Data Science or Data Analytics: Compare and Contrast?
Big data is getting bigger: by 2020, it’s estimated that there will be 44 zettabytes (or 44 trillion gigabytes) of data in the world, with 1.7 megabytes being created for every human being per second.
We know that the examination of data is vital for any industry, but with “data science” and “data analytics” being used almost interchangeably, we’re breaking down the differences between them and how their applications are being used.
What is Data Science?
“Data scientist” was rated as the best job in the United States (2018) when considering a median base salary, job openings, and overall job satisfaction. But it’s a vague job term made up of two particularly wide-encompassing words – so what exactly is data science?
Data science is the exploration of large amounts of data, both raw and structured, to find connections and insights we can act upon. Rather than finding the correct “answer” to a problem, data scientists focus on finding the right question to ask to develop further study.
These data science questions are created by examining various data sources, including those which at first may not seem to be linked, identifying potential trends, and developing new techniques for analyses. It has a broad application, combining computer programming, mathematics, statistics, and problem-solving, and there are several different methods used to study the data, including computer science, statistics, predictive analysis, and machine learning.
Although formal training in computer science is not necessary, key skills include proficiency with programming tools, data visualization and communication, machine learning, statistics, and data wrangling and intuition.
Where is Data Science Used?
Data science is particularly useful for the development of areas where information is categorized and sorted, such as AI and machine learning (including internet searches, search recommendations, and digital marketing), and is also found in engineering and corporate analytics.
What is Data Analytics?
Data analytics is a more focused area, concerned with the capture, processing, and organization of data and performing statistical analysis on it to find solutions to identified problems. Once the analysis of the data has yielded actionable results, these can be applied immediately to existing queries.
Where is Data Analytics Used?
Data analytics is primarily found where there is an immediate need to develop data analysis for better provisions, such as healthcare, finance, energy management, agriculture, travel, and gaming.
How Do Data Science and Data Analytics Compare?
When stripped of technological jargon, the differences between the two sects are easy to interpret. Data science is the exploration of a question to use the data more effectively. Data analytics, however, is searching for an answer to an issue that has already been identified. Data science is the explorer traipsing through the jungle to see what they might uncover; data analytics is the explorer with a fixed destination in mind.
Data science is explorative questions; data analysis is focused on answers. Data scientists are unearthing what we don’t know that we don’t know, whereas data analytics engineers are finding answers to what we know we don’t know. Data science is identifying trends in the future, and data analytics are identifying solutions to existing problems. Neither needs a specific degree, but the majority of workers in both data science and data analytics are educated to master’s level and will have certification in their field.
How Do They Interact?
Data science might be considered the base of any data analytics investigation. Data science examines data to provide insights and predict trends but does little to provide answers to already-identified issues. This is where data analytics comes in: Building on the insights gathered from data science, data analytics can unearth answers with practical applications. Data science and data analytics are not competing but instead supporting each other in development.