The media update team dive into what data science is, and how it goes about picking apart the sea of data for information and insights.

The components of data science

According to Avantika Monnappa, marketing content lead for certification training provider Simplilearn, data science is a combination of mathematics, programming, problem-solving, and data capturing in “ingenious ways”. It is also the ability to find patterns, along with cleaning, preparing, and aligning data.

“Data science is a field that encompasses anything related to data cleansing, preparation, and analysis. It is an umbrella term for techniques used when trying to extract insights and information from data,” Monnappa says.

According to analytics and software solutions company SAS, data scientists are a “new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved”.

“They are part mathematician, part computer scientist, and part trend-spotter.”

How data science is used in AI

Data mining and data wrangling are both needed for data scientists to do their job, which sees them using artificial intelligence (AI) tools to find the insights they are looking for.

What is interesting about data science as an occupation is that it does not necessarily need a degree to get into the field, according to Forbes contributor and author Meta S Brown. Skills in maths, statistics or operations research, business, political science, or many others, can be leveraged as long as they are supported by a base knowledge of mathematics and programming.

It appears data science will continue to have a role to play as data and AI evolve, and its complexities multiply.

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The increasing intelligence of AI has a lot to do with how neural networks are being applied within the field. Read more in our article, What are neural networks in AI?