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Data Science vs Data Analytics – Understanding Differences

A brief introduction to the differences and similarities between Data Science and Data Analytics

Data science is the application of statistical and analytical methods to data to extract knowledge for solving business problems. The field is growing rapidly with an increasing number of jobs being created, as well as a number of new universities offering degrees in data science.
Data scientists can work in many different fields and industries, but they generally work with data in one of three ways: predictive analytics, business intelligence, or technical expertise. They are professionals who use their expertise in mathematics, statistics, and computer science to extract value from big data.
Data analytics is the process of deriving meaningful information from data. Data analytics can be used to identify patterns and trends in data, as well as to predict future events. It can also be used for decision-making and for the optimization of processes. There are many different types of data analytics, such as descriptive, predictive, and prescriptive analytics.
Data analysts work extensively with data mining tools like R or Python to find insights into the dataset. The skills required for a career in data analytics have increased exponentially over the past few years due to their widespread application in various industries. In order to become a successful professional in this field, one needs to have strong analytical skills.
Below is a table showing the differences and similarities between Data Science and Data Analytics:
Although the table above list out many skillsets and tools should have, the requirement to be data professional is varied from hiring companies according to their objective to build the data team.

Written by: Xiao Tong Loh

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