Many businesses handle a lot of data, increasing the demand for data science and big data professionals. It has made companies find ways, methodologies, technologies, and systems they can use to analyze, process, and present data in an understandable manner. We require professionals like data scientists and big data engineers to achieve this. There has been a big debate about the difference between big data and data science. Data science involves the processes and techniques that scientists use, while big data is a branch of data science that deals with large datasets and methods one can use for its usefulness. The article will cover more details about the two topics providing key differences, skills, applications, similarities, job descriptions, etc. This blog is planned to provide you with a glance to discriminate both these disciplines and aids you in understanding the main terms such as what is big data, why big data, what is data science, why data science. You will also comprehend the applications and skills required to become a big data specialist or data scientist, along with key differences between them. Let’s start exploring the concepts.
Data science is a multidisciplinary field that uses different techniques and practices like data wrangling and data cleaning, among others. It works with other areas like programming, statistics, mathematics, etc. To work on big data to analyze and extract valuable information and insights. It provides ways to discover, extract, analyze, compile, visualize, process, and present data without having an issue with the data size. It is large, complex, and diverse, and you can apply it in all the fields like economics, marketing, medicine, finance, social sciences,geo-location, etc. Big data is a field of data science.
Big data involves dealing with huge data with complexity, and you can't handle it using traditional management tools. You must apply different techniques, systems, and tools to provide insights and usefulness. The data is not in the standard form, and it is hard to provide them in common forms like charts, tabulations, etc. Some scientists use special tools and techniques like parallel programming and algorithms to remove all the data constraints.
There are several classifications of Big data: This includes the following types below:
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The key differences between data science and big data include:
Similarities between data science and big data include:
Data Science vs. Big Data
Data science focuses on scientific activities using different approaches to work on big data to make decisions, while big data deals with large volumes of data that you can't handle using the common methods. Big data is mainly on three Vs-velocity, volume, and variety.
Data science is an area that wants one to have multiple skills like programming, dealing with models, and processing data. It has different techniques that extract information when working with large data sets. One of the biggest advantages of having data science is to help in making decisions.
Big data collects data from different data sources, and the data type is very diverse, and it works with all the formats and types of data.
Applications of Data Science
Big Data applications
Data scientists work closely with companies and businesses by creating models and algorithms and using predictive modeling to help the business performance and meet customers' needs. They are helpful, and they play a big part when making decisions. Some of the job responsibilities of data scientists include:
Some of the job responsibilities of big data include:
There are many skills good data scientists should have. The skills include the following:
Some of the skills of a big data engineer include:
According to payscale.com, data scientists earn $97,000 per year, which can go high as $136k, while big data engineers earn $127,000 to $176,000 per year.
There are several career options for data science. They include data architects, mining engineers, business intelligence analysts, and senior data scientists.
Some big data roles include data engineer, data architect, database manager, database developer, etc.
Data science uses scientific methods to make big data useful using data analysis, filtering, and preparation. It also develops models checking for complex patterns that exist in big data. Developers can also use programming to develop models.
Most big data sources are internet traffic, system logs data, electronic devices data including sensors, data from spreadsheets, databases, email, and transactions, RFID, data from online discussions, data from video and audio streaming, and live feeds.
When businesses collect data, they have to develop strategies and methods they can use to get meaningfulness from it. The company has to develop efficient ways they can use to collect data, process, analyze, visualize and store it. Data science has many processes, while big data use different technologies to make large data useful. The growth of the two fields is on the rise, and there will be a lot of hiring in the coming days. The article has helped you understand the two concepts briefly, and you should now have more knowledge about them when you come across them.
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