Which is better data science or big data? | Does Big Data require coding?

That one is always superior than the other is not correct. There are two independent areas called "data science" and "big data," each of which has its own set of methods and resources.

In the subject of data science, organised and unstructured data are mined for information and insights using scientific techniques, procedures, algorithms, and systems. It includes analysing data and making predictions based on it using statistical and machine learning approaches.

Large and complicated datasets that are challenging to analyse using conventional data processing methods are referred to as "big data." It includes storing, processing, and analysing vast volumes of data utilising distributed systems and specialised technology.

Big data and data science may be utilised in conjunction to address a range of issues. Big data may be used by the discipline of data science to identify patterns, trends, and relationships that may not be immediately obvious in smaller datasets. On the other hand, data scientists may be able to deal with larger and more complicated datasets with big data technology than they would be able to do using conventional data processing methods.

Coding is required for huge data?

Yes, coding is a common part of working with large data. Large volumes of data will probably require the usage of specialised technologies like Hadoop, Spark, and NoSQL databases in order to be stored, processed, and analysed. In order to use these technologies successfully, programming knowledge is frequently required.

For instance, to process data using Apache Spark, you could need to write code in Java, Python, or Scala, or you might need to write SQL code to query data held in a NoSQL database. The usage of computer languages like Python or R may also be required in order to conduct statistical analysis and create machine-learning models using your data.

Having said that, not all jobs in the big data industry necessitate highly developed coding abilities. Depending on your employment responsibilities, you might just need to be able to code enough to extract data and carry out simple data manipulation operations. The ability to design unique tools and solutions for working with big data, however, might be advantageous for people who work with it with and analysing large datasets.

Which area of data science has the highest pay?

Given that incomes can vary widely based on a person's degree of education, experience, talents, location, and the particular business they work in, it is impossible to tell which area of data science is the best paid.

In spite of this, the discipline of data science often pays well. In 2020, the typical annual compensation for data scientists was $121,000, according to a poll by O'Reilly Media. The pay for some specialised positions in the field of data science, such data architects and data engineers, may even be higher.

When picking a professional path, it's crucial to pick a subject that you are enthusiastic about and that fits with your interests and talents. Salary is not the only thing to take into account. However, it is important to note that people with the right training and expertise may pursue a rewarding career in the data science profession, which is in great demand.