Do you enjoy working with numbers and raw data? Interested in using your understanding of complex data sets to drive organizational decision-making? If so, either data science or data analysis could be paths worth exploring.
What is the difference between data science and data analysis, though? Below, we outline the ways in which these two fields are distinct.
Key Takeaways
- Data analysts and data scientists both work with data, but they often focus on different outcomes: analysts explain past trends, while data scientists build models that help predict future outcomes.
- Data analysts commonly collect, clean, organize, analyze, and visualize data so stakeholders can better understand business performance.
- Data scientists often work with larger or more complex data sets, machine learning, algorithms, and predictive analytics to solve business problems.
- Both roles benefit from strong communication skills because data professionals must translate technical findings into insights that non-technical teams can use.
- The right path may depend on whether you are more interested in reporting and business insights or modeling, automation, and predictive analytics.
Why People Compare Data Science vs. Data Analyst Roles
It is a relatively common misconception that data science and data analysis are synonymous. Despite some overlapping tools and skills, there are noteworthy differences.
The Growing Prevalence of Data-Driven Decision-Making
One key reason people tend to compare data science and data analyst roles in today’s market is that both are relevant amid the growing prevalence of data-driven decision-making. Perhaps more than ever, organizations are leveraging insights from data to make informed decisions that align with business goals. Thus, professionals who understand how to use and interpret data are valuable.
How the Two Roles Complement Each Other in Organizations
It is also worth noting that many organizations rely on both data science and data analysis to drive decision-making, allowing these two fields to complement each other meaningfully. For example, whereas data analysts may look to historical data to explain past trends, data scientists may use these same findings to build algorithms that forecast future events.
Data Analyst vs. Data Scientist
Both roles work with data, but they often use it for different business purposes.
Data Analyst
Explains what happened
Data Scientist
Predicts what may happen
What Does a Data Analyst Do?
In simplest terms, data analysts are professionals who collect, prepare, and analyze data sets in order to identify trends and extract meaningful insights from raw data.
Collecting, Cleaning, and Organizing Data
One core responsibility of a data analyst is to collect, clean, and organize data in preparation for analysis. This may entail processing and fixing any issues or inconsistencies with data, as well as mining data from appropriate sources and maintaining it in a database.
Creating Reports and Visualizations for Stakeholders
Data analysts are often responsible for not only for analyzing and interpreting data but also helping non-technical stakeholders understand it. This may involve generating reports and other data visualizations to make insights more accessible to stakeholders, including organizational decision-makers.
Communicating Insights to Non-Technical Teams
Because decision-makers in business are often not well-versed in data analysis themselves, they rely on data analysts to communicate insights and recommendations to them in a way that is clearly understood. This calls for strong written and verbal communication skills, as data analysts in these roles must serve as “translators” of sorts.
What Does a Data Scientist Do?
A data scientist, on the other hand, focuses more on the use of data to automate systems and predict future trends. Rather than looking to explain the “why” of historical data, these professionals are more interested in how data may help anticipate what might happen in the future.
Developing Predictive Models and Algorithms
A major aspect of a data scientist’s job, then, is building predictive models and algorithms using machine learning (ML). These algorithms are specifically designed to predict future outcomes based on historical data, which may further inform business decision-making.
Working With Large or Complex Data Sets
Compared to data analysts, data scientists often work with even larger and more complex data sets. This requires data scientists to use specialized tools to properly sort and store large amounts of data in preparation for analysis, alongside a solid grasp of exploratory data analysis (EDA) to pinpoint patterns and trends.
Translating Business Problems Into Data Solutions
In addition to extracting insights from data, data scientists may be called upon to solve complex business problems using machine learning, modeling, and statistical analysis. From risk management challenges to operational efficiency issues, they apply tried-and-true techniques to make predictions and recommendations for long-term business strategy.
Key Differences in Daily Responsibilities
Although both data scientist and data analyst responsibilities entail working with vast volumes of raw data on a regular basis, the day-to-day tasks of these roles often differ significantly in terms of overall scope and complexity. In general, data scientists are focused on predicting what might happen in the future — whereas data analysts’ responsibilities are more centered around explaining past performance.
Tools and Technologies Used in Each Role
While there exists some overlap between the tools and technologies for data analysis vs. data science, each role also involves using specific tools and platforms.
Where the Toolsets Overlap
Some tools are common across both paths, while others are more closely tied to reporting or predictive modeling.
