Race is on for the data science maestro Posted on : Oct 04 - 2018

A skilled data scientist needs a wide range of skills, advanced qualifications and several years of business experience.

Effective data science is becoming the differentiator between business success and failure, but few have the broad range of skills and expertise needed to deliver on the promise of data science.

It is both an art and a science, demanding a range of seemingly disparate skills. Much like a conductor must coordinate and guide a performance to deliver a meaningful final product, the data scientist must apply a range of toolkits and skills to not only interrogate data, but ask meaningful questions that deliver true value, and then ensure the data product drives meaningful business change.

These maestros of data science are in short supply. Both globally and in South Africa, forward-thinking enterprises are looking to harness skilled data scientists to drive more than just competitive edge; they need their insights to help companies reinvent, innovate and disrupt into the future.

Data science transforms data, extracts true business value from it and derives it into a data product. In straightforward terms, a data-driven product is software, a service or platform that is able to solve deeply complex problems and provide actionable insights by utilising internal or external data and a number of different machine learning algorithms.

But finding a skilled data scientist is easier said than done. A true data scientist needs a wide range of skills to deliver on the promise latent within enterprise data: they need advanced qualifications, preferably starting with a Masters or PhD in computer science or quantitative fields.

In addition, they need several years of business experience, preferably at a strategic level. They also need certain personality traits: curiosity, creativity, innovative flair and an entrepreneurial mindset, as well as the ability to negotiate and collaborate with people at all levels of the business.

They must be au fait with prescriptive analytics, predictive analytics, descriptive statistics, pattern recognition, data visualisation, data preparation, hypothesis testing, time series models and Bayesian models, and need skills in the use of a range of computing tools.

Data science transforms data, extracts true business value from it and derives it into a data product.

Without this combination of skills and attributes, data scientists cannot deliver on what business needs them to do: pose the right questions, find the right answers, and use the findings to effect measurable change in the business. View More