Top 5 Ways Data Science is Different from Decision Science

Analyzing data is not something only data scientists do. Analysts, researchers, growth marketers, and decision scientists often have to dig into data to find solutions to business problems through meaningful insights. Quite often organizations have both data scientists, and decision scientists and that creates confusion as to who's responsible for what. Those who are venturing out to learn data science should have a prior understanding of what they want to learn. The following are the 5 key differences between data science and decision science.

• Data science considers data as a tool for enhancing and developing new products with different statistical methods. Decision science considers data as a tool to make smart decisions for the benefit of a company.
• Data science seeks meaningful insights for the company executives. Decision science takes a 360 view to make insights usable for the company.
• Data science needs only analytical ability whereas decision science demands both analytical ability and business acumen.
• Data science integrates mathematics and technology with data visualization and mathematical computation. Decision science is the interdisciplinary application of mathematics, technology, and business with behavioural science.
• One of the challenges of data science is the supply of a huge amount of dirty data whereas one of the challenges of decision science is the lack of reliable data.

More Trending Stories 

• Dogecoin is Trading 78,000% Above its All-Time Lows Amidst the Crypto Bloodbath
• Regulators Scrutinize Voyager's Backside Dealings Using Investors' Money
• Only AI Characters can turn Metaverse Ghost Towns into Virtual Hotspots
• Robots in a Human Workspace Are Dangerous, Amazon Warehouse Is the Example
• GitHub's Copilot is Not Democratic but Big Tech is Playing Dumb
• Artificial Intelligence 100: Top Industry Wise Companies Listing
• Flawed AI Makes Sexist Robots and Researchers Are Okay with It