The digital transformation of our world. Automation. The Internet of Things and rise of smart devices. Sustainability and the energy transition. These are just a few of the catalysts driving an explosion of data, which can be harnessed to drive growth and profitability, find efficiencies, generate better insights, improve safety, and, generally, improve our world. As a result, the demand for data professionals working in Data science and Data analytics is increasing at a rapid rate. Organizations increasingly rely on data-driven strategies, intelligence and insights to optimize, automate, 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 and analyzing this data.
Data science differs from data analytics in that it uses computer science skills (e.g., Python programming) and focuses on large and complex data repositories, where “complex” may refer to the modality of the data (images, time series, text, as well as traditional tabular data) or other facets of the data in question (data can be complex because they are geographically distributed, "unclean" or "unstructured", characterized by extensive missing or inaccurate values).
Although occasionally used as an umbrella term for all aspects of data analysis (including data science), in a practical sense, traditional data analytics tends to focus on "simpler" and more straightforward data processes. For example, a data analytics team may work on the extraction of structured data from a database or repository, cleaning it, analyzing it with excel, and visualizing it in reports using Tableau, Power BI, Google Data Studio or other reporting tools.
Often, data analytics is a career stepping-stone to data science, but it doesn't have to be. Consider your current skills, needs and demands within your organization, and/or which discipline you're most interested in to help visualize your career path. Once you have a sense of your career aspirations, consider the required education and/or professional certifications for that path. Data science and data analytics often 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 vs. Data Analysts Do With Data
A key difference between data science and data analytics lies in the tasks each set of professionals does. 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 use more technical and computational tools to conduct data analysis at scale, often with large, "unclean" data sets. They work with massive unstructured or structured sets of data to extract useful information, explore, draw inferences, 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 statistical machine learning models and other techniques.
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 Data Science and Data Analytics Education Requirements
Educational requirements also differ for data analysis versus data science careers.
Education Requirements for Data Analysis
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 with a specialization in analytics will give you a competitive edge and lead to a higher salary.
Education Requirements for Data Science
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, oftentimes earning a PhD. If you are currently an analyst interested in breaking into data science, working while pursuing your master’s degree can help pave the path to a data science career.
U.S. labor market demand trends forecast growth for both Data scientists and Data/Business Analytics professionals between 2022-2032. That's why, at Rice, we offer a versatile online Master of Data Science, with the ability to specialize in one of two areas: 1) Business Analytics, for those looking to lean into the generalist area of data analytics and data science methods for organizational and societal impact or 2) Machine Learning, for individuals looking to focus on the software engineering and data infrastructure aspects.
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 extract and analyze data to suggest strategies for improving an organization’s operational efficiency, finances, marketing effectiveness, and more. Data scientists take data analysis to the next level, developing statistical models and other techniques to acquire, explore, analyze, structure, and process the proprietary data assets of an organization.
A master’s degree can help data science and analytics professionals learn the emerging skills they need to succeed long-term.
Data Analytics vs. Data Science 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 | |||
Differences in Data Science vs. Data Analytics Skills
Data scientists and Data analysts (or adjacent roles, like Business analysts) each have distinct skill sets; however there are certain skills that overlap across both professions, such as data analysis skills, SQL database skills, data visualization skills, a general curiosity required to develop hypotheses and derive insights from data, and the ability to simplify complex technical concepts into actionable insights.
Data Analytics Skills
Data analytics requires the ability to analyze and derive insights using tools like SQL to help an organization or business meet its goals. Skills vary, but the core skills include:
- Excel and Microsoft Office
- SQL
- Data visualization, reporting and dashboard tools like Tableau, Power BI or Google Data Studio
- Ability to communicate complex technical concepts to broad audiences and key stakeholders
- Domain knowledge of a particular function or industry (for example, supply chain or retail)
- Understanding of data governance requirements, security and privacy regulation
Increasingly, we're seeing employers ask for data science skills like Python or R programming within a Data Analyst or BI Analyst job description, suggesting that the lines between these positions are blurring as more practitioners learn advanced data management skills.
Data Science Skills
Data Scientists move beyond Excel to perform advanced data analysis on larger, less structured data sets using computational tools like Python programming and machine learning algorithms. Data scientists "10x" the traditional data analysis process with the most cutting-edge tools and techniques, as well as a reliance on the methodical, repeatable scientific method.
The core skills of a Data scientist typically include:
- Data analysis using the scientific method
- Computer science concepts, like ML algorithms
- Python or R programming
- SQL
- Understanding of how to work with and engineer big data
- Agile project management methodology
- Ability to communicate complex technical concepts to broad audiences and key stakeholders
- Domain knowledge of a particular function or industry (for example, supply chain or retail)
- Advanced understanding of data governance requirements, security and privacy regulation
Rice's online Master of Data Science program features a Business Analytics specialization, designed for working professionals looking to make the transition from traditional data analyst, business analytics and business intelligence (BI) roles to the advanced skillset of an in-demand Data scientist.
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 data science and data analytics are constantly evolving. As a complex and cutting-edge 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. For many, data analytics is the career aspiration, but it can also provide career flexibility with a path into data science and other roles over time.
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, though data roles that require computer science or more technical skills may be a stretch opportunity. 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 online 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 to the data science discipline, it can also help students advance their careers in data analysis by allowing students to specialize 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.