Data Science vs. Data Analytics: What’s the Difference?
What are the differences between data science vs. data analytics? The truth is there are no hard and fast rules. Different companies and industries use the terms in different ways. But there are a few common distinctions between data science and data analytics that we can identify.
If you’re deciding between the two for a career path, let’s help you understand the differences between a data scientist and a data analyst — and whether the two are even mutually exclusive at all.
Data analysts answer known questions, while data scientists find the right questions
Generally, a data analyst (sometimes labeled a “business analyst”) has a very specific task at hand. Examples of data analytics tasks include:
- Discovering what might have caused a sudden drop in website traffic
- Analyzing which ads were most effective in different places
- Identifying which new products are trending in different markets
- Helping with budgeting by identifying trends in a company’s revenue and costs
- Performing risk management by identifying the likelihood of adverse events and the costs they could incur
Last Updated September 2022
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Data analysts typically have a concrete problem to solve and tease out actionable insights from past data to solve them.
Data scientists work in a more exploratory fashion. They might gather and structure data that isn’t even in your data warehouse yet, and they will build new data models to glean insights that nobody expected. Data science might discover unexpected relationships that lead to new questions nobody thought to ask before. A data scientist could find that some illnesses aren’t really caused by what people originally thought. They might also discover that your customers can be classified into a few different groups, each of which responds to specific kinds of marketing or product recommendations in different ways. Data science allows us to question our assumptions and discover new problems and opportunities by modeling data wherever it may be found.
This distinction is not cast in stone, however. “Exploratory data analysis” is a thing, but typically a data analyst is focused on maintaining metrics and dashboards that have already been identified and created. And a data scientist will be expected to perform data analysis as well.
Data analysts study historical data. Data scientists find new data
In general, the field of data analytics focuses on teasing out insights from past structured data. A data analyst works heavily with databases queried by Structured Query Language (SQL) commands to extract data, visualize, and analyze it. Generally, these are key business metrics that have been identified and collected for some time already.
Data science casts a broader net. A data scientist might gather data from massive distributed sources, such as raw server logs spread across hundreds of servers, sensors spread across hundreds of locations, or even data stored across millions of phones or web browsers. A data scientist can gather this data together, apply structure to it, extract the information needed, and build models to understand the data. Rather than using relational databases exclusively, data scientists might employ distributed “big data” tools such as “NoSQL” databases, Hadoop, Spark, and the heavy use of cloud computing to wrangle large, dispersed, and unstructured data sources.
Again, there is some inconsistency across the industry in this distinction. In the world of Amazon Web Services, for example, the discipline of collecting and processing unstructured big data is called data analytics, not data science — although that work could be more specifically categorized as “data engineering.” Ultimately, you should rely on job descriptions to understand a specific data-related role and not just the job title. The titles “data analyst” and “data scientist” can be ambiguous.
Top courses in Data Science
It is sometimes said that data analytics focuses on historical data, while data science focuses on making predictions for future data. Because a data scientist may employ machine learning algorithms, they have more sophisticated tools for making predictions at their disposal. However, a data analyst can certainly apply regression techniques (for example, fitting a line to a graph) to identify trends based on past data and make projections for planning purposes. A data analyst might use the term “predictive analytics” for this forward-looking analysis, while a data scientist would use the term “predictive model” to imply a more complex understanding of what drives their predictions.
Data analysts use higher-level tools, data scientists (mostly) write code
The field of data analysis typically uses SQL to interact with large databases and business intelligence tools such as PowerBI, Excel, Tableau, Looker, SiSense, SAP, or countless other offerings. These tools make it easier to query and visualize data, as well as to quickly discover trends and correlations within it to assist in making the best business decisions.
Data science, on the other hand, employs a wider variety of more complex tools. Clustering, dimensionality reduction, and machine learning algorithms might be applied to the data; generally, this involves writing Python code. The data at hand may also be raw and unprocessed, and further code will be necessary to prepare, structure, and clean the data prior to modeling it.
Again, this is not a hard-and-fast rule. Data analysts are generally expected to have some understanding of programming languages such as Python and R, and you could argue that SQL is also code. And while the world of data science makes heavier use of Python, people who call themselves “data scientists” tend to stay more on the theory side than on the side of building systems to automatically process their data. A data scientist who ventures more into coding for scalable, production-grade systems might cross the line into becoming a machine learning engineer instead.
Data scientists make more money
You’ve probably gathered that data science involves the ability to bring together many complex tools and algorithms to novel sources of data in order to discover new insights. This requires more experience and the ability to work with less explicit direction and guidance, not to mention an understanding of computer sciences. These skills come at a premium.
According to Indeed.com, the average base salary of a data scientist in the US (as of September 2021) is US$119,277, while a data analyst commands US$67,076. You don’t need to be a data scientist to see that’s a substantial difference. When it comes to compensation, considering a career in data science vs. data analytics is no contest. It simply takes less education and experience to analyze existing data than it does to build complex models to predict future data. The salaries in data analytics reflect this. Entry-level data analysts do exist, but jumping right into data science generally involves a master’s degree in lieu of experience.
Data science vs. data analytics: it’s not either/or
As we’ve pointed out, the line between these two fields can be fuzzy. Both data analytics and data science can glean insights from data and make predictions from it. Increasingly, the tools used for data analytics are incorporating machine learning algorithms previously open only to data science. These commonalities make for an obvious career path from data analyst to data scientist and perhaps from there to machine learning engineer. This path involves heavier and heavier applications of computer science, and the salaries rise as one progresses.
The analysis and communication skills you learn as a data analyst carry over to data science, and the big data distributed databases and systems used by data scientists are often based on the same SQL queries you might use as a data analyst. Powerful distributed tools such as Apache Spark can be used with less Python and more SQL today, or even exclusively with SQL. Even complex machine learning systems are becoming easier to use, as the process of selecting and tuning models is increasingly automated. Making the transition into data science isn’t as hard as it once was. Udemy can help you learn the skills expected of a data scientist today.
Frequently Asked Questions
Can a data analyst become a data scientist?
The skills of a data scientist include those of a data analyst, so moving from a data analyst to a data scientist is a natural progression. It involves learning more about computer science, Python coding, machine learning, distributed systems, and handling big, unstructured data.
Should I learn data analytics before data science?
Some aspects of data analytics, such as SQL, introductory Python coding, business communication skills, and basic statistics will form a foundation for learning data science. Many of the business intelligence tools used in data analytics are not heavily used in data science; however, and learning those tools will be less useful.
Can you work from home as a data analyst?
Data analysis involves querying data and creating reports. It is particularly well suited to working from home — provided a secure connection between your home and any sensitive work data may be established. People working with especially sensitive information, such as classified military data, may not have the option to work remotely.