Data Analyst Tools
- SQL and MySQL
- Tableau and BI platforms
- Data cleaning tools
- Reports and dashboards
Shared Tools & Skills
- Python and R
- Data visualization
- Statistical analysis
- Stakeholder communication
Data Scientist Tools
- Machine learning tools
- AI and NLP platforms
- Apache Spark
- Cloud tools such as AWS
Tools Commonly Used by Data Analysts
Because data analysts work more with historical data, they are more likely to use tools such as:
- Database management platforms (like SQL and MySQL)
- Data visualization tools (like Tableau and BI)
- Programming languages (like Python and R) for statistical modeling and data cleaning
Programming Languages and Frameworks Used by Data Scientists
Meanwhile, data scientists are more likely to rely on the above tools along with:
- Machine learning and artificial intelligence (AI) tools, including natural language processing (NLP) platforms and AI modeling key libraries
- Big data and cloud-based tools, like Apache Spark and AWS, for processing large amounts of raw data
Data Visualization and Reporting Platforms
Because both data scientists and data analysts are responsible for communicating their findings to non-technical stakeholders, it is also common for those in both professions to use data visualization tools and reporting platforms. These are a great way to better understand and visualize complex data — in turn making it easier to identify trends and helping to “translate” information to non-technical audiences.
Data Science vs. Data Analyst: Skills Comparison
So, how do skills stack up when comparing data scientists and data analysts?
Analytical and Statistical Skills
Both data scientists and data analysts need to possess strong analytical abilities and an understanding of basic probability/statistics. However, these skills are arguably more important in a data analyst role, especially when it comes to making sense of complex historical data.
Programming and Machine Learning Knowledge
In addition, both roles require some basic knowledge of programming (most often Python and R), but data science roles do require a more extensive understanding of programming as well as machine learning techniques and their applications. After all, a central aspect of a data scientist’s work involves creating, testing, and implementing ML algorithms to predict future outcomes.
Communication and Business Understanding
Each role also benefits from excellent written and verbal communication skills, namely when translating highly technical findings to a non-technical audience of organizational decision-makers and other stakeholders. Likewise, because data analysts and data scientists help businesses make informed decisions based on data insights, basic business acumen may go a long way in these roles, too.
Educational Backgrounds and Degree Paths
Looking to break into data science or data analysis as a potential career? Both roles typically require a minimum of an undergraduate degree, although the specific field of study may vary depending on whether you are interested in becoming a data analyst or a data scientist.
Typical Degrees for Data Analysts
Common degrees for aspiring data analysts to pursue include bachelor’s degrees in:
- Data analytics
- Computer science
- Mathematics or statistics
- Business analytics
How an MS in Data Science Supports Data Science Roles
For those who already have an undergraduate degree and want to take their formal education a step further, a master of science in data science (MSDS) could be a practical choice. This type of degree program could support a transition from data analysis into data science roles, with coursework that covers such relevant topics as:
- Data engineering
- Natural language processing
- Machine learning
- Python, R, and SQL programming
Which Career Path Might Be Right for You?
As you weigh your options for working with data in a data science or analysis capacity, keep in mind a couple of key considerations.
Which Data Career Path Might Fit You?
Your preferred type of work can help you decide whether data analysis or data science feels like the better direction.
Choose Data Analysis If...
- You enjoy working with historical data.
- You like finding patterns in business performance.
- You want to build reports, dashboards, and visualizations.
- You enjoy translating data into clear recommendations.
- You prefer business insights and stakeholder communication.
Choose Data Science If...
- You are interested in predictive analytics.
- You want to build models, algorithms, and automated systems.
- You enjoy machine learning and AI-related tools.
- You want to work with large or complex data sets.
- You like using data to solve long-term business problems.
Preference for Reporting and Business Insights
If you are more interested in working with historical data to help businesses gain valuable insights that inform decision-making, then a path in data analysis may be right for you. In this type of role, you are more likely to work with existing data to better explain why certain trends have occurred — which may be ideal for those who prefer data reporting, analysis, and insight.
Interest in Modeling, Algorithms, and Predictive Analytics
On the other hand, if you are more compelled to leverage data to make predictions about future trends and behaviors, a career in data science may be more suitable. This is especially true if you have an interest in machine learning, artificial intelligence, and predictive analytics.
Understanding the Distinction Between Data Roles
The data science and data analysis realms both necessitate an extensive understanding of how to collect, prepare, and analyze data in order to make sense of it. No matter which discipline is most appealing, Post University offers undergraduate and graduate programs that align with your goals — including our Bachelor of Science in Data Science and our Master of Science in Business Intelligence and Data Analytics. Request more information about any of our degree programs or get started with your application today.
Thank you for reading! The purpose of this blog is to provide general information to the reader, and as such, this information may not directly relate to programs offered by Post University.
Please note jobs and/or career outcomes highlighted in this blog do not reflect jobs or career outcomes expected from enrolling in or graduating from any Post program.