Data Science vs Data Analytics: Unraveling the Differences

Data science and data analytics are two phrases that are frequently used interchangeably, which leads to misunderstandings regarding their true meaning and scope. In reality, while they both involve working with data, they serve different purposes and require distinct skill sets. Let’s delve into the specifics of each discipline to understand their unique roles in the realm of data. Are you looking to advance your career in Data Science? Get started today with the Data Science Course in Chennai from FITA Academy!

What is Data Science?

Data Science is the study of extracting useful information and insights from organized and unstructured data using various algorithms, preprocessing, and scientific methodologies. This field is related to artificial intelligence and is one of the most in-demand skills today.

Data Science Process

Data collection

If there aren’t currently mechanisms in place to capture and store source data, the data scientist creates one.

Data integration & management

The data scientist uses optimal data integration practices to transform raw data into clean information that is suitable for analysis. Data replication, ingestion, and transformation are used in the data integration and management process to integrate various forms of data into standardized formats.

Data investigation & exploration

The data scientist conducts an initial exploration of the data and exploratory data analysis in this step. This research and exploration is usually carried out with the help of a data analytics platform or a business intelligence tool.

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What is Data Analytics?

Data analytics is used to derive conclusions from raw data. It is useful in a variety of organizations since it allows the company to make decisions based on data conclusions. Essentially, data analytics aids in the conversion of a large number of numbers in the form of data into plain English, i.e., conclusions that can then be used to make more in-depth judgments.

Data Analytics Process

The basic steps in the data analytics process are to define needs, integrate and manage data, analyze data, and share findings.

Project Requirements & Data Collection

Determine the question(s) you want to answer and make sure you have all of the necessary source data.

Data Integration & Management

Transform raw data into apparent business-ready information. This process includes data replication and ingestion to aggregate various forms of data into standardized formats that are kept in a repository such as a data warehouse or data lake and managed by a set of specific rules.

Difference Between Data Science and Data Analytics

There is a distinction to be made between Data Science and Data Analytics. We’ll go through them one by one for each feature.

Coding Language

Python is the most often used language for data science, however other languages such as C++, Java, Perl, and others are also utilized.
Knowledge of Python and R is required for Data Analytics.

Programming Skills

Data science necessitates extensive programming knowledge.
For data analytics, basic programming abilities are required.

Use of Machine Learning

In data science, machine learning algorithms are utilized to gain insights.
Machine learning is not utilized to obtain insight in data analytics.

Data Type

Data Science primarily deals with unstructured data.

Data analytics is concerned with structured data.

Statistical Skills

Statistical skill is required in the subject of Data Science.
In data analytics, statistical abilities are of little or no benefit.

In conclusion, while data science and data analytics both revolve around data, they cater to different organizational needs and operate within distinct domains. Data science is more inclined towards predictive modeling and algorithm development, while data analytics revolves around interpreting historical data to support business operations. Understanding the nuances between these two fields for organizations seeking to leverage data effectively for strategic decision-making and innovation.

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