Programming
Data Analytics or Data Science?
Oct 29, 2025
4-5 min Reading time
Data analytics and data science are closely related fields, but they have several key differences.
The main focus of data analytics is to uncover existing problems, their causes, and potentials that can be realized for the efficient operation of a business. Data science, in turn, focuses on eliminating and realizing existing problems and potentials through statistical and scientific methods. Unlike a data analyst, a Data scientist does not “discover” anything or answer questions, but instead automates complex business processes and frees them from human error.
What is Data Analytics?
Data analytics begins with collecting, processing, cleaning, visualizing, and modeling data, in short, preparing it for analysis. But that's only part of the story. Organizations can make more accurate and effective decisions through data analytics. Like data science, data analytics aims to help organizations make better, data-driven decisions. The main difference is that data analytics focuses on answering more specific questions.
Data Analytics Process
Hypothesize the problem or potential . Determine what questions you want to answer with your data analytics and make sure you have gathered the necessary source data. Sometimes the questions will be directed by other parties, but in most cases, you should be the one to write them yourself.
Data acquisition, processing, and management . This stage in data analytics involves the retrieval and reception of data, the integration of different types of data into standardized formats, their storage, and their management according to established rules.
Data analysis and reporting . Your data is brought into report form, visualized, and the report is made interactive, comprehensive, and drilldownable.
The Role of Data Analytics
Design and maintain data integration systems and data warehouses.
Working with the IT team to develop data governance policies and improve data analytics processes, data integration and management systems.
Finding information by building applications, conducting analyses, creating dashboards and visualizations using a data analytics or BI (Business Intelligence) tool.
In the absence of a full-featured analytics or BI platform, extract and analyze data sets using statistical tools. Provide regular (hourly, daily, weekly, monthly) and, if possible, automatic delivery of primitive analysis results to end users.
Develop dashboards and KPI reports for stakeholders to effectively communicate trends, patterns, and predictions using data.
What is Data Science?
Data science is the use of tools and techniques such as programming, statistics, machine learning, and algorithms to collect, organize, and analyze large sets of data. This data is usually a combination of structured and unstructured information. The goal of data science is often to identify patterns, but it can also be to ask questions, find the right ones, and identify areas for investigation.
6 main stages of the data science process:
Goal setting . The data scientist works with business stakeholders to define goals and objectives for the analysis. These goals can be both specific and broad.
Data collection . If systems are not set up to collect and store data, the data scientist sets up this process in a systematic way.
Data integration and management . A data scientist applies data integration best practices to clean data and make it ready for analysis.
Data exploration and research . The data scientist performs the initial research and analysis of the data in this phase.
Model development . A data scientist selects one or more potential analytical models and algorithms, then builds these models using languages such as SQL, R, or Python and develops them using AutoML, machine learning, and artificial intelligence.
Model implementation and presentation . Once models are selected and developed, theories are generated using the available data. These theories are then shared with all stakeholders through advanced data visualization and dashboards.
Difference between Data Analytics and Data Science
Both fields involve extracting valuable information from data, but data analytics focuses more on analyzing past data to support current decisions, while data science uses data to predict future outcomes.
Data scientists use statistical and computational methods to derive insights from data, build predictive models, and develop new algorithms. Data analytics, on the other hand, aims to extract valuable information from data and support business decisions.
Purpose
In data science, both broad insights are gained by examining data, and actionable results are obtained by answering specific questions.
Data analytics focuses on answering more specific questions, and the results obtained are ready for practical use.
Coverage and Capabilities
Data science is a multidisciplinary field that encompasses data engineering, computer science, statistics, machine learning, predictive analytics, and the communication of the results obtained.
Data analytics includes data processing, reporting, visualization, integration, analysis, recommendation generation, and presentation.
Approach
Data scientists prepare, manage, and explore large data sets, then develop specialized analytical models and algorithms to extract required business insights.
Data analysts prepare, manage, and analyze defined data sets, identify trends, and create visual representations to help organizations make better, data-driven decisions.
