Careers in Data: Analyst vs. Scientist

January 20, 2023

Updated October 3, 2023

Estimated reading time: 5 minutes.

CAREERS IN DATA: ANALYST VS. SCIENTIST Since long before the invention of the internet, data has been driving business strategy and economic growth across industries. The advent of the internet and the increased ease of collecting and processing significant amounts of data has made it even easier. This increased integration of data-driven approaches constantly creates new career opportunities for data-savvy experts.

Regardless of industry, data science professionals generally follow two career paths. After completing your education, you can pursue a career as a data analyst or a data scientist. While these might often be referred to with other job titles, such as business analyst and data engineer, the data field is generally broadly divided along these career paths.

Though these roles overlap in several ways, there are a few key areas in which they differ. Knowing what kind of position is right for you is crucial when setting your sights on your career goals. Read on to understand the key differences between these two roles.

Data Analyst vs Data Scientist: Key Differences

Though both these professionals analyze data to acquire insights and ultimately understand reality, their angles toward their work are slightly different. Data analysts can be described as using data to better understand the past. In contrast, data scientists use data to make assumptions about the future. Data analysts examine data sets to draw insights about things that have happened and present coherent stories through visualizations. Data scientists use raw data, statistics, and deep learning to create predictions and analyze opportunities.

Data Analysts	Data Scientists
Utilize data science skills to become experts in the performance of specific businesses and departments.	Use data expertise to create guiding insights for businesses based on trends and patterns.
Usually assigned to a single team or department, like Sales, Marketing, or Customer Experience.	Work across multiple departments or in dedicated data science teams with individual focus areas, like Applied Machine Learning, Marketing Optimization, and Churn Prevention.
Implement basic scripts and pipeline code but typically are not expected to develop software.	Typically report to a C-suite executive or senior data scientist.
	Develop tools or software to serve predictions, analytics, or insights for internal or customer-facing use.

Interested in becoming a Data Analyst? Read our feature: HOW TO BECOME A DATA ANALYST.

Data Analyst vs Data Scientist: Skills

While there is some overlap, data scientists tend to have more specialized and deeper skills than data analysts. However, both need to master programming languages and become statistical wizards.

Data Analyst	Data Scientist
Are proficient in using tools like SQL, Excel, or Python libraries (e.g., Pandas) to clean, transform, and prepare data for analysis.	Need strong programming skills, often in languages like Python or R, to build and deploy machine learning models and conduct in-depth data analysis.
Need strong data visualization skills using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib and Seaborn) to create meaningful charts and graphs.	Create predictive models, perform feature engineering, and optimize model performance.
Basic statistical knowledge is crucial for data analysts. They should be comfortable with concepts like mean, median, standard deviation, and hypothesis testing.	Require a solid foundation in advanced statistics and probability theory for model building and evaluation.
Need to understand the business context and be able to translate data insights into actionable recommendations for business stakeholders.	Need skills in data engineering to extract, transform, and load (ETL) data efficiently. Knowledge of tools like Apache Spark or Hadoop can be valuable.
Create regular reports and dashboards to track key performance indicators (KPIs) and provide insights to decision-makers.	They should have a deep understanding of machine learning algorithms, including supervised and unsupervised learning, classification, regression, and clustering.
Cleaning and preprocessing data are essential to deal with missing values, outliers, and inconsistent data.	Depending on the industry they work in, data scientists may need specific domain knowledge to apply their data science skills effectively.
Need strong communication skills to convey their findings effectively to non-technical stakeholders.	Like data analysts, data scientists need to communicate their findings, but they may also need to explain complex models and their implications to both technical and non-technical stakeholders.
	Conduct experiments and A/B tests to evaluate the impact of changes or interventions.

Data Analyst vs Data Scientist: Qualifications

Data analysts and scientists alike will need to have some sort of educational background. On top of a bachelor’s degree or a bootcamp certification, data scientists will often hold a master’s or a doctoral degree if they’re interested in research.

Data Analyst	Data Scientist
Bachelor's Degree: Many data analyst positions require at least a bachelor's degree. A degree in a quantitative field such as mathematics, statistics, economics, computer science, engineering, or a related field is often preferred.	Bachelor's Degree: A bachelor's degree in a quantitative field like computer science, mathematics, statistics, engineering, or a related field is often a minimum requirement.
Master's Degree (Optional): While not always necessary, having a master's degree, particularly in fields like data analytics or business analytics, can make you a more competitive candidate for certain roles.	Master's or Ph.D. (Preferred): Many data scientist roles, especially those in research or more advanced positions, prefer candidates with a master's or Ph.D. in fields such as data science, machine learning, artificial intelligence, or a closely related area.
Microsoft Excel Certification: Demonstrating proficiency in Microsoft Excel can be beneficial as it's a commonly used tool in data analysis.	Machine Learning Certifications: Certifications from organizations like Coursera, edX, or the Google Machine Learning Certification can help you demonstrate your proficiency in machine learning.
SQL Certification: Earning a certification in SQL can showcase your ability to work with databases, which is a fundamental skill for data analysts.	Data Science Certifications: There are various data science certifications available, such as the Data Science Certification Institute (DASCA) certifications or the Data Science Council of America (DASCA) certifications, which can enhance your credibility as a data scientist.
Tableau Certification: If you plan to work with data visualization tools like Tableau, obtaining a Tableau certification can be advantageous.	Cloud Platform Certifications: Familiarity with cloud platforms like AWS, Azure, or Google Cloud can be valuable, and earning certifications related to these platforms can be a plus.
Data Analytics Certifications: Several organizations offer data analytics certifications, which can help you demonstrate your skills in data manipulation, analysis, and visualization.	Deep Learning Specializations: If you plan to work extensively in deep learning and neural networks, consider certifications or courses from organizations like Deeplearning.ai or NVIDIA.
Certified Analytics Professional (CAP): The CAP certification from INFORMS is a respected credential for analytics professionals and can be beneficial for data analysts.	Data Engineering Certifications (Optional): If your data science role involves significant data engineering responsibilities, certifications in related technologies (e.g., Apache Spark) can be useful.
	Domain-specific Certifications: Depending on the industry you work in, domain-specific certifications (e.g., healthcare, finance) may be beneficial for understanding industry-specific challenges and regulations.

Data Analyst vs Data Scientist: Where They Work

The typical day-to-day of these professionals can differ in many ways, from the types of industries they tend to work in, to how their careers progress over time.

Data Analyst	Data Scientist
They are typically found in industries that collect and maintain large amounts of data, like SaaS, healthcare, retail, and government.	Tend to be found in engineering or software companies pivoting to data-centric products and services.
They usually work within medium to large enterprises with established or up-and-coming data departments.	They usually have higher data skills and expertise, resulting in more specialized roles and higher salary expectations.
Continue to develop their statistics, machine learning, and software development skills to advance to a data scientist role.	High-tech startups are beginning to hire data scientists in dual roles of Data Analyst and AI/machine learning technology developer.

Data Analyst vs Data Scientist: Salaries

Both data analysts and data scientists can expect to earn high salaries. Earnings will vary depending on seniority, sector, educational background. However, due to its more specialized and complicated nature, those in data science can expect to earn more.

Average Salary for Data Analysts According to:

Glassdoor: $70,288

Indeed: $67,229

Talent: $91,083

Average Salary for SeniorData Analysts According to:

Glassdoor: $96,132

Indeed: $87,150

Talent: $135,161

Average Salary for Data Scientists According to:

Glassdoor: $102,167

Indeed: $89,388

Talent: $136,351