To the wide business community, a Data Scientist is one of those “data magicians,” who can acquire disparate data masses from diverse business functions; clean, massage, organize, and prepare the data; and, then exploit their inherent skills in mathematics, statistics, and Machine Learning to uncover hidden business insights and intelligence.
The data used by a Data Scientist can be both structured (metrics or raw numbers) and unstructured (e-mails, images, videos, or social data). As Data Scientists often have to create algorithms to extract insights from such complex data, these “data magicians” are expected to be equipped with a variety of skills and experience levels. Data Science skill development has been an issue of ongoing debate and continues to raise questions about the appropriate training programs available in Data Science.
A Recent RJMetrics Study of LinkedIn Profiles reveals that the number of Data Scientists has doubled in the last four years. According to Glassdoor, Data Scientists enjoy a median salary of $113,000. In a Datanami article titled The Future of Data Science, the continued impact of Data Science on the global business world will primarily depend on advanced analytics – especially using predictive or “real-time” analytics for achieving predetermined objectives like superior products and services, micro-market products, improved customer experience, and reduced operating costs.
Thus, advanced analytics and Machine Learning skills are high on any industry leader’s list of wanted skills when looking for a Data Scientist to hire. The future business environment will also expect more speed of execution with Data Science projects, so future Data Scientists will not only be expected to have traditional math, statistics, and Machine Learning skills, but also sound knowledge of and experience in using data productivity tools, such as those to automate data cleaning or data modeling.
The organization Masters in Data Science recently wrote about a typical day in a Data Scientist’s life,which according to them revolves around a number of particular tasks, including:
Observations on Data Science Skill Development A late 2014 article at KDNuggets explored the most important skills needed in Data Science, which included advanced degrees, knowledge of SAS or R, Python coding, knowledge of Hadoop platform, knowledge of SQL, and Cloud Computing among them. All have been identified as essential skills for an aspiring Data Scientist breaking into the industry. A 2016 DataStax survey has added to that list NoSQL expertise.
Along with the above skills, a Data Scientist will also need to demonstrate strong communication skills to articulate problems, business knowledge or domain skills, and intellectual curiosity to be able to do the job successfully.
Quora Analyzes Data Science Skill Development When Quora analyzed the issue of finding qualified employees trained in Data Science, the most critical skills needed to become a successful Data Scientist were identified as a curiosity about data and a deep interest in the domain. The ideal Data Scientist, according to the article, must demonstrate a passion for the subject and an intellectual inquisitiveness about the data to be able to make an impact on the job.
