Are you considering a career in data science or statistics but unsure which path to take? As data-driven decision-making takes center stage in today’s job market, the lines between these two professions often blur, leaving aspiring professionals uncertain about which path to follow.
While data scientists and statisticians play crucial roles in harnessing the power of data, their approaches and skill sets can differ significantly. This blog will break down the key differences between data scientists and statisticians to help you confidently navigate your career decision.
Data Scientist vs. Statistician: The Difference in Profession
Understanding the distinction between a data scientist and statistician is crucial for anyone considering a career in data analysis. Let’s take a look below at the differences between a data scientist vs. a statistician when it comes to their work and responsibilities:
What Is a Data Scientist?
A data scientist is a skilled professional who leverages both structured and unstructured data to address complex business challenges. They use a combination of advanced analytics, coding, mathematics, machine learning and programming to uncover patterns, predict trends and generate actionable recommendations.
Companies across industries rely on data scientists to harness data-driven findings, transforming them into actionable insights that enhance business strategies, optimize operations and identify new opportunities. The rise of artificial intelligence and big data technologies has only amplified the importance of data scientists, making them essential to staying competitive in a rapidly evolving market.
Their responsibilities typically include:
- Understanding business problems: Data scientists begin by deeply understanding the business challenges or objectives they need to address, ensuring their work aligns with organizational goals.
- Data collection and preparation: They collect, clean and prepare large datasets from various sources, ensuring the data is suitable for analysis and modeling.
- Exploratory data analysis: Data scientists conduct exploratory data analysis (EDA) to identify trends, patterns, and anomalies within the data, helping to inform subsequent modeling efforts.
- Model development: They create and refine statistical and machine learning models to analyze data, using techniques tailored to the specific problems being addressed.
- Data visualization: Data scientists develop visual representations of data and model outputs to communicate findings effectively to both technical and non-technical stakeholders.
- Deploying models: Once the models are developed, data scientists deploy them into production environments, enabling real-time data processing and analysis.
Interpreting results: They analyze the results generated by their models, translating complex data insights into actionable recommendations for business strategy.
What Is a Statistician?
A statistician is a highly trained professional specializing in collecting, analyzing, interpreting and presenting quantitative data. Statisticians transform complex datasets into meaningful information that informs decision-making across various fields, including business, healthcare, government and academia.
Their expertise ensures that conclusions drawn from data are valid, reliable and applicable. Statisticians help organizations navigate uncertainty by providing the tools to understand trends, test hypotheses and evaluate risks.
Their responsibilities typically include:
- Data collection: They design and conduct surveys, experiments or observational studies to gather relevant data, which involves selecting appropriate sampling methods and ensuring data quality.
- Data analysis: They utilize statistical methods and software to analyze complex datasets, applying techniques such as regression analysis, hypothesis testing and variance analysis to draw meaningful insights.
- Statistical modeling: Statisticians develop mathematical models to represent data relationships and predict future trends, including creating simulations and forecasts based on historical data.
- Interpretation of results: They communicate findings clearly to non-technical audiences through reports, presentations or visualizations, explaining the implications of their analyses and providing recommendations based on the results.
- Quality assurance: They implement quality control measures to ensure the accuracy and reliability of data and analysis, which may involve refining data collection processes and validating results.
Data Scientist vs. Statistician: Educational Requirements
Determining whether to embark on the journey of a data scientist or a statistician involves more than just a preference for numbers; it requires understanding the specific educational backgrounds that shape these careers. Let’s take a look at the educational requirements for a data scientist vs. a statistician below:
How to Become a Data Scientist
Here are the essential education requirements you need to know to become a successful data scientist:
- Obtain a bachelor’s degree: A bachelor’s degree is the minimum requirement for most data scientist positions. The common data science degrees include computer science, statistics, mathematics, data science, engineering and information technology.
- Gain practical experience: Gaining hands-on experience through internships, boot camps, research projects or entry-level positions in related fields allows you to apply your theoretical knowledge to real-world problems and build a portfolio showcasing your skills.
- Get certifications or take online courses: Many aspiring data scientists enhance their qualifications through certifications from reputable institutions or online platforms. For government-related certifications, you can take up the AWS Certified Data Analytics Specialty. For required certifications, the Certified Analytics Professional (CAP) and SAS Certified Data Scientist are essential for showcasing your ability to transform data into actionable insights. You can also check out industry-specific certifications like the Cloudera Certified Associate (CCA) Data Analyst and IBM Data Science Professional Certificate to validate your skills in specific platforms and tools.
- Consider an advanced degree: While a bachelor’s degree can open doors to entry-level positions, many employers prefer or require candidates to hold an advanced degree, such as a master’s in data science, computer science, statistics, applied mathematics or a related degree. An advanced degree equips you with deeper expertise in complex data analysis, machine learning and advanced statistical techniques, making you more competitive in the job market.
- Consider a doctoral degree (Ph.D.): While not a requirement for all data scientist roles, some professionals choose to pursue a Ph.D., especially those aiming for academic, research or highly specialized positions. A doctoral degree focuses on advanced research methodologies, enabling graduates to contribute to innovative projects and cutting-edge studies in data science.
How to Become a Statistician
Here are the key educational requirements you should be aware of to embark on a successful career as a statistician:
- Obtain a bachelor’s degree: A bachelor’s degree is generally the minimum requirement for entry-level positions in statistics. Relevant fields of study include statistics, mathematics, applied mathematics, economics, engineering and computer science.
- Gain practical experience: Start exploring internships or look for opportunities to work in part- or full-time statistics-related roles where you can apply your analytical skills and broaden your expertise.
