Difference Between Data Science and Data Analytics

Posted by Diksha Sharma on August 31st, 2021

Moreover, data\'s proliferation in business has grown with the emergence of social media, smartphones, the internet of things (IoT), and other technological advances. This trend is likely to continue as Artificial Intelligence, and Machine Learning become highly integrated into our daily lives and economy.

This exponential rise has driven organizations of all extents to question how to leverage information to accomplish business goals. Meanwhile, individuals are increasingly looking to develop their data skills to make their CVs stand out, elevate their careers, and obtain job security. Data Science and Data Analytics are two terms that may seem to overlap with each other but are still quite distinct.

What is Data Analytics?

It is the knowledge of Intermediate Statistics, excellent problem-solving skills, and agility in Excel and SQL databases to systematically reduce a body of data into smaller parts or compositions that can yield further information. In addition, you will need experience working with BI tools like Power BI for reporting and knowledge of Stats tools like Python, SAS, etc. To be a data analyst, you need not come from an engineering background, however having solid skills in statistics, databases, and predictive analytics will come as an added advantage.

Applying data analytics mechanisms and methodologies in a business context is typically known as business analytics. The primary objective of business analytics is to extract significant insights from the data that a company can employ to apply in its strategy and, ultimately, meet its goals.

What is Data Science? 

Data science is a multi-disciplinary mixture that comprises data inference, predictive modelling, and algorithm development to solve analytically intricate business complications. Data scientists leverage and create statistical models, algorithms, and their own custom analyses to assemble and mold raw data into something that can be more conveniently understood. In addition, data scientists can arrange undefined assortments of data using multiple tools simultaneously and build their own automation systems and frameworks. 

Difference between Data Science and Data Analytics

While Data Science focuses on detecting meaningful correlations between massive datasets, Data Analytics is meant to reveal the specifics of such extracted insights. In other words, Data Analytics is a division of Data Science that centers on more specific solutions to the problems that Data Science brings out.

Unlike data analytics which requires investigating a hypothesis, data science seeks to build connections and form the questions to solve them for the future. In addition, while data science concentrates more on machine learning and predictive modeling, data analytics is more about analyzing the historical data in context. Generally, Data scientists are highly technical, requiring a mathematical mindset, while Data Analysts practice a statistical and analytical procedure. From a career aspect, the role of a Data Analyst is more like an entry-level position. However, the choice between the two professions mainly depends on your interests and career goals.

It is worthy to note that data scientists are known to shine in all facets of industries like technology, e-commerce, or retail data essential for an organization. Their research strives to define a preferred audience and serve them to prepare for potential marketing and development strategies. Moreover, data scientists are in high demand and in short supply, making the competition even more fierce.

If you find this your calling, then start searching for relevant online professional courses to make a mark in the field. Hero Vired\'s integrated data science online program along with ML and AI aims to furnish you with the proper tools to grow indispensable in the future. This PG program in data science aims to deliver global educational quality with comprehensive Indian contextualization to deliver a fulfilling experience to students. With this data science program, you get to lead innovation and become the thought leader and game-changer that our society demands.