What Does a Data Scientist Do?

Virtually every business that you interact with digitally—whether that be a streaming service, a weather app, or a social media site—is collecting data on how you use their services. But for that data to be beneficial to an organization, it has to be carefully tracked, maintained, and then analyzed. That’s where data scientists come into play. Data scientists specialize in taking in data that an organization has collected and transforming it into actionable insights. This could be something as straightforward as identifying the kinds of demographics that a new app is being used by, or, in the field of medicine, as complex as determining the most effective version of a new treatment for a disease.

As more industries become increasingly data-driven, the degree to which they depend on data science similarly increases. Want to find out more about what data scientists do and how you can become one too? Then you’re in the right place. Read on!

Data scientists have a plethora of options available when it comes to pursuing a career, as data science is used widely across many industries. Many data scientists choose to go into a field that mirrors their own interests. Do you like the idea of helping a company become more efficient and better serve its customers? A data scientist can do that. Is helping a government agency protect its community something that appeals to you? A data scientist can do that too! Data can help healthcare practitioners better treat their patients, sports teams better perform, and so much more—the possibilities are almost endless, and so are the career opportunities.

Data scientists perform a diverse array of daily tasks, but their primary task is to build models based off of unstructured data, the majority of which is pulled from smartphones, applications, and other digital sources. Data scientists are also often tasked with a number of tasks, including the following:

• Building data models designed to predict results

• Identifying data trends that reveal important details

• Employing industry-standard programming languages such as Python

• Using machine learning to improve their organization 

• Communicating findings with stakeholders

Data scientists need to develop both hard and soft skills to succeed in their field. The above daily tasks require an assortment of hard and technical skills, including the following:

• Statistics: A knowledgeable data scientist must be able to review and understand data, including analyzing data patterns so that any irregularities in the data will stand out.

• Programming: As noted already, having a full understanding of at least one programming language is the standard for data scientists, so capable data specialists are able to understand and use modern programming techniques.

• Machine Learning: Using assorted data-driven algorithms and statistical models to help a computer system adapt to incoming data.

• Computer Science: Skilled data scientists use many computer science techniques, such as databases, artificial intelligence, statistical analysis, and numerous engineering processes.

• Visual Techniques: Your findings are worthless if you can’t find a way to present them to non-technical stakeholders. That’s where data visualization comes into play. With graphs and charts, you can corral your data into a compelling narrative that supports your findings.

In addition to these extensive hard skills, data scientists need soft skills for their careers. Important soft skills include:

• Critical Thinking: Being able to take a step beyond what is clearly visible is an important skill for a data scientist to possess. 

• Inquisitive Nature: Most data scientists are able to better serve their organization by having a natural instinct to better themselves and look for new solutions to even problems that already have a known fix.

• Communication: One of the greatest barriers to success is always the ability to communicate. A successful person in just about any industry is able to communicate clearly with others on their team.

• Data Minded: While a healthy interest in data is a natural part of a career in data science, having a genuine affection for data-driven content can help make the work infinitely more fulfilling.

Many of the business sectors that use data science are driven by “big data”—data that has a lot of variety, is presented in massive quantities, and is delivered quickly. Being able to take in such large amounts of data, absorb it effectively, and then repurpose that information for the betterment of the company is, more often than not, the stated goal of a data scientist job posting. Thankfully, there are several programs that are designed to assist a data scientist in analyzing big data, such as Hadoop and Apache Spark. It is therefore recommended that anyone interested in starting a career in data analytics should also familiarize themselves with big data itself and these helpful programs.

There are many reasons to consider a career as a data scientist. First and foremost is the monetary advantage offered by a career in data science. According to Glassdoor, the average salary for a data scientist is around $150,000. In addition, data scientists often enjoy a substantial amount of job satisfaction. Their work is integral to making critical decisions, so data scientists rest easy knowing they’re bringing positive change on a daily basis.

As the business world becomes increasingly digital, the demand for data scientists grows. Even better, increased access to digital tools has also expanded into training and education. This means finding the training needed to become a data scientist can be as painless as a few clicks. The University of South Florida in particular offers a stellar bootcamp that provides you with all you need to start your career in data science. Their mentor-driven, results-oriented bootcamp is the perfect way to kickstart your career in data. Sign up today and start down your career path toward becoming a data scientist.