- Obtain accreditations and certifications: While statisticians are not required to have a professional certificate, the American Statistical Association (ASA) provides two valuable accreditations: the Accredited Professional Statistician (PStat) and Graduate Statistician (GStat). Including a certification on your resume can enhance your appeal to employers and may increase your chances of securing a job.
- Consider an advanced degree: For more advanced positions, a master’s degree is often preferred or required. Graduate degrees in statistics, applied statistics, data science and biostatistics can significantly enhance your qualifications.
- Consider a doctoral degree (Ph.D.): For those aspiring to senior-level positions, research roles or academic careers, a Ph.D. in statistics or a related field may be required.
Data Scientist vs. Statistician: Knowledge and Skills
To make an informed decision about your future as a data-driven professional, it’s essential to delve deeper into the skillset each profession entails. Here’s a closer look at the knowledge and skills of a data scientist vs. a statistician:
What Are the Skills of a Data Scientist?
Data scientists require a diverse set of skills to effectively analyze data, draw insights and make informed decisions. Here are the key skills essential for success in this field:
- Programming (Python, R, SQL, etc.)
- Statistics
- Mathematics
- Experimental design
- Machine learning and artificial intelligence
- Natural language processing
- Data wrangling
- Extract, transform and load
- Data visualization tools
- Big data technologies
- Data engineering
- Cloud platforms
- Soft skills (communication, working collaboratively, problem-solving)
- Ethical considerations
- Knowledge of data privacy laws and regulations
What Are the Skills of a Statistician?
To excel in this field, you must possess a diverse range of skills that enable you to draw meaningful conclusions and communicate insights effectively. Here’s a look at the essential skills required for success as a statistician:
- Strong foundation in math and statistics
- Data collection and management
- Proficiency in statistical software programs (R, SAS, SPSS, STATA)
- Programming skills
- Data visualization
- Applied statistical methods
- Good communication and presentation skills
- Soft skills (critical thinking, attention to detail, problem-solving)
Tools Required for Data Science vs. Statistics
The fields of data science and statistics overlap significantly, yet they utilize different tools tailored to their specific needs and objectives.
What Are the Common Tools Used in Data Science?
What Are the Common Tools Used in Statistics?
- Statistical software (R, SAS, SPSS)
- Data analysis tools
- Data visualization tools (ggplot2)
- Survey and experimental design tools (Qualtrics, SurveyMonkey)
- Specialized statistical packages
Data Scientist vs. Statistician: Salary Comparisons
When evaluating career options in the data field, salary is a significant factor to consider. Both data scientists and statisticians are in high demand, but their salaries can vary. However, it’s important to note that several factors include industry, experience, education and geographic location.
What Is the Average Salary for Data Scientists?
Please note that salary information may fluctuate over time and can be influenced by factors such as the employer, specific job responsibilities, required education and experience, and geographic location.
According to U.S. News & World Report, the median salary for a data scientist is around $108,020, with salaries ranging from $79,810 to $147,670. Moreover, Indeed reports an average base salary of $127,618, while the U.S. Bureau of Labor Statistics estimates the average salary to be approximately $112,590.
This competitive salary reflects the high demand for skilled data scientists in various industries, making it an attractive career choice for those interested in leveraging data to drive business decisions and innovation. Based on the U.S. News & World Report’s list of Best Technology Jobs, data scientists rank #4 out of 10.
What Is the Average Salary for Statisticians?
According to U.S. News & World Report, the median salary for a statistician is approximately $104,110 with a range between $78,140 and $134,950. The U.S. Bureau of Labor Statistics reports a median pay of $104,350.
While these figures are competitive, it’s worth noting that data scientists tend to earn higher salaries, reflecting the growing demand for data science expertise in various industries. Nonetheless, a career in statistics remains a rewarding choice for those passionate about data analysis and its applications in decision-making.
Companies Currently Hiring
If you are looking to start a career in data science or statistics, now is an excellent time to explore the job market. Here’s a look at some of the leading companies currently hiring data scientists and statisticians:
For data scientists:
- Grammarly
- Microsoft
- Netflix
- Charlotte Hornets
- Samsung
- NASA
- Visa
- Etsy
- WEX
- DoorDash
- Spotify
For statisticians:
- IRS
- U.S. Department of Justice
- The Johns Hopkins University Applied Physics Laboratory
- U.S. Department of Commerce
- Bank of America
- Fidelity Investments
Which Career is Right for You?
If you have a passion for analyzing large datasets and extracting meaningful insights, a career as a data scientist may suit you well. Data scientists frequently engage with machine learning and artificial intelligence, making it ideal for those who enjoy innovative technologies and building predictive models. This role also requires a willingness to learn new technologies and adapt to fast-paced environments, making it suitable for those who thrive in dynamic settings.
On the other hand, if you have a strong foundation in statistical theory and enjoy structured, research-focused settings, becoming a statistician might be more fitting. Statisticians excel at designing experiments, collecting data and applying statistical techniques to analyze results, often using statistical software in their work. This career path is well-suited for individuals who appreciate traditional methodologies and a more methodical approach to problem-solving.
Ultimately, consider your interests and strengths as you evaluate these two paths. Whether you lean toward the technological innovation of data science or the foundational analysis of statistics, both careers offer rewarding opportunities to make an impact through data.
Launch Your Data Career at the University of San Diego
Launching a successful data career requires not just a willingness to learn but also a strategic approach to the skills and tools needed in the field. As high-paying career opportunities in data science and statistics continue to grow across various industries, the right education and practical experiences can give you a competitive advantage in the job market.
To help you make informed decisions about your educational journey, download the free eBook, 7 Questions to Ask Before Selecting an Applied Data Science Master’s Degree Program. This essential resource will guide you through key considerations, ensuring you choose the right path to achieve your career goals. For more information, please reach out to an enrollment advisor.