If you’ve ever wondered what the differences are between data analytics and data science, you’re not alone. Many people—even within the field—use the two terms interchangeably. Different companies define the two roles in different ways, and the titles alone may not reflect actual job activities and responsibilities. Not to mention, the data science field is relatively new and constantly evolving, so the boundaries are equally in flux.
The Key Distinctions Between Data Analysts and Data Scientists
Data Analysts’ Data is Structured and Data Scientists’ Data is Unstructured
While both data analysts and data scientists work with data, the nature of the data may be different. Generally speaking, data analysts perform statistical analysis on existing, structured datasets. These datasets may be contained in Microsoft Excel sheets or queried from SQL databases or Customer Relationship Management (CRM) systems.
Data scientists, on the other hand, are more likely to work with unstructured data. Unstructured data may include text, images, video, or audio. They may not exist in organized tables and are generally harder to query. Unstructured data may also be messier and grander in scale. Because of this, data scientists spend a lot more time collecting, cleaning, and wrangling data before they can start building mathematical models.
Data Analysts Have Goals and Data Scientists Have Hypotheses
Data analysts and data scientists are both problem solvers, but the problems they solve and how they go about solving them tend to be different. Generally, data analysts solve more focused problems that deal with day-to-day operations, such as: What is the total revenue this quarter? Why did a marketing campaign do better in Seattle compared to Texas? What are the current industry trends?
Data analysts have specific goals as they comb through the data, and they use statistical tools to turn data into actionable insights. They may build automated processes to collect, process, and present the data in a digestible way to the general audience.
Broadly speaking, data scientists solve problems that are a little squishier. Like explorers venturing into unknown terrain, data scientists are more interested in making new discoveries. Their main objective is to ask the right questions that will expose new patterns and insights, building connections that may change or challenge current understandings. To do this, data scientists incorporate computer science, predictive analytics, and statistics to parse through big, complex datasets and train machine learning models or AI algorithms to uncover trends.
Data Analysts Generate Reports and Data Scientists Build Models
The products of data analysis generally take the form of reports, data visualizations, or presentations. Data analysts may help to create data dashboards or automated reports that track business metrics, allowing other people to gain insight into how the business is doing. A good analyst will know how to communicate their findings effectively to stakeholders. Broadly speaking, data analysts check the pulse of an organization, making sure that everything is on track and diagnosing any problems.
Data scientists strive to build models that can then be used to power applications, driving new innovations and technologies. They are more likely to work with software engineers, developers, and user experience designers to create products or features that engage the customer base, or help stakeholders understand the business in a different way. A successful data science product changes how the business does business.
How Data Analysis and Data Science Work Together in Everyday Life
Let’s say you’re the CEO of an online school supplies company called Teacher’s Pet Supplies. With back-to-school season coming up, you would like to understand what items sell best so you can have enough inventory and advertise those best sellers to your customers.
You ask your data analysts to look into historic sales data to determine what consistently sells well every year. The analysts pull the data from the database, crunch the numbers, and find that fluffy pink flamingo pens sell out year after year. In fact, they discover, none of your competitors sell this pen, and many customers come to your website solely to purchase this amazing avian-themed pen.
It’s great news that you have a popular product, but you want people to buy more than a three-dollar pen. Preferably, these users will land on your site, buy other things, and become repeat customers. So you give this squishy problem to your data science team: How can we get our flamingo pen customers to buy more than just flamingo pens?
There are different ways to tackle this problem and no single right answer. The data scientists would begin by coming up with a hypothesis. They would then verify it with research and build a model that implements that hypothesis. They would also ensure that the model has some way of collecting feedback, so the data scientists can either improve the model or disprove the hypothesis and try something else.
We know that one way to increase sales is to build recommendation engines. But what items should we recommend? The data scientists may hypothesize that all customers who buy fluffy pink flamingo pens have similar tastes and interests. So if we recommend items that another flamingo pen customer has purchased, such as this fluffy pink flamingo notebook, our customer will be more likely to shop around and buy other things.
Recommending products based on similar customer interest is a common form of recommendation engine called Collaborative Filtering.
At the end of September, when all the students are geared up for school, the data analysts and data scientists at Teacher’s Pet Supplies can comb through the data to see if the recommendation engine has, in fact, increased sales. The data scientists find that customers are seeing and clicking on the recommended products, indicating that the model seems to be working. The data analysts observe that sales have increased, with customers spending more money overall.
Why It’s Important to Distinguish the Two Fields
While data analysts and data scientists both work with data, you now know they perform different duties and have different objectives. For companies looking to hire data scientists and data analysts, understanding the difference means bringing on the right person for the job or resourcing the right person for a project. Both data analytics and data science are important to a company’s success, so it is crucial to understand the roles they play and how they can contribute to the bottom line.
Despite their many differences, data analysis and data science are highly interconnected fields operating in service of the same goals. At a time when data literacy is vital to a business’s success, we need both roles to generate actionable insights from data and build innovative products.
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