What is the Difference Between Big Data and Data Science?

The debate between Data Science and Big Data is raging. In recent years, the Big Data and Data Science hype has gained quite a popularity. According to the industry’s growth tendency, Data Science and Big Data analytics are the data segment’s future. Both professions are valuable and can prosper. Big Data is a massive amount of information collected in structured and unstructured forms, but uncovering the underlying information requires different stages and processes.

As a result, extensive data can only be processed for commercial decision-making with data science. Data Science handles big data processing, analyzing, and presentation to provide relevant insights. Each is unique, with its meaning and relevance. Enroll in the best Data Science course online to learn more about this exciting field.

What is Big Data?

Big Data refers to large amounts of data that cannot be efficiently processed with the typical applications already in use. Big Data processing begins with raw data that has yet to be aggregated and is frequently impractical to store in the memory of a single machine.

Big Data is a buzzword that refers to massive amounts of unstructured and structured data that can inundate an organization daily. Big Data is utilized to gain insights to improve decisions and strategic business choices.

What is Data Science?

Data Science is the study of unstructured, structured, and semi-structured data. It entails activities such as data cleaning, data processing, data analysis, and much more.

Data Science combines statistics, mathematics, programming, and problem-solving; gathering data in novel ways; the capacity to see things from new perspectives; and the process of cleansing, preparing, and aligning data. This umbrella phrase refers to various strategies to extract data insights and information.

Data Science vs. Big Data : Comparison Table

Let’s compare Big Data and Data Science.

1. Meaning

Big Data: Massive amounts of data that are too complex and large to be stored and managed by standard data processing technologies. Big Data refers to all sorts of data that aid in presenting the correct information, to the suitable person, in the appropriate quantity, to help make intelligent decisions.

Data Science: Data Science is an area of study that encompasses all aspects of data, including how to use enormous data effectively. It is the primary strategy for realizing the promise of Big Data.

2. Purpose

Big Data: Analysts’ capacity to examine massive and intricate datasets was previously unattainable. The purpose is to assist firms in creating new growth opportunities or achieving a significant edge over traditional business approaches. This is the actual value of Big Data.

Data Science: Data Science strives to capitalize on Big Data’s potential by developing new data formats, ideas, tools, and algorithms.

3. Applications

Big Data: It is usually used for business goals and client happiness. Big Data applications include research and development, health and sports, telecommunications, etc.

Data Science: It is primarily used for scientific objectives like internet searches, digital advertising, risk detection, etc.

4. Approach

Big Data: Businesses may use Big Data to track their market presence and build agility.

Data Science: It makes business decisions by combining mathematics and statistics with programming skills, which aids in creating a model to test the hypothesis.

Salary Insights on Big Data and Data Science

The other most sought topic is Big Data vs. Data Science salary. In India, a Data Scientist’s starting compensation is roughly Rs. 4.5 Lakhs per year with at least one year of experience.

In contrast, a Big Data Analyst’s average yearly income is Rs. 7.2 LPA, with incomes ranging from Rs. 3.2 LPA to 18.2 LPA. The jobs of Data Scientist and Big Data Analyst may sound similar, but they are not.

Big Data and Data Science - Similarities Big Data and Data Science are often used synonymously. Big Data is a subset of Data Science. Both of these areas deal with data. A Data Scientist is required to manage large amounts of unstructured data.

Yet, the line between Big Data and Data Science has become increasingly blurred in recent years. This is due to the inclusion of data analytical engines in current Big Data platforms such as Spark and Flink.

Mahout, a data analytics engine with Machine Learning algorithms, is now available on older systems such as Hadoop. As a result, the Big Data platform is finished and includes all data science tools.

Conclusion

In this article, we compared and contrasted Data Science and Big Data analysis, concentrating on concepts such as definition, application, talents, and remuneration associated with the specific profession.

Do you plan to pursue Data Science, Big Data, or Analytics as a career? If so, you can enroll in AlmaBetter’s Full Stack Data Science course. You can also select from various courses available in these subjects to benefit from the in-depth knowledge given.