Data science and its Scope

What is Data Science?

In Simple Terms, Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning, and big data. By combining all these, Data Science uses advanced algorithms and scientific methods to extract information and insights from large datasets – both structured and unstructured.

What is a Data Scientist?

Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.

Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, and skepticism of existing assumptions – to uncover solutions to business challenges.

A data scientist’s work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.

Who Can Become a Data Scientist?

A data scientist needs a technical background in computer science, statistics, and programming. Having these foundations prepare you for the big game ahead. Your ability to think, test, analyze, and build systems evolves the analytical mindset.

To be able to program data, you need to understand the data and possibly have a good knowledge of data structures and algorithms. Machine learning is a vital skill for a quintessential data scientist, likewise, knowledge of software programming languages like Python and R makes you a good Data Engineer, Data Scientist, Machine learning developer, and so on.

What are the 4 Pillars of Data Science Expertise?

While data scientists often come from many different educational and work experience backgrounds, most should be strong in, or in an ideal case be experts in four fundamental areas. In no particular order of priority or importance, these are:

  • Business/Domain
  • Mathematics (includes statistics and probability)
  • Computer science (e.g., software/data architecture and engineering)
  • Communication (both written and verbal)

The scientist that is truly an expert in all, then you’ve essentially found yourself a unicorn.

Based on these pillars, my data scientist definition is a person who should be able to leverage existing data sources and create new ones as needed in order to extract meaningful information and actionable insights. A data scientist does this through business domain expertise, effective communication and results in interpretation, and utilization of any and all relevant statistical techniques, programming languages, software packages, and libraries, and data infrastructure. The insights that data scientists uncover should be used to drive business decisions and take actions intended to achieve business goals.

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