Last updated on November 30th, 2023 at 09:22 am
In the field of data science and decision science, many specialists are being hired to perform specific tasks. Both fields are unique, making it difficult to justify one over the other. In this blog, we shall look at these two fields of data science and decision science, their similarities and differences.
What is Data Science?
Data science is the branch of data that analyzes the hidden patterns from a large dataset. It is the process of gathering insights from the data for business purposes. The analysis of data involves the use of various applications of mathematics. Commonly used areas are statistics, probability, calculus, and linear algebra.
It uses mathematics, specialized programming, artificial intelligence, machine learning, and advanced analytics. Principles from all these identify the hidden patterns in the large raw dataset.
What is Decision Science?
Decision science, on the other hand, is the branch of data that deals with interpreting the analyzed data. Or in simple words, it is the decision-making process for solving problems based on the analyzed data. The tools used mainly include applications of mathematics, thinking, and behavioral science.
Data interpretation is essential for identifying problems and challenges within a business. You can make reliable, accurate, and unbiased decisions by working on solutions with analyzed data. This helps the business grow and benefits employees by improving their decision-making skills.
What Is the Difference Between Data Science and Decision Science?
Look at this real-life example to get a clear picture of the two terms.
Every social media application has a database. This database consists of data from a large number of users. This data is generally unsorted or not in a pattern. Most of the data is raw. Data science helps analyze the patterns in this unsorted data using various algorithms.
On the other hand, using this analyzed data to plan strategically for improving the application comes under Decision Science.
Here is a deeper insight into what makes data science different from decision science.
| Sr. No | Parameter | Data Science | Decision Science |
| 1 | Meaning | A branch of data that deals with the analysis of data present in large amounts (large datasets). | A branch of data dealing with the interpretation and decision-making based on the analyzed data. |
| 2 | Function | Collecting information about the data patterns present in large amounts. | Understanding the data patterns and processing them into action. |
| 3 | Size of data | It works on a large dataset. | It works on a comparatively smaller dataset. |
| 4 | Framework | It acts as a data framework for machines. The machines then do the further processing. | It acts as a data framework that humans work on by making decisions and thinking creatively. |
| 5 | Tools | The tools used are: | The tools used are: |
| BigML. | Strategic planning. