growth Rate (2020-2030)|
|Network Architects||Bachelor's Degree||5 years or more||$116,780||5%|
|Database Administrators and Architects||Bachelor's Degree||None||$98,860||8%|
|Information Security Analysts||Bachelor's degree||Less than 5 years||$102,600||33%|
|Computer Systems Analysts||Bachelor's degree||None||$99,270||7%|
Source: Bureau of Labor Statistics (BLS)
Resources for Data Scientists
Consult the data science resources in the sections below to find networking opportunities, job boards, and continuing education classes.
Professional Organizations for Data Scientists
Common Data Scientist Questions
What is a data scientist?
A data scientist studies data to help answer questions and solve problems. These professionals collect, clean, manage, and analyze data. They design algorithms, use programming languages, and create data visualizations.
What skills are required to work in data science?
A career in data science requires skills in programming, data mining, and big data analytics. Other useful skills include competency with R, SAS, and SQL. Individuals with strong math, analytical, and problem-solving skills can thrive in the field.
Is data science a difficult career?
It can be. A data scientist career requires advanced technical skills and knowledge of math, statistics, and computer science. Data scientists also must remain up-to-date on new technologies in their field to remain relevant.
How long does it take to become a data scientist?
Most data scientists spend at least four full-time years earning their bachelor's degrees. Many follow that up with master's degrees in data science or other related fields, which usually adds another two full-time years.
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