Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions. Let’s explore data science vs data analytics in more detail.

Overview: Data science vs data analytics

Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes.

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists. In other words, while the two concepts are not the same, they are heavily intertwined.

Data science: An area of expertise

As an area of expertise, data science is much larger in scope than the task of conducting data analytics and is considered its own career path. Those who work in the field of data science are known as data scientists. These professionals build statistical models, develop algorithms, train machine learning models and create frameworks to:

Forecast short- and long-term outcomes
Solve business problems
Identify opportunities
Support business strategy
Automate tasks and processes
Power BI platforms

In the world of information technology, data science jobs are currently in demand for many organizations and industries. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. Data scientists also work closely with data engineers, who are responsible for building the data pipelines that provide the scientists with the data their models need, as well as the pipelines that models rely on for use in large-scale production.

Data analytics: Tasks to contextualize data

The task of data analytics is done to contextualize a dataset as it currently exists so that more informed decisions can be made. How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture, which allows an organization, its users and its applications to access different types of data regardless of where that data resides. Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics.

The benefits of data analytics

Business decision-makers can perform data analytics to gain actionable insights regarding sales, marketing, product development and other business factors. Data scientists also rely on data analytics to understand datasets and develop algorithms and machine learning models that benefit research or improve business performance.

Data science, data analytics and IBM

Practicing data science isn’t without its challenges. There can be fragmented data, a short supply of data science skills and rigid IT standards for training and deployment. It can also be challenging to operationalize data analytics models.

IBM’s data science and AI lifecycle product portfolio is built upon our longstanding commitment to open source technologies. It includes a range of capabilities that enable enterprises to unlock the value of their data in new ways. One example is watsonx, a next generation data and AI platform built to help organizations multiply the power of AI for business.

Watsonx comprises of three powerful components: the watsonx.ai studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.