What Skills do you Need as a Data Scientist?
Signzy, a Bangalore-based fintech company that makes regulatory processes for banks, NBFCs, and other financial institutions simple, secure, and compliant.I want you to close your eyes and imagine the image of a data scientist. When people think about data scientists, they tend to imagine a picture of a man in a lab coat who maybe hasn't slept for a while, doing AI applying algorithms and analyzing data for industrial commercial use. But in reality, the profile of a data scientist requires the skills of a person whose has expertise in different things. A person who embodies entrepreneurial spirit and curiosity known as Da Vinci.
Since the dawn of humanity, until 2003,5 exabytes of data were created, and today, we are generating the same amount of data every two days. That's a huge amount of data. But the good news is that the technology today has newer ways to handle the process and analyze all the vast amount of information.
With the help of Artificial Intelligence and machine learning, big data is being used for a number of things. For better profiling users, for doing personalized recommendations, improving healthcare and diagnosis, for predicting political revolution, preventing crime and even creating fine art. It's clear that big data enhances human potential.
When there's a social debate like in the months after Brexit,we can analyze the data and understand and map where the social debate happens and what communities were created. We can see how the remainders were opposed to the live community how the media community was seated in the middle kind of neutral. How there was a technical community who were the people talking about the economic and social and political implications of the Brexit were
closer to the menainer community. We can also see how the US Republicans look like they supported Brexit.
As for fashion, we can even predict trends by analyzing the photographs that people share on Instagram with the hashtag OOTD (outfit of the day). We collect all the hundreds of thousands of photos and we can analyze the underlying influence of people sharing those photos because we can see who interacts with whom and then see what patterns emerge in the photos that the most influential people share. It's a paradigm shift where we used to make decisions based on intuitions and guesswork now we can manage based on evidence and we can move to data-driven decisions.
"The most important skill of a data scientist is asking the right question about data"
Management guru Peter Drucker said,"You cannot manage what you cannot measure". Now there's no excuse. You can measure and you can manage. Without the help of AI, machine learning, and all the Big Data technology, we will not be able to handle this data revolution. But it still is the most important element in driving insight out of data,is what makes a data science irreplaceable. It is the human factor.
The key to turning data into insight lies in what we can do that machines can’t. Curiosity. We all have access to Google, with billions of data points in Google, but it's your curiosity that determines what you learn and what you search for, and therefore, also what you filter and how you discover what is relevant to you.
Empathy is the key to connect with others and to understand what other people need. Creativity is the key to invent and articulate solutions to solve problems. Communications is the key to persuade to influence and to spark ideas that create change. And leadership is the key to step up and move all these people to action. And, at the center of these all, is curiosity. Because it is the curiosity for emotions, that drives empathy. It is curiosity for ideas that drive imagination, it is the curiosity for solutions that drive creativity, it is the curiosity for influencing that drives communication. And it is the curiosity for results that in the end, that drives leadership.
The most important skill of a data scientist is asking the right question about data. It is the curiosity of a data scientist that make them ask those right questions, to iterate and to understand human issues to imagine the possibilities to create an articulate solution, to convey the message and the insights with the right visuals to make them actionale. Those are the key elements that turn data into something meaningful. It is a set of skills that no artificial intelligence can match. It is the intersection of technology and liberal arts where the magic happens. If the renaissance of 14th and the 17th century was a force that drove humanity of the middle ages into the modern era, the big data renaissance is already unveiling endless possibilities to push human race forward with the power of data. It is up to us to imagine what data and machines will do for us.It is the curiosity of a data scientist makes them ask those right questions. It is important to remember that a fool with a tool is still a fool. Big data is not only about technology. Big data needs big brains, big data needs the curious brain of an artist to make a difference.
As for fashion, we can even predict trends by analyzing the photographs that people share on Instagram with the hashtag OOTD (outfit of the day). We collect all the hundreds of thousands of photos and we can analyze the underlying influence of people sharing those photos because we can see who interacts with whom and then see what patterns emerge in the photos that the most influential people share. It's a paradigm shift where we used to make decisions based on intuitions and guesswork now we can manage based on evidence and we can move to data-driven decisions.
"The most important skill of a data scientist is asking the right question about data"
Management guru Peter Drucker said,"You cannot manage what you cannot measure". Now there's no excuse. You can measure and you can manage. Without the help of AI, machine learning, and all the Big Data technology, we will not be able to handle this data revolution. But it still is the most important element in driving insight out of data,is what makes a data science irreplaceable. It is the human factor.
The key to turning data into insight lies in what we can do that machines can’t. Curiosity. We all have access to Google, with billions of data points in Google, but it's your curiosity that determines what you learn and what you search for, and therefore, also what you filter and how you discover what is relevant to you.
Empathy is the key to connect with others and to understand what other people need. Creativity is the key to invent and articulate solutions to solve problems. Communications is the key to persuade to influence and to spark ideas that create change. And leadership is the key to step up and move all these people to action. And, at the center of these all, is curiosity. Because it is the curiosity for emotions, that drives empathy. It is curiosity for ideas that drive imagination, it is the curiosity for solutions that drive creativity, it is the curiosity for influencing that drives communication. And it is the curiosity for results that in the end, that drives leadership.
The most important skill of a data scientist is asking the right question about data. It is the curiosity of a data scientist that make them ask those right questions, to iterate and to understand human issues to imagine the possibilities to create an articulate solution, to convey the message and the insights with the right visuals to make them actionale. Those are the key elements that turn data into something meaningful. It is a set of skills that no artificial intelligence can match. It is the intersection of technology and liberal arts where the magic happens. If the renaissance of 14th and the 17th century was a force that drove humanity of the middle ages into the modern era, the big data renaissance is already unveiling endless possibilities to push human race forward with the power of data. It is up to us to imagine what data and machines will do for us.It is the curiosity of a data scientist makes them ask those right questions. It is important to remember that a fool with a tool is still a fool. Big data is not only about technology. Big data needs big brains, big data needs the curious brain of an artist to make a difference.
