What Does a Data Scientist Do?

There are many ways to describe what data scientists do. From a functional point of view, they create procedures to collect, store, organize, maintain, and analyze data. They interpret what all the figures mean, explain how they impact a business, and aim to predict the future based on given data. They may also be called on to build apps that can process the data they work with.

But ultimately, the most valuable output a data scientist delivers is the narrative that communicates findings to stakeholders so they can see the big picture and direct the people concerned to plan and act accordingly. Efficient data scientists can help organizations and their employees make sense of all the numbers.

What Skills Should a Data Scientist Have?

Data science requires the following characteristics from practitioners, at the very least:

• They must be knowledgeable in statistics and statistical procedures.
• They need to be very good with at least one programming language. Data scientists are required to do intensive data logging, and will most likely be asked to create data-driven applications.
• They must be very good at using database management tools to extract, transform, explore, and wrangle data whichever way necessary.
• The only way they can keep up with the massive amounts of data generated by systems these days is by employing artificial intelligence (AI) to do most of the grunt work. So, data scientists must have a good handle on machine learning (ML) and deep learning.
• They must have a good grasp of big data processing frameworks. They also need the ability to select the most efficient and appropriate approaches to data analytics-related problems.
• They should be proficient communicators. The ability to effectively visualize data is, therefore, a must for them.
• Of course, all those require data scientists to have top-notch analytical ability.

What Tools Does a Data Scientist Use?

The fundamental tools used in data science include:

• Python or R or both. Python is a programming language that data scientists can use to create custom algorithms. R is also a programming language, but unlike Python, it was explicitly designed for data science. Both systems are capable of handling statistics and ML.
• A deep learning tool such as Keras that is useful for those who handle a lot of data or are after the state-of-the-art in AI.
• A data visualization tool.
• A good, old-fashioned spreadsheet.

Why Are Data Scientists in Such High Demand These Days?

Many companies seek the services of data scientists because they are keen on unlocking secrets that may lie hidden in their business data. These secrets could lead to obtaining significant competitive advantages. As more businesses adopt big data and analytics, the demand for people to help make sense of these (data scientists) also increases.

To date, however, the supply of data scientists does not appear to be keeping up with the growing demand. Many companies are finding it hard to recruit for this role.

Data Scientists Are in Demand Now, but Will They Still Be in the Future?

The role of the data scientists quickly evolves. The term itself may even be going obsolete. Why? More and more businesses are seeking specialists who can extract knowledge and generate insights and tackle business-critical and complex challenges, rather than general practitioners with basic skill sets.

Technological developments may also be threatening this role. As new systems capable of automating many of the tasks data scientists currently perform are created, they will need to up the ante.