Data Science vs. Data Analytics: Differences Explained

Data is everywhere today. We generate massive amounts of data with our activities over the Internet. Data Science and analytics sector is growing exponentially, and organizations are looking for candidates who can sift through the data and identify meaningful insights to help them drive important business decisions efficiently.

According to a report by IBM, the number of jobs for the US data professionals will reach 2,720,000 from 364,000 job openings. The Big Data industry will reach USD 77 billion by 2023. 

With almost 2.5 quintillion bytes of data generated every day, it is important to process the data to make it meaningful. Suppose this data is analyzed and presented in such a way that it captures the requirements of the user and makes innovations accordingly. In that case, we can develop a revolutionary system that enables businesses to provide high-quality solutions to the problems that a common man faces, all this at a very low cost. This system can enhance and improvise itself to become more innovative gradually. 

How do you think it can be possible?

Its Data Science includes Data Analytics, Deep Learning, Machine Learning, and many more things. Data Science with Python courses can enable you to bring these revolutions in the industry of your choice by making you land your career in Data Science. 

The terms data analytics and data science are used interchangeably, but there is a difference in both of them in the way they come up. Let us explore what they mean and the basic differences between them.

A Basic Illustration

Since Big Data implies huge volumes of data, the traditional methods of collection of data are no longer sufficient. There is a need for systems that can collect the data, process it for the relevant target group, apply any of the machine learning and statistical models to analyze it, and then predict future decisions based on existing data. This is rendered as a feedback system. A part of this system is Data Analytics that involves applying statistical analysis on the datasets to find answers to business issues. And the remaining part - which involves parsing the data, machine learning, predictive analysis, and data visualization is Data Science.

What is Data Analytics?

Since there is a lot of data to be collected and can be analyzed in an appropriate manner so that business benefits can be achieved. Such analysis of data to extract meaningful information and useful insights to solve a business problem is referred to as Data Analytics. 

In Data Analytics, various tools and techniques are used to analyze Big Data. The steps involved in Data Analytics are:

• Identifying data requirements and grouping in relevant groups depending upon the business problem. For example, age, location, etc.

• Collating the data from different sources offline and online may include social media, computers, surveys, etc.

• Arranging and organizing the data for analysis. Commonly, spreadsheets are used for data organization, although Apache Spark and Hadoop are becoming popular these days.

• Cleansing the data by removing inconsistent, incomplete, or duplicate data sets. Data is made ready for analysis by removing errors, if any.

Data Analytics is employed in almost every sector today, including the media and entertainment, healthcare, finance, tourism, retail, and hospitality industries. 

What is Data Science?

Data Science has a broader scope than data analytics. Data Analytics is the subset of Data Science and forms a phase of the Data Science lifecycle. This means that the processes carried out before and after the data analytics is part of Data Science.

Let us see how Wikipedia defines Data Science.

“Data Science is an interdisciplinary field that uses scientific methods processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.”

The main components of Data Science are:

• Statistics- Statistics is all about collecting, analyzing, interpreting, and presenting the data through mathematical models.

• Data Visualization- after obtaining results from data science, they are interpreted in the form of visually attractive charts, diagrams, and graphs which makes them simple and easy to understand. This highlights the key results and hence helps in better decision-making. 

• Machine Learning- this is an important part where you need to use machine learning algorithms that learn on their own and project human conduct as correctly as possible. 

As a Data Scientist, you have to identify and define potential business issues from a variety of unrelated sources and collect data from these sources. After the data is analyzed via Data Analytics, a model is developed and tested with various iterations until the desired accuracy is achieved. 

Data Science is an extensive domain and offers a more promising future. Data Analytics is the domain for you if you wish to be in the programming area. One thing common in both domains is that both work on data extensively to get a clear picture of the business problem. Data Science involves the whole business process from business partners, stakeholders, storytelling, data analysis, preparing, model development, testing, and deployment. Data Analytics is a major step in Data Science that involves analyzing Big Data and extracting insights and is prepared in the form of charts, graphs, and diagrams. To step into Data Science, you need to start with Data Analytics.

Conclusion

By now, you have come across the fact that both domains offer excellent career options, with Data Science making it a more rewarding one.

To make a career in this domain, go with an online training course from an accredited institute and forget about what to study and how to arrange the study material. It is all set for you by the training providers. You get lifetime access to the study material and get to work on real-life projects.

They train you according to the level of your knowledge and provide job assistance as well. You can get a self-paced learning option and a choice in the mode of learning too. That means you can choose to have online training, instructor-led, or blended learning.