Companies need Data Science now more than ever before as the data economy proliferates to usher in the new age – one where data is the driver of everything.
The value that data brings to the table can no longer be ignored – competitive advantage, accelerated pace of innovation, successful new product development, increased efficiencies of people and processes, etc. are the obvious ones. It is but natural to assume that organizations globally will be working hard to improve their data capabilities. It is then hardly a surprise when we see the job of the data scientist being hailed as the sexiest job of the 21st century.
According to a study in Figure Eight, 50% of employed data scientists are contacted once every week for new opportunities. 30% are contacted several times a week, and around 85% are contacted at least once a month. These numbers further illustrate the growing demand for data scientists.
Just like the role of the data scientists is growing in prominence, it is also evolving as data science matures and industries change owing to the technology impact.
So, given the general interest in mind, here’s a look at some of the key skills that an ace data scientist will possess in today’s data-crazy world.
Technical Skills – Statistics and Statistical Programming
It’s redundant to mention that data scientists are highly educated. At least, the ace ones are. 88% have a master’s degree, and 46% have Ph.D.’s.
It is natural to assume that ace data scientists have exceptional statistical knowledge and are excellent at Hypothesis Testing, Probability, Descriptive and Inferential Statistics. Having an intuitive understanding of business statistics is another feather in the data scientist’s cap.
Their technical knowledge and skillset are expansive. Given that different businesses use different tools and languages in their workflow, ace data scientists have to have a strong core of technical skills that can be applied to many problems. In-depth knowledge of analytical tools is a given for ace data scientists. The preferred one being R since R is designed to fulfill the needs of data science. While R does seem to have a steep learning curve, 43% of data scientists use it to solve the problems they encounter.
Python is another programming language popular amongst great data scientists as it provides them with better insights as well as helps them correlate data from large sets of data. Having a great grasp of Python and its libraries is almost like a trademark of ace data scientists
Algorithms are data scientists’ playgrounds. These people compete with things such as logistic regression, decision trees, neural networks, random forest, clustering, and the like. Having a great grasp of machine learning and advanced machine learning knowledge becomes quite imperative to participate, play, and win in this game. A deep understanding of different machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning, and their subsequent algorithms is a hallmark of a good data scientist.
Ace data scientists also have knowledge of neural networks as deep learning models. They know how to create deep learning models and understand how Convolutional Neural Networks, Recurrent Neural Networks, and RBM and Autoencoders work.
Did you expect a laundry list of the technical skillsets in this blog? Well, there are a few things that separate a good data scientist from one who is a rock star. This is one of the differentiators.
Having good business knowledge and deep domain expertise helps them put the data to work. Data-driven problem solving includes understanding the salient features of the situation at hand, assessing how to frame the right questions to get the right answer, evaluating the approximations that make sense, and knowing which resource to approach at the right juncture of the analytics process. A data-driven approach to problem-solving comes with experience and is a venerable weapon in the data scientist’s toolkit.
Having a good business understanding also hones their visualization skills and helps data scientists present their data in a visually appealing format. This helps them communicate better with their end-users as they can use the language of business as opposed to the language of IT
Albert Einstein’s famous words, “I have no special talent. I am only passionately curious” are profoundly relevant in the narrative of a data scientist.
Ace data scientists are curious beings and have an innate desire to acquire more knowledge. And ‘more curious and curious must you get, much like Alice in Alice in Wonderland, to be an ace data scientist. Why? Because 80% of the time a data scientist’s job entails discovering and preparing data based on the asked questions. And it is curiosity that helps them play with the data, push it, wrangle it and twist it, and turn it in multiple ways to get answers that we get ace data scientists.
Ace data scientists have to be ace storytellers because they have to fluently translate their technical findings in the language that the non-technical user, such as the sales and marketing teams, can comprehend. The ace data scientist enables non-technical teams with quantified insights and also understands their needs, problem areas, and desired outcomes to wrangle the data appropriately.
Creating a storyline around the data helps in communicating the findings easily across the organization, making it easy for everyone to enjoy the fruits of data-driven decision-making.
Ace data scientists are all this and more. But above all, they are also team players. They understand that they have to work across teams and people to help develop strategies, create new products, launch better campaigns, drive more sales, or improve business processes.
While we expect to see this upswing in demand for data scientists, we also feel that organizations have to give their workforce the power to glean intelligent insights from data and make data-driven decision making and organization-wide practice. For this, they need citizen data scientists, who are the everyday employees, and give them the capacity to convert data into insights leveraging an intuitive, easy-to-use data science platform. The sky, then, will not be the limit for success.