Data science and data analytics are two overlapping and complementary areas within the data department of a modern enterprise. If we want to be a little more specific, data science is concerned with the creation of systems that use large – and often unstructured – datasets to empower machine learning (ML) capabilities and therefore inform predictive and prescriptive analytics and processes. Data analysis, on the other hand, is more concerned with reporting or presenting and visualizing more traditional, descriptive operational data or results that can be used by specialists in other departments within the company.
Data science and data analytics: Clearing up the confusion
A data scientist, for example, can incorporate data and write algorithms to integrate real-time social media sentiment, geographic-based economic information, supply availability and cost, and customer demand to create detailed revenue and profitability forecasts and resource allocation models.
Meanwhile, a data analyst can focus on providing visualisation tools to assist business analysts in finance and other departments.
There is some confusion about the difference between the terms, as both are used in broad and specific senses and are therefore sometimes used differently within different organisations.
If we use the term ‘data science’ broadly, it refers to the general purpose of collecting, organising, processing and presenting data; in this context, data analysis is only one stage in the process pipeline. If ‘data analysis’ is used in a broad sense, it refers to the general analysis of data, with data science being a particularly rigorous and mathematically oriented subset within the discipline.
Both require business and statistical skills, while the data scientist needs particularly strong programming skills and the analytics specialist strong communication skills.
Data science vs. data analytics: Similarities and differences
Similarities between data science and data analytics
- Both disciplines are concerned with extracting key business insights from data.
- Both disciplines require the transformation of insights into a more usable or understandable form.
- Both disciplines require a combination of programming and statistical skills, along with a significant knowledge of business.
Differences between data science and data analytics
- Data science tends to deal with newer, larger, more complex and unstructured data sets (i.e. more real-time and external data), while data analytics primarily uses more traditional, operational data.
- Data science tends to use the artificial intelligence (AI) capabilities of ML, while data analytics is more focused on improving the traditional reporting of operational business results.
- Data science requires a broader range of programming and statistical skills, while data analytics requires more communication and collaboration skills to understand and respond to the needs of non-technical users in other departments.
Source: Venturebeat