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Data Science vs Data Analytics: What Are the Similarities & Differences?

The differences between data scientists and data analysts lie in how they work with data, but lead to varying career paths, job titles, and salaries.

An illustration comparing big data with basic data analytics and visualization

Digital tech. The Internet of Things. Automation. COVID-19. Sustainability and the energy transition. These are just a few of the catalysts driving an explosion of data, which can be harnessed to lower costs, improve safety, meet environmental goals and, generally, improve our world. As a result, the demand for data professionals like Data scientists and Data analysts (or professionals with data competencies, like the Hybrid/I4 Engineer), is increasing at a rapid rate. Organizations increasingly rely on data-driven strategies, intelligence and insights to optimize and automate, deliver efficiencies, and achieve competitive advantage.

While data science and data analytics are related disciplines, it’s important to understand the similarities and differences between them. There are two primary differences: first, the size and structure of the data and, second, the processes and tools for managing this data.

Data analysts mainly examine raw, structured data sets to answer questions and find opportunities. Through processes like data cleaning, database querying, and basic Excel analysis, they identify trends and correlations. Data analysts then use visualizations to present their findings to team members and stakeholders and leverage this data to inform business decisions. They can be found in nearly every industry and function.

Data scientists (sometimes referred to as “Big Data analysts”) take a much more complex, layered and computational approach to the end-to-end management of big data. Using advanced statistical models, they write computer programs to clean and organize massive sets of unstructured or structured data for analysis at scale. Data scientists are pioneers in a new world where data is ubiquitous, using the scientific method to explore and invent new methods for sourcing, processing and modeling data. Their innovations are used by businesses, government, nonprofits and other organizations who need help solving big problems.

Which career you want to pursue will determine the path you should take and the required education and/or professional certifications. Data science and data analytics have different educational requirements, career options, salary ranges, and more. This article provides a high-level overview of these differences to help you make an informed decision.

Differences in What Data Scientists and Data Analysts Do With Data

A key difference between data science and data analytics lies in the tasks professionals in these areas perform. A Data analyst cleans and mines raw data to derive the insights needed to solve a specific problem, such as why a product or marketing campaign is underperforming in certain regions. The data they leverage is typically structured, and they use tools like SQL and Excel.

Common data analysis tasks include:

  • Collaborating with business leaders to identify problem areas that data could elucidate
  • Sourcing primary and secondary data
  • Preparing datasets for analysis by cleaning and restructuring them
  • Analyzing data to discover trends and patterns that reveal valuable insights
  • Creating and delivering visual presentations to business leaders to provide actionable guidance and inform strategies

Data scientists play a more technical and innovative role in data utilization. They work with massive unstructured or structured sets of data to extract all useful information and make predictions based on those insights. Data scientists are sometimes called Big Data analysts because they specialize in Big Data, which refers to datasets that are too large for traditional databases and analysis processes. They use programming languages like Python, R or SQL and tools like SAS, BigML, MATLAB, Hadoop, and Tableau, but they also develop their own tools.

Common data science tasks include:

  • Creating custom data frameworks
  • Harvesting, cleaning, and organizing massive unstructured datasets
  • Developing and updating machine learning algorithms and predictive models to mine large data sets
  • Developing automation systems and programs for data collection and processing
  • Creating and compiling data visualization tools and reports

Differences in Education Requirements

Educational requirements also differ for data analysis versus data science careers.

Education Requirements for Data Analysts

For a career in data analysis, a bachelor’s degree in mathematics, statistics, computer science, finance, or a similar field is recommended. However, you can earn online data analysis certifications to land an entry-level position, allowing you to gain work experience while pursuing your degree. It’s less common for Data analysts to have advanced degrees, but it’s precisely because of this that attaining a master’s degree will give you a competitive edge and lead to a higher salary.

Education Requirements for Data Scientists

For data science, you’ll need at least a bachelor’s degree in a related field like statistics, computer science, or mathematics, but advanced degrees are recommended. Most Data scientists have at least a master’s in data science or computer science, mathematics, or statistics, so you’ll need one to match industry expectations and earn a competitive salary. Working as a Data analyst while pursuing your master’s degree can help you create a solid foundation for a data science career.

Differences in Job Title & Salary

Job titles and salaries differ between data analysis and data science positions, reflecting the differences in the scope of their educational requirements and job responsibilities. Data analysts typically collect and study data to suggest strategies for improving an organization’s marketing, sales, or operational efficiency. Data scientists have broader and more diverse roles, developing systems and tools to organize, process, and utilize the Big Data businesses collect. A master’s degree can help data science professionals earn a higher salary and create career advancement opportunities in either field.

Data Analyst & Data Scientist Job Titles & Salaries

DATA ANALYST JOB TITLES APPROX. SALARY*
Senior Data Analyst $96,800
Business Intelligence Analyst $85,600
Financial Analyst $83,600
DATA SCIENTIST JOB TITLES APPROX. SALARY*
Data Engineer $112,400
Data Scientist Manager $162,000
Artificial Intelligence Architect $129,000
*Source: U.S. Bureau of Labor Statistics, Glassdoor, ZipRecruiter

Choosing Between Potential Career Paths of a Data Scientist and Data Analyst

Choosing between a career in data science or data analytics can be challenging, but this decision is important to help you plan your education and career trajectory. The profession that’s right for you will depend on your individual interests, background, educational aspirations, and desired salary.

1. Consider your interests

If you don’t genuinely enjoy what you’re doing, you won’t be happy in either position. Consider each profession’s responsibilities, and ask yourself questions like:

  • Do I want to build data models, machine learning algorithms, and presentation tools from scratch?
  • Would I enjoy pulling all meaningful information from massive datasets and using it to offer insights to inform business decisions?
  • Am I excited at the thought of being an innovator, drawing on my knowledge to inform new approaches to data management and processes?

If you answered “yes” to these questions, data science may be the best fit for you. The next three questions will help you determine if you may be more suited for a career in data analytics:

  • Would I thrive in an environment that blends people, business, and data skills, where I would collaborate with business leaders to identify problems I could help solve?
  • Am I skilled at communicating complex or abstract concepts? Can I explain these concepts in a way that’s digestible and easy to understand?
  • Would I rather focus on data itself, without needing to develop new models, algorithms, codes, and tools?

Asking yourself these questions can help you determine how your personal interests and skills align with each field, and which career path can potentially lead to overall job satisfaction.

2. Consider your desired job and salary

It’s wise to consider the career trajectory, growth, and earning potential of each field, noting that both disciplines are constantly evolving. As a field that’s increasingly in demand, data science offers higher salaries with room for continuous growth and flexibility in areas like artificial intelligence, Big Data processing, and data engineering. Data analysis has a narrower scope, but gaining relevant experience, certifications, and advanced degrees will allow you to move into senior-level roles and earn a competitive salary.

3. Consider your educational background

Your educational and experiential background and your willingness to pursue additional education should help inform your decision to pursue data science or data analytics.

To be a Data Analyst, you may need only a bachelor’s degree or online certifications to get your foot in the door, making it an easier field to jump into with limited experience. For data science, you’ll need experience in mathematics, statistics, or coding, plus an advanced degree, which you could attain while working in data analysis.

The MDS@Rice program offers a world-class faculty and robust curriculum that prepares students for thriving careers in data-focused industries. While it’s tailored for data science, it can also help students advance their careers in data analysis, with a course specialization in business analytics. If you’re interested in becoming a Data Scientist, learn more about the process and the necessary steps to set yourself up for career success.