When discussing big data, the phrases “data science” and “data analytics” are sometimes used interchangeably, which might cause misunderstandings. Although data is at the center of both disciplines, their approaches and points of emphasis are very different.
Data science: what is it?
Data science is an interdisciplinary area that extracts knowledge and insights from both structured and unstructured data using scientific procedures, systems, algorithms, and methodologies. Analyzing and comprehending complicated data includes a range of methods from big data technology, machine learning, statistics, and data mining.
Essential Features of Data Science:
Investigative: It all comes down to posing pertinent queries and looking through the data to find relevant information.
Predictive: makes use of machine learning to extrapolate data-driven future patterns.
Programming Intensive: To modify data and create algorithms, one must possess strong programming skills.
Unstructured Data: Handles unprocessed or unstructured data, such as text, pictures, and videos
Data analytics: What is it?
In contrast, data analytics is primarily concerned with handling and executing statistical analyses on pre-existing datasets. The main goal of analysts in this profession is to produce insights that are useful for making defensible conclusions.
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Important Features of Data Analytics:
Descriptive: Describes patterns and sets of facts.
Business-oriented: Seek to provide precise answers that can enhance the operation of businesses.
Less code: Compared to data science, there is less complexity in the code involved.
Structured Data: mostly pertains to data that is well-organized and easily stored in a database.
For in-depth information about data, go to Wikipedia.
The Meeting Point and Breaking Off
Although they both work with data, data scientists and data analysts do so in different capacities:
Data scientists create prediction-making algorithms that are useful for making tactical choices. They frequently possess graduate degrees and in-depth knowledge of machine learning.
Data analysts seek strong trends and patterns to provide operational insights that help guide quick decisions. Compared to data scientists, they usually don’t need as sophisticated of a degree and don’t require as much programming experience.
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