Confused, what is the difference between Data Science and Decision Science? What are the roles of a Data Scientist and Decision Scientist? Well, this article has got you covered.
Table of Contents
- What is Data Science?
- What Exactly is Decision Science?
- Key Differences Between Data Science and Decision Science
- Concluding Lines
What is Data Science?
Data science is the field of computer science that helps in providing actionable insights from large chunks of data. Data Science developers use several techniques such as predictive analytics, statistics, and machine learning algorithms to interpret data for a better decision-making process.
In simpler words, we can say that Data scientists estimate the unknown by writing complex algorithms and building statistical models.
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What Exactly is Decision Science?
In most simple words, Decision Science is a toolbox of approaches that helps you solve problems analytically.
The Decision Scientist takes a 360-degree view of the business challenge and takes into account the type of analysis, visualization methods, and behavioral understanding to assist a stakeholder in making a precise decision.
The task of a data scientist is to extract valuable insights from structured and unstructured data, whereas Decision Scientist is responsible for making those insights usable.
Key Differences Between Data Science and Decision Science
- View on Data
Data scientists analyze data as a tool for innovation. They interpret and analyze situations to build better results and encourage data-driven decision-making.
Decision Scientists consider data as a tool that inspires better decisions. They are responsible for figuring out several ways of analyzing data to resolve the business challenges of clients.
Here it is important to note that data is equally important for both, yet the mechanisms are quite different. For instance, Data Scientists focus on finding insights, whereas Decision Scientists focus on revealing those insights.
- Purpose
Data Scientists reveal insights from structured/unstructured data that are further applied to the benefit of the various industries. These are skilled professionals proficient in applying technology, mathematics, and statistics to well-defined business problems.
Decision Scientists focus on uncovering data-driven insights. They hold exceptional knowledge in the business space along with proficiency in technology, mathematics, and statistics.
Another point of difference is that Decision scientists don’t really work with big data, but data scientists are committed to working with big data.
- Applications
Data Science is applied across various industries, including banking, finance, manufacturing, transport, e-commerce, education, travel, agriculture, etc.
Decision Science is usually applied to business and management-related activities, environmental regulation, military science, public health, and policy.
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- Aim
A data scientist begins with the data by concentrating on the former data trends in order to make the best decision, whereas a decision scientist begins from values, focusing on what clients want tomorrow in order to obtain the best decision.
A data scientist aims to create a framework provided to a machine, whereas a decision scientist provides a framework for decision-making to humans.
- Challenges
There are some of the challenges associated with each of these. Data Scientists have to deal with a tremendous amount of data, development of sourcing, and issues related to data security. And when it comes to Decision Science, there is a need for complex knowledge of maths, analysis, the complexity of the techniques applied, lack of reliable data, and issues while dealing with complex data environments.
Concluding Lines
This has led us to the end of our discussion. Do remember that although the job roles of each of them intersect, but none is a subset of the other. And these two diverse yet interconnected jobs help companies and businesses to recognize the full potential of data-driven decision-making and build a winning approach.
Hope this guide has helped you in understanding and distinguishing data science and decision scientists.
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