What is Data Science?
Data Science is a multidisciplinary field that combines scientific principles, analytics tools, and statistical algorithms to derive meaningful insights and hidden patterns from data.
It covers a wide range of specializations, such as mathematics, statistics, programming, visualization, IT infrastructure and data engineering.
Despite all the hype over state-of-the-art machine learning models, the basic aim of data science is to use data to produce as much business impact as possible, regardless of the tools or complexity of the models involved.
Given that businesses have many different functions and that data is wide and multifaceted, it is little surprise that there are a wide variety of data-related roles in the data science domain. While a data scientist has a dominant position, the data team has other important members who play an equally important role in a data science project.
Business Analyst: Responsible for linking data insights to actionable results that increase the profitability and efficiency of the business. This is done through in-depth business domain knowledge and continuous communication with business stakeholders
Data Analyst: Similar to business analysts, data analysts perform technical analysis to generate actionable insights from data and present these findings and trends to business stakeholders in the form of regular reports and visualizations.
Data Engineer: Develop, deploy, manage and optimize data pipelines and infrastructure so that raw data can be extracted, transformed, and loaded into a format ready for downstream analysis and modeling.
Machine Learning Scientist: Builds and deploys machine learning models in mass production and monitors system performance and functionality, focusing on the software engineering aspects that support these models.
Statistician: Apply statistical methods and models in the design, maintenance, and analysis of experiments (such as A/B tests) that meet high statistical rigor
Data Consumers and Leaders: Consume data insights and analytics to drive data-driven decision making for business. They are non-technical stakeholders who have a sound understanding of the fundamentals of analytics, and are able to leverage data insights to support decision making.
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What is the Difference between a Data Scientist and a Data Analyst?
At a high level, the two roles of a data scientist and a data analyst are similar, as they both use data to solve business problems. Because of this, there is overlap of tasks to be performed, such as querying and cleaning up data.
The difference between these two roles lies within the specifics of the types of problems they focus on and the tools and techniques they leverage to achieve solutions.
Data analysts focus on business intelligence by translating data into actionable insights to support an organization's strategic decision making.
Data analysts present these findings in regular reports and dashboards to keep stakeholders updated on the state of the business through key performance indicators. They focus on uncovering insights that describe business trends with an objective focus on the past and present.
Data scientists go beyond descriptive analysis by using advanced statistical techniques as part of a forward-looking approach to predict future outcomes. These predictions are used to drive well-informed business decisions as part of what is described as predictive and prescriptive analysis.
Data scientists design and run various experiments to identify the best models for specific business problems. They also build machine learning pipelines and data products, all focused on the present and the future.
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