Which Path is Right For You?

You may have heard the phrases “data analytics” and “data science” mentioned before. If you are new to the world of data, you might be wondering what these terms mean — and if you’re interested in a career in data, which is the right path for you?

Though there is a lot of overlap between the two areas (and disagreement about the exact definitions), the main difference is how much they rely on machine learning. In general, data analytics covers everything from collecting data to spotting trends to communicating insights. Data science is a broader field that includes data analytics, and often involves making predictions with tools like machine learning or conducting experiments with data.

Companies collect a great deal of data. Almost all of them can benefit from data analytics to help make sense of it. But not as many require building algorithms that predict the future or apply patterns to new information.

Interested in working with data, but not sure where to start? In this article, we’ll explore data analytics and data science in more detail, to help you decide between our Data Scientist: Analytics Specialist career path and our Data Scientist: Machine Learning Specialist career path.

What is data analytics?

Data analytics is all about helping organizations make decisions based on data. Page visits can inform marketing strategies, housing costs can affect policy changes, and patient outcomes can impact a hospital’s operations. Data analytics helps us find patterns and tell stories from the large quantity of data organizations have.

“Every company is collecting some data. And a lot of companies need to leverage their data to make good data-driven decisions. There’s a huge opportunity for Data Analysts to really put that data to work.” says Codecademy Data Science Domain Manager Michelle McSweeney.

Data science is a broad field that includes data analytics. It also covers making predictions with machine learning, working with big data, and developing artificial intelligence.

“Data science tends to be more specialized than data analytics, because not every company needs to make predictive data decisions, and not every company needs to leverage big data,” Michelle says.

To learn more about data science, check out our blog all about: What is data science?

Data Analyst vs. Data Scientist

The Similarities

At their core, both roles revolve around turning raw data into actionable insights that can inform better business decisions. Whether it’s analyzing trends, identifying patterns, or helping stakeholders understand performance metrics, both data analysts and data scientists share a common goal: making sense of data to drive smarter strategies.

A key similarity lies in the tools and techniques they use. Both roles typically work with programming languages like Python or R. They also leverage SQL for querying databases, and share foundational knowledge in statistics, data wrangling, and exploratory data analysis.

The Differences

While Data Analysts and Data Scientists share overlapping skills, their roles diverge in scope, complexity, and focus.

Data Analysts typically concentrate on taking a business question and translating it into a data question. They answer “what happened?” and “why did it happen?”. To do so, they’re responsible for collecting and reformatting data, analyzing it with statistics and probability, and sharing actionable insights in the form of visuals and reports. Their work is often more structured and driven by specific business queries or performance metrics.

Data Scientists, on the other hand, tend to operate at a more advanced level to answer deeper questions like “what will happen?” or “how can we influence future outcomes?”. They’re tasked with creating algorithms to automate data processes, recognize patterns, and make recommendations based on past behavior. They work on things like forecasting the financial future, creating customer-facing chatbots, detecting tumors in X-ray images, and making suggestions of things you might like.

Data Analysts: Salary and Skills

Salary

According to ZipRecruiter, the average salary for Data Analysts in the U.S. is $82,640 per year. Still, your salary as a Data Analyst will depend on your location. 

Industries with a higher demand for Data Analysts tend to provide higher salaries. For example, Payscale reports that data analysts working for Amazon can get paid up to $101,000 in the U.S. With Meta offering around $146,000, per Indeed. Other high-paying industries include healthcare, finance, insurance, and professional services.

Skills

Here are some of the basic skills required of a Data Analyst:

• Data collection & cleaning: Gather data from various sources and prepare it for analysis by fixing errors, handling missing values, and organizing it into a usable format.
• Data analysis: Explore and interpret data to find patterns, relationships, or trends that can help answer specific questions or solve problems.
• Data visualization: Use BI tools to create visual representations of data — like charts, graphs, and dashboards — making complex information easier to understand and interpret.
• Data reporting: Summarize and present data findings in a clear, structured format — often through reports or presentations — to inform decision-makers and stakeholders.

Data Scientist: Salary and Skills

Looking at Indeed, Data Scientists make an average salary of $126,833 per year in the U.S. Even with less than a year of experience, Data Scientists earn $101,338 on average. Those with three to five years of experience make $138,080 each year, with a high of $201,230.

Skills

Here are some of the basic skills required of a Data Scientist:

• Data modeling: Design and organize data structures — like tables, relationships, and schemas — to represent how data is stored and accessed in databases or systems.
• Machine learning: Create and use algorithms to detect patterns in data and make predictions or decisions without being explicitly programmed for every scenario.
• Experimentation: Design and execute controlled tests (such as A/B tests) to validate hypotheses with data and optimize performance.

Data Analyst vs. Data Scientist: At A Glance

Data Analyst Career Paths

Interested in learning more about data analytics? You can dive into our Data Scientist: Analytics Specialist career path to learn everything you need to become a Data Analyst.

Or, our Business Intelligence Data Analyst career path is designed to teach you the essential skills to be job-ready in as little as three months.

If you want to learn a specific Data Analyst skill, check out the following Skill Paths:

Even if your ultimate goal is to become a Data Scientist, gaining a solid foundation in data analytics is a good first step.

Data Scientist Career Paths

Interested in becoming a Data Scientist? Our Data Scientist career paths will teach you everything you need to know to become an entry-level Data Scientist from the ground up.

Regardless of which path you choose, you’ll use your new skills to build unique projects you can use to build a portfolio — and we’ll also help you prepare for the hiring process with interview prep courses and other helpful resources you can find in our Career Center.

This blog was originally published in April 2021 and has been updated to include updated salaries, and new resources.