The data scientist role is an offshoot of the statistician role that includes the use of advanced analytics technologies, including machine learning and predictive modeling, to provide insights beyond statistical analysis. The demand for data scienceskills has grown significantly in recent years as companies look to glean useful information from the voluminous amounts of structured, unstructured and semistructured data that a large enterprise produces and collects — collectively referred to as big data.Roll
Data scientist roles and responsibilities
Data scientist topped the list of 50 Best Jobs in America by Glassdoor.com in 2016 and again in 2017, based on metrics such as job satisfaction, number of job openings and median base salary.
Basic responsibilities include gathering and analyzing data, and using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets.
A data scientist uses large amounts of data to develop hypotheses, make inferences and hone in on customer, business and market trends. The data scientist must be able to communicate how to use analytics data to drive business decisions that may include changing course, improving a process or product, or creating new services or products. In the case of software, for example, the data scientist’s role involves using data analytics to prescribe new features.
Data scientists also set best practices for data collection, use of analytics technology and data interpretation.
Data scientist skills
Soft skills required for data scientists include intellectual curiosity combined with skepticism and intuition, along with creativity. Interpersonal skills are also a critical part of the role, and many employers want their data scientists to be data storytellers who know how to present data insights to people at all levels of an organization. They also need leadership skills to steer data-driven decision-making processes in an organization.
The education requirements for data scientists typically include abachelor’s degree in statistics, data science, computer science or mathematics.
Hard skills required for the job include data mining, machine learning and the ability to integrate structured and unstructured data. Experience with statistical research techniques, such as modeling, clustering and segmentation, is also often necessary.
Data science requires knowledge of a number of big data platforms and tools, including Hadoop, Pig, Hive, Spark and MapReduce, and programming languages that include structured query language (SQL), Python, Scala and Perl, as well as statistical computing languages