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Data Science vs Decision Science: Which One You Should Choose?

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18th Nov, 2019
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Data Science vs Decision Science: Which One You Should Choose?

By now, we’re accustomed to the saying that Data Scientist is the “Sexiest Job of the 21st Century.” As companies across the world realize the potential of Big Data, AI, and ML, the demand for Data Scientists is only skyrocketing. While it is true that Data Scientists are the wizards who help uncover the hidden designs from within massive datasets, nobody is talking about the valuable contributions of the unsung heroes of the tech and business world – Decision Scientists. 

Now, you must be wondering, “what’s the difference between a Data Scientist and a Decision Scientist?”

According to Data Science Central, “Data Scientist is a specialist involved in finding insights from data after this data has been collected, processed, and structured by data engineer. Decision scientist considers data as a tool to make decisions and solve business problems.”

But more importantly, what is Decision Science?

Essentially, the connection between Data Science and Decision Science runs deep. While Data Science integrates math and technology to extract insights from large datasets using analysis, visualization, and mathematical computations, Decision Science is the interdisciplinary application of math, business, technology, design thinking, and behavioral sciences.

Data science aims to extract insights, and Decision Science seeks to transform those insights into actionable business decisions. In the words of Dhiraj Rajaram, CEO of Mu Sigma –

“While Data Scientists are about creating analytics, Decision Scientists help companies consume them.”

Data Science gives prime importance to data and analytics to find meaningful insights for business usage. However, it is Decision Science that helps shape the insights into data-oriented business decisions.

Hence, it is often also referred to as Business Data Science that juxtaposes the instrumental (Data Science techniques and tools), social (business context), and functional (information processing) aspects of a business to create real value out of data.

In light of the increasingly complex and ambiguous nature of the business landscape, the true success of a company can only become tangible when it realizes the value of both Data Scientists and Decision Scientists.

Together, these professionals can revolutionize the business scenario for the better. Data Scientists can take care of the data analytics part, while Decision Scientists can handle the business context of it by converting data into context-specific, objective insights that can promote better and speedy decision-making in organizations. 

Source 

Data Science vs. Decision Science

Let us now dig deeper into the Data Science vs. Decision Science debate by looking into the actions of Data Scientists and Decision Scientists. Since the thought process plays a significant role in influencing and driving action, we hope to shed light on the two emerging fields of study by dissecting the way how Data Scientists and Decision Scientists approach data.

Data Scientists

They consider data as a tool for innovation. Data is for Data Scientists a means to understand, interpret, and analyze situations and things to build better products and encourage data-driven decision making. Hence, Data Scientists place primary importance on data quality, analysis, and statistical methods – business context is secondary for them.

The end-goal of Data Scientists is to gather high-quality data and apply robust statistical approaches to it to facilitate product development. Data quality is something they cannot compromise on since it affects the entire process of product building – the better is the data quality, the better will be the product. 

Data Scientists approach data in terms of data patterns, data processing, algorithms, and statistics. They are particularly obsessed with finding causal relationships, and hence, they often play around with deep analysis and experimental statistics.

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Decision Scientists

They consider data as a tool that drives better decisions. Decision Scientists look at data analysis from the lens of decision-making. They are devoted to finding different ways of analyzing data to solve the specific business challenges of clients/customers. 

For Decision Scientists, the business aspect of the problem comes first. While Data Scientists focus on finding insights through various statistical approaches, Decision Scientists aim to uncover insights that can lead to the creation of best decisions to tackle the business problem at hand. As such, data analysis for Data Scientists is largely dependent on the business decision that needs to be implemented.

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Usually, Decision Scientists need to get a 360-degree view of the business challenge at hand, and then accordingly consider the type of analysis, visualization techniques, and behavioral understanding that can help an organization make the right decision.

Thus, Decision Scientists must be able to work with multiple data sources and inputs that are carefully chosen based on their ability to solve a business problem. Decision Scientists should be able to tell when its right to move forward with a decision based on correlations and when they need to move on to another experiment altogether.

The bottom line – Decision Scientists MUST possess an analytical bend of mind along with strong business acumen. Their end-goal is to leverage data and statistics in ways that enhance the business decision-making process and optimize budgeting and marketing spend.

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The Final Verdict

According to recent stats, there’s a big demand for data scientists and tremendous shortage in the industry, which is only bound to escalate in the future. Naturally, an increasing number of companies and organizations are on the mission to fill the talent shortage. Data science courses are in high-demand now and the trend will likely to increase in coming years.

However, in the pursuit of Data Scientists, companies often tend to forget that Data Science is but one aspect of the bigger picture. After all, if you have the insights at hand but no one to give direction or shape to those insights, what good are they for your business?

While analytics is a crucial ingredient to “help” businesses make better decisions, it is Decision Science that completes the whole equation. 

Unfortunately, Decision Scientists are even rarer than Data Scientists. These versatile professionals are skilled in blending business, math, technology, and behavioural science to help companies make the right decisions. They possess the ability to synthesize new ideas out of business challenges, whatever they may be.

While Data Scientists are “problem-specific” experts who can use math, statistics, and technology to solve specific problems, Decision Scientists are prepared for all kinds of business situations. 

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To conclude, if an organization in the modern world wants to emerge as the winner, it must acquire both Data Scientists and Decision Scientists. Only a combination and collaboration between both can impart meaning to the full-circle of business – by bringing the data analytics and business side together, Data Scientists and Decision Scientists can generate the true potential of Big Data and data-oriented decision making.

If you are curious to learn about python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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Frequently Asked Questions (FAQs)

1 What degree do I need to become a data scientist?

You can get a bachelor’s degree in mathematics, statistics, computer science, IT, physics or any related field. More than the degree it is the skills that will take you places through it. For instance, you must be well versed with basic topics of mathematics including probability and linear algebra. You must also be aware of the fundamentals of statistics. Apart from this, a good grip on programming languages like Python, R and others is a must.

2Can you become a self-taught data scientist?

You can teach yourself data science and become a self-taught data scientist. Though it might sound simple, it might still be a little complicated for you if you are new to the world of data and coding. There are no specific rules to follow and ways to lead yourself towards success - there are many. You need to first identify the above skills and if you do not yet possess those skills, start adopting those skills from today.

In today’s world, the internet will never let you put a limit to your learning of new courses and skills, you can explore the jungle of technology and learn every day. You just need to find the right resources that will help you in achieving your goals with proper directions and skills.

3 Are Data Scientist Jobs Competitive?

Data Science is emerging as one of the fastest-growing industries all over the world. As the sexiest job of the 21st century, data scientists are in high demand in the industry. More and more people are showing interest in securing a successful career as a data scientist, hence there is indeed a huge competition going on in this industry.

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