Data science careers are on the cutting edge of new technology, using their skills to help organizations solve complex problems in designing, marketing and selling products and services.
The emergence of Big Data has led to data science, a relatively unknown profession just a decade ago, becoming one of the most important positions in businesses across many industries. It’s also becoming increasingly important to nonprofit and government agencies.
With training in mathematics and statistics, many data scientists write the algorithms used to analyze large sets of data, looking for trends and correlations between two or more data points.
While automation is taking over many routine tasks, data scientists still play a prominent role in not only analyzing data but developing recommendations for business leaders on how that data can be used.
The Rise of the Data Scientist
The importance of data scientists is in direct relation to the rise of big data. With more data at their disposal than ever before, organizational leaders turn to data scientists to develop methods for putting that data to use.
The overall goal is to leverage trends found in data to make operations more efficient as well as provide better products and services to consumers.
Some uses of data science and predictive analytics are well known to anyone who uses services such as Amazon or Netflix. Both use data from millions of customers to create models that predict what a viewer might be interested in, based on past viewing habits.
The results are shown to customers as recommendations. While off-base in many instances in the early days of the internet, the increased sophistication of data science has made those recommendation more accurate than ever.
In a more serious arena, healthcare organizations use data science and predictive analytics to provide better care for patients. Based on a number of factors, including age, medical history and the nature of a condition, healthcare data scientists can aid medical professionals in identifying high-risk patients who require specialized care.
While data science has made its way into many types of industries, there are many variations in what a data scientist does on the job.
Variations in Data Science Jobs
Depending on the industry and the type of job, the work data scientists varies. Here are a few examples of typical data science jobs.
Numbers cruncher. Data science is ultimately about statistics. Expert-level knowledge in statistical strategies is key in this area, including hypothesis testing, confidence intervals, quantitative research, data visualization and analysis of variance.
Modelers. Strong skills in mathematics drives this group, who develop data models to tackle complex challenges. Examples could include analyzing stock price movement, drug trial results and modeling the movement of planets, galaxies and other celestial bodies for scientists.
Engineer. In some jobs, data scientists work as engineers, using coding and existing data libraries to write programs for a number of common business uses, such as using machine learning models to automate monthly report generation.
Machine Learning. Data scientists increasingly work with machine learning as artificial intelligence becomes more sophisticated. So data scientists specialize in working with software that can automate data collection and even provide recommendations on some areas such as pricing and product development.
Actuarial Science. Actuaries have been around for a long time, working primarily with banks and insurance companies. But data scientists can excel in these positions with their detailed knowledge of math and statistics, creating accurate predictive models. Companies in the financial services industry are particularly turning to data scientists for modeling and predictive analytics.
As the potential uses of Big Data expands, data scientists will be in a strong position to enjoy stable and potentially lucrative careers across many different industries.