Data Science Vs Data Analytics

Data Science Vs Data Analytics

April 26, 2021

Despite how similar the two may sound to the unskilled reader, data scientists and data analysts are not one and the same. Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. Data analytics is narrower in scope, focusing mainly on processing and performing analysis of existing datasets. In this article, we will explore what these terms really mean as well as some common misconceptions about each one. Let’s understand what is Data Science and Data Analytics in detail.

What is Data Science?

Data science is a fast-growing branch of science and engineering that uses computational methods to extract knowledge and insights from data in various forms and types such as structured or unstructured. The main objective of data science is extracting meaningful knowledge, intuitions, and patterns from both unstructured and structured datasets. The main focus of data science is discovering new insights that can be produced using the massive explosion of digital data in various forms. Data scientists often use machine learning algorithms to derive predictive insights from existing datasets.

Data science techniques help integrate large amounts of raw and structured data into useful visualizations and diagrams for making clear insights available. Data scientists often work on the production of visualizations to help communicate results to others, while at the same time labeling the different variables in a dataset using names that are meaningful (as opposed to arbitrary numbers or letters).

Data science is a fast-growing branch of science and engineering focused on extracting actionable insights from massive amounts of unstructured data.

Data Science

What is Data Analytics?

Data Analytics is a scientific process of extracting knowledge from the existing datasets that are used to gain insights into business intelligence. Data analytics is a subset of data science that focuses mainly on current datasets.

Data analytics often help business users make more data-driven decisions. The main focus of data analytics is the analysis of existing datasets to find hidden patterns and convert them into actionable insights. Data analytics encompasses a wide range of different approaches such as business intelligence, data visualization, and sometimes machine learning.

Data Analytics can often reveal valuable insights from existing datasets through various analytic methods. Business users often use these insights for making more data-driven decisions to improve their business operations and increase revenue.

Data Analytics

What is the difference?

What are the Differences Between Data Science and Data Analytics?

The first thing to realize is that both of these titles are quite vague. Essentially, anybody who works with data can be called a ‘data scientist’ or ‘data analyst’ depending on the extent of their duties. Data analysts typically perform a detailed analysis of large sets of structured data whereas data scientists typically focus more on extracting actionable insights from massive amounts of unstructured data. Data scientists are involved in predictive analytics using machine learning models where they may also have to make fine-tuned decisions and provide analytical support for business users to improve their operations using more specialized skills of computer programming and statistical analytics.

The two titles both rely on large amounts of data analysis but the methods used to analyze this data are very different.

Data analysts focus on structured data whereas data scientists focus on unstructured data that is typically available in the form of text, videos, audio, and other sources of media. It is also important to remember that ‘data’ does not only refer to numerical and statistical data. Data can be anything from a picture or audio file to social media posts or user contact lists.
Data Visualization is the Most Important Part of Data Science

What are the skills required to be a data analyst?

A good data analyst should be good at math, statistics and a good data storyteller.

Data analysts don’t typically have any specific degree or educational qualification. Instead, they generally have strong backgrounds in computer science, mathematics, and statistics as well as a few years of work experience.

What are the skills required to be a data scientist?

Data scientists need to have extensive knowledge of computer science & mathematics. They must be able to write functional code in several programming languages.

In the past, it was common for analysts and statisticians to be able to tackle data analysis but this is no longer the case. Due to the massive increase in both structured and unstructured data coming online, today’s data scientists must have plenty of skills and knowledge of both statistical concepts and computer programming. A good data scientist should be a master at probability, statistics, machine learning, predictive analytics as well as computer programming.

Importance of Data Visualization

Data visualization goes hand in hand with data analysis and most if not all effective data scientists and analysts use both together in order to truly understand the meaning behind their work.

What is the main benefit of data visualization? Data visualization makes raw and unstructured data actionable. It is quite easy to get lost in the sea of numbers and statistics which can be difficult to properly understand by itself, let alone discover any kind of insights from.

People feel more comfortable when they are presented with visual information, even if it is just an image. This is because our brains are not very great at processing information, especially when it comes to raw numbers and statistics. For this reason, it is important to make data visualization an essential part of data science projects.

Data Visualization

There are many benefits to data visualization in both the research and the practical stages of your project:
● You will be able to understand the meaning behind your results in a much easier way. Visualization is a great way not only to better understand any kind of results but also gain insights from your raw datasets by finding hidden connections between seemingly unrelated issues.
● Data visualization allows you to see the patterns behind your raw and structured data sets. By visualizing your data, you can easily spot trends or patterns that are not obvious in plain text.
● Data visualization is an extremely effective way of communicating your results to other people. The best thing about it is that it can be both informative and engaging for the rest of your team. Using visualizations, they will be able to clearly understand any kind of results without having to read through any large amounts of text and figures.
● Data visualization is an excellent way to share your insights on your project with the next generation. Visualizations are a great way to explain any kind of data science results without having to go into complicated mathematical equations or charts for everyone.
● Data visualization is also a great way of finding visual ways of communicating any kind of ideas and concepts, which can help you make an important point clearer for others.

Due to the nature of data visualization consisting of simple low-dimensional images, it is suited well for humans, who are visual creatures. One disadvantage of visualization however is that there are no defined rules governing its design. This makes it hard for anyone other than a designer to make conclusions about your data. In addition, there is no way to fully express or compare results without displaying them visually in contrast to each other.


To summarize- Both, data analytics and data science are highly in demand in today’s world. The more data you manage, the further you can get in understanding your data. A data visualization, which effectively highlights the importance of a certain point in your data, is the best way to make your findings noticeable. One also has to be able to look at data from a new perspective and show it in a way that is easy for the end-user to understand.


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