ChatGPT
Perplexity
.png)
.png)
.png)
Build your first embedded data product now. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
With the rise of big data as a technology and the overwhelming amount of data that we work with every day, a bunch of new terms were coined. Two of them are data science and data analytics, which are often used interchangeably because both data scientists and data analysts work with big data.
However, there are tangible differences between data science vs data analytics and today, we’re going to show you those differences, as well as the similarities between the two terms.
Data science uses math, statistics, programming languages, machine learning, artificial intelligence, and more to uncover actionable business insights in a company’s data. The data science process usually involves data collection, data exploration, data ingestion, data storage, data engineering, data mining and data processing.
Typically, the data analytics process happens next.
Data analytics is the process of taking raw data and distilling it to find trends, patterns, and behaviors that can lead future decision-making. It has a more narrow scope than data science and data analysts typically take the readily available data, visualize it, and present solutions to ongoing problems in a business.
Skills involved include statistical analysis, data visualization, as well as some mathematics - but far less than data science. Data analysts also deal with areas such as predictive analytics, descriptive analytics, prescriptive analytics and diagnostic analytics - all concerned with various stages of solving a problem. You can develop math skills for data science by taking online math lessons. It will help you in your future career.
It is also important to differentiate data analytics and business analytics in the realm of data intelligence. Business analytics focuses on the overall implications of data on the entire business. For example, it tells you whether your company should launch an entirely new product to complement based on the readily available data.
There are some common areas that are covered in both disciplines.
Even more importantly, there are key differences between data science and data analytics that data-driven businesses should be aware of.
The distinction between data science and data analytics matters practically for product teams deciding how to staff their data function and which tools to invest in.
A data analyst's primary output is insight about what has happened and why. They work with existing data to answer business questions: which features are driving retention, where users drop off in the onboarding flow, which customer segments are growing fastest. Their tools are SQL, visualization platforms, and dashboards. Their audience is the business — product managers, marketing teams, executives — who need to make decisions based on current and historical data.
A data scientist's primary output is models that predict or automate. They build systems that forecast churn, recommend next actions, classify support tickets, or optimize pricing. Their tools include statistical modeling, machine learning frameworks, and experiment infrastructure. Their work often runs in the background — the output is a score, a recommendation, or an automated action rather than a dashboard a human reads.
In most software teams, these functions are not cleanly separated. An analytics engineer may build both dashboards and prediction models. A product analyst may run A/B tests that require statistical methods. What matters for tooling decisions is understanding which work is about understanding what has already happened (analytics) and which is about predicting or automating what happens next (data science), because these require different infrastructure and different evaluation criteria.
Embedded analytics — putting data analysis features inside a product for end users — sits firmly in the analytics domain. The question it answers is not "what will happen?" but "what is happening, and what does it mean for this specific user's context?" Luzmo AI is built for this use case: surfacing data science outputs (model scores, predictions, classifications) alongside analytics visualizations so that end users get both historical context and forward-looking intelligence in the same embedded experience.
A data scientist uses a combination of machine learning, and mathematical and statistical models to extract, clean, process, and interpret data and get meaningful insights from it. They use data modeling processes and machine learning algorithms to get insights from that data.
On the other hand, a data analyst works with a clean dataset that may or may not come from a data scientist. They take this data, organize and structure it, and then do data analysis to find relevant patterns. After the analysis, they do data visualization (in business intelligence tools such as Luzmo, Tableau, PowerBI) to show that data in an easily digestible format, such as dashboards, graphs, charts, tables, and others.
For example, a data analyst could get a set of data about the use of an app over time to determine who are the heaviest users of an app, what they use it for, what features are the most valuable to them and more.
In other words, data analysts take complex data and translate it into a common language that anyone can understand and make business decisions from it.
As you can see, the roles of a data scientist overlap quite a bit so it’s easy to see why the two terms are often confused.
We really can’t tell you that as both are great career choices for a young professional with a passion for data. As we continue building our world around big data, both will be in demand in the future. Both require a bachelor’s degree as a basis for starting.
The great news is that both a data scientist and a data analyst job have identical educational backgrounds and similar skill sets. You’ll need skills in computer science, statistics (different levels), mathematics, IT, and some programming for both careers. Of course, you’ll also need the basics such as problem-solving and critical thinking skills to be a pro in either role.
You can try out both and switch careers at some point once you figure out what suits you better. If you have better analytical skills and prefer solving problems in real-time, you might like data analyst roles better. If you’re more passionate about statistics, database management, data wrangling and managing large datasets, a data science career is a better career choice.
It depends on your business needs, if you’re thinking about hiring a data scientist or a data analyst. The truth is, you might want to hire both if you have the needs and the budget. It’s not an either/or kind of situation.
Data science is useful for any organization as it can help organize unstructured data and prepare it for further use. If you have large volumes of data sitting around being unused, data science can answer some questions you may not even know you had.
At the same time, data analytics is necessary if you want to put that data into perspective with your business goals. It can help your stakeholders solve the problems you have now by giving you visualizations of your available data.
Once again, if you run a business that handles large volumes of data, it’s best to have both a data scientist and a data analyst on board. This is particularly beneficial when working with big data analytics companies, as they require expertise in both areas to derive meaningful insights.
The analytics stack for a software team typically includes a data warehouse (Snowflake, BigQuery, Redshift), a transformation layer (dbt, Fivetran), a SQL interface for ad hoc queries (DataGrip, DBeaver, or the warehouse's native interface), and a visualization and dashboarding layer. For internal analytics, Metabase, Redash, and Looker are common choices. For customer-facing analytics — embedded into the product — purpose-built platforms designed for multi-tenant deployment are more appropriate than internal BI tools.
The data science stack typically includes Python (with pandas, scikit-learn, and PyTorch or TensorFlow for modeling), Jupyter notebooks for experimentation, an experiment tracking platform (MLflow, Weights & Biases), and a model serving layer (SageMaker, Vertex AI, or a custom FastAPI service). Feature stores (Feast, Tecton) are increasingly common in teams running multiple models in production, as they provide a shared layer of computed features that multiple models can access without duplicating computation.
The clearest overlap is in the visualization layer. Data scientists build models; the outputs of those models — scores, forecasts, recommendations — need to be presented to users in context. An embedded analytics platform that can display both traditional BI metrics and ML model outputs in the same dashboard is more valuable to a product team than two separate tools. This is increasingly the direction that analytics platforms are moving: the boundary between "show me what happened" and "tell me what will happen" is dissolving as AI capabilities become embedded in the visualization layer itself.
Both data science and data analytics are necessary if you take your data seriously. While data science is considered to be a wider discipline (and more senior), data analytics complements it perfectly as it provides actionable insights that most data science does not offer on its own.
And if you do analytics, you’re going to need an easy-to-use, reliable tool for visualizing your data. For stunning analytics dashboards, sign up for a free trial of Luzmo and start visualizing your data - directly within your product.
All your questions answered.
What is the main difference between data science and data analytics?
Is data analytics part of data science?
Which career is better: data scientist or data analyst?
Build your first embedded data product now. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
