Data scientists will remain in high demand in 2019, with businesses in practically every industry wanting to get the most value from their flourishing data resources. As companies increasingly take advantage of their internal data assets and invest in the integration of third-party analytics providers, the job of the data scientist will only grow in relevancy.
Previously, the teams that oversaw data were consigned to the back rooms of an organization, performing integral database tasks to keep the different corporate systems provided with essential information, like data for executive and financial reports.
As the role of data science is increasingly critical, the rising stars of business are those data scientists that are capable of handling vast quantities of data and generating forward-looking insights from that data. These insights help anticipate possible outcomes and address possible threats to the company.
With the growing importance of data scientists in mind, consider the following top skills you need to flourish in the profession.
To be effective as a data scientist, you need programming abilities that comprise two computational aspects: dealing with large volumes of data in real time and managing statistical models such as regression, optimization, and decision trees.
The effect of big data starting in the late 1990s has required that more and more data scientists have a working knowledge of languages like C or Java. More recently, the language of choice in data science has become Python, with R also now having a considerable following.
Data scientist may not be a solid career path for those who don’t enjoy mathematics.
The job of data scientist is to make use of complex mathematics to develop statistical models, which may be used to create or change essential company strategies. An effective data scientist excels at both mathematics and statistics, while having a capacity to explain intricate equations and reassure stakeholders that various statistical models can be trusted.
Very little in technology and data science right now is conducted in a vacuum; there’s always some involvement of various processes, applications, information and users. Due to this reality, being capable of communicating with a number of stakeholders using information is an essential ability.
Data scientists are frequently communicating about the company benefits of information to company executives, such as details about technology resources, the difficulties with data quality and confidentiality concerns.
A data scientist should be aware of what is happening with a data architecture from inception to model to business implementation. Not knowing data architecture can have severe impacts on sample size inferences and assumptions, which can result in bad outcomes and decisions.
Worse yet, things often change inside the architecture. Without knowing how a change influences models to begin with, a data scientist would be vulnerable to suddenly flawed models, and not know why these flaws appeared.
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