data science vs data analytics

With the rise of Big Data, two new buzzwords have emerged: Data Science and Data Analytics. The entire world now contributes to tremendous data expansion in colossal proportions, thus the term “Big Data.” According to the World Economic Forum, everyday global data creation will exceed 44 zettabytes by the end of 2020. By 2025, this figure will have risen to 463 exabytes!

Big Data encompasses everything we do online, including messages, emails, tweets, user queries (on search engines), social network activity, and data created by IoT and connected devices. Traditional data processing and analysis tools can’t handle the massive amounts of data generated every day by the digital world.

We commonly use Data Science and Data Analytics interchangeably since Big Data, Data Science, and Data Analytics are still developing technologies. Both Data Scientists’ and Data analysts’ work with Big Data contributes to the misconception. Despite this, the distinction between Analysts and Data scientists is apparent, driving the dispute between Data Science and analytics.

Data science: definition

Data science is a concept that combines data purification, preparation, and analysis and is used to deal with massive data. A data scientist collects data from various sources and uses machine learning, predictive analytics, and sentiment analysis to extract useful information from it. As a result, they can provide accurate predictions and insights that may be used to support crucial business decisions since they understand data from a business perspective.

Data analytics: definition

A data analyst can perform basic descriptive statistics, visualize data, and communicate data points to reach conclusions. They must have a fundamental understanding of statistics, a thorough knowledge of databases, the capacity to design new views, and the ability to visualize data. It can be thought of as the first step in data science.

Data science vs. Data analytics: Know the difference

Data Science and Data Analytics both deal with Big Data, but in different ways. Data Science is a broad term that incorporates data analytics. Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence are all included in Data Science.

Data mining, data inference, predictive modelling, and machine learning algorithm development are used to discover patterns from large datasets and turn them into meaningful business strategies. On the other hand, data analytics is mostly concerned with Statistics, Mathematics, and Statistical Analysis.

Data Analytics is aimed to reveal the particular extracted insights, whereas it’s focused on uncovering significant correlations between vast datasets.

Data Science aims to find fresh and interesting issues that might help businesses innovate. On the other hand, data analysis tries to uncover answers to these questions and decide how they might be implemented within a company to encourage data-driven innovation.


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