Data science plays an important role in ranking your Google search results. It helps LinkedIn suggest professionals who might be in your network, or BuzzFeed decide what articles you may like. The field is having an impact on all industries from agriculture to education and retail. And yet, very few people can answer what is data science. If you’re looking for an answer to the question – what is data science, then this blog provides an overview of the answers given by 35 data scientists to a podcaster.

What Is Data Science? The Simple Answer

In my book, ‘How to Become a Data Scientist’, I define data science as ‘a dynamic and growing field that lies at the crossroads of other fields like statistics, computer science, and business management. It refers to processes and methods that help us make sense of large volumes of data for organizational purposes.’

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Of course, this is a simple theoretical answer. Practically speaking, data science includes the frameworks is using to develop their product. These frameworks are online. They are huge. And at the end of the day, they are experiments! BuzzFeed uses data science to improve the titles and click-worthiness of their articles. AirBnB uses data science to make key enterprise and product decisions. Clearly, data science is different from industry to industry and varies with the types of businesses and their objectives.

From the data scientist’s perspective, data science is the work done to carry out serious analytics. This includes conducting experiments on digital platforms, and other processes that help organizations grow in a steady fashion. Data science takes up the responsibility of personalizing products using information that flows through machine learning pipelines to learn more about customers, their preferences and make smart decisions in line with insights.

What Is Data Science According to Data Scientists?

Not Just Machine Learning & Artificial Intelligence

One of the major myths that data scientists want to burst is that their field isn’t all about cars that drive themselves or general artificial intelligence. Machine learning and deep learning that are instrumental in creating such technologies claim much attention. And, they arguably deserve this too. But data scientists are quick to assert that much of their every day work revolves around less glossy and more mundane tasks such as gathering data, preparing it for analysis, drawing statistical inferences, creating reports, visualizing data and of course communicating their findings with a variety of parties to help decision making. All of this is quite far from the rosy pictures of machine learning and artificial intelligence that people like to portray.

Communication Skills More Valuable Than Deep Learning Experience

Data scientists are dynamic professionals as their field is fast-changing. The skills that are in-demand today may not be so necessary tomorrow. This is because data science tools are evolving at a rapid pace. They are automating a lot of data science tasks including machine learning and deep learning. It is helping data scientists reduce the time they spend on tasks like data gathering or cleaning that involve no analysis. Data scientists spend roughly 80% of their time on such tasks. And believe it or not, in this environment, it is the ability to communicate insights – perhaps using a PowerPoint presentation even – that is most valuable on this job.  Even more than experience in deep learning according to a leading data scientist.

All About Questions Not Techniques

It is essential to focus on the questions of the right kind in data science, not the techniques. Techniques change with tools and technology and as per requirements. What must remain with the data scientist, however, is the ability to think critically, and more importantly quantitatively without forgetting the context. The ability to ask the right questions in a domain and to quantify solutions is key in data science.

Specialized Field

Data scientists walk different paths. There is no one way to work or progress in your career as a data scientist. Most data scientists are generalists – not specialists. They perform three types of tasks: 1) tasks that accrue business intelligence, 2) tasks that help organizations make decisions and 3) tasks that help make machine learning models and implement them. But this is changing. Data scientists of the future will be expected to specialize in one of these sets of tasks. They will be expected to bring in-depth knowledge to the table and really put their mastery to use for the benefit of the field.

Clouded by Ethical Uncertainty

Data science lacks no standard practices. Not all data scientists speak the same language or think alike. Plus, there isn’t proper ethical regulation or consensus among professionals about the rights and wrongs of data science. Hence, the field is vulnerable to ethical violations and their worrying consequences. Social and cultural biases can easily find their way into data systems. For instance, racism could become inherent in a system trying to gauge the risk a citizen or immigrant poses. The trouble in such cases is the lack of transparency in the way deep learning functions. Professionals are now aiming to make machine learning more interpretable so that biases can be eliminated. Whether this will prove to be effective, however, remains to be seen.

So, What Is Data Science?

Data science is much more than machine learning or artificial intelligence. It is quickly evolving with rapid advancements in tools and technologies. Data science is about asking the right questions to discover quantified solutions that can help decision making in organizations. It is a field that is suited from professionals with the ability to think critically and communicate effectively.

Although data scientists pursue different career paths, the field is quickly becoming specialized. Data scientists of the future will be expected to focus on either gathering business intelligence, mastering decision science or being machine learning developers. Despite the quick progress the field is making, data science is still unpredictable. It is still clouded by unethical uncertainty. But with the advent of machine learning models that can be interpreted, data science has the potential to become more transparent by eliminating biases in the ways machines learn about humans.

That’s the gist of the answer that data scientists gave to the question – what is data science? You are now up to speed with the latest in this new and thriving field. Data science never stays still however. We can expect it to change rapidly and must pay close attention to what it comes to mean for different data scientists in the future. We, at Acadgild, will most definitely be following this field closely. So, if you’d like more updates and insights into data science, make sure you subscribe! As always, we wish you happy learning.

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