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Data science and data analytics are two overlapping and complementary functions within the data department of a modern enterprise.
Data science, however, is more specifically involved in creating systems by which large — and often unstructured — datasets are used to drive machine learning (ML) capabilities and therefore to inform predictive and prescriptive analytics and processes.
Data analytics, by contrast, is more involved in reporting or presenting more traditional, descriptive operational data or results for use by professionals in other departments within the business.
Data science and data analytics: Clearing up the confusion
For example, a data science department might incorporate data and write algorithms to integrate real-time social media sentiment, geographically-based economic information, supply availability and costs, and customer demand to create detailed revenue and profitability forecasts and resource allocation models.
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Meanwhile, a data analytics department might focus on providing visualization tools to help business analysts in finance and other operational departments.
Data scientists may write algorithms that data analysts use to provide dashboards and other visualization tools for nontechnical users to access and use organizational data.
There is some confusion about the difference between the terms because they have both been used in broader and more specific senses, and therefore are sometimes used variably within different organizations.
When “data science” is used broadly, it represents the overall function of gathering, organizing, crunching and presenting data; in that context, data analysis is just one stage of the process pipeline. When “data analytics” is used broadly, it represents the analysis of data generally, with data science being one particularly rigorous and mathematically oriented subset within the discipline.
The Cleverism job site provides an organizational chart depicting how the two functions may be positioned within a data department. Both require business and statistical understanding, while a data scientist also especially needs strong programming skills, and the analytics practitioner needs strong communications skills. The University of Wisconsin provides more detail on what is required for each role and function.
Data science vs. data analytics: Similarities and differences
Similarities between data science and data analytics
The similarities between data science and data analytics include:
- Both disciplines involve the extraction of key business insights from data.
- Both disciplines require the conversion of insights into more usable or understandable forms.
- Both disciplines require a combination of programming and statistical skills applied with significant understanding of the business.
Differences between data science and data analytics
Key differences between data science and data analytics include:
- Data science is more involved with newer, larger, more complex and unstructured datasets (that is, incorporating more real-time and external data), while data analytics primarily makes use of more traditional, operational data.
- Data science tends to use the artificial intelligence (AI) capabilities of ML, whereas data analytics is based more on enhancing the traditional reporting of operational business results.
- Data science requires more extensive programming and statistical skills, whereas data analytics requires more communications and collaboration skills for understanding and responding to the requirements of nontechnical users in other departments.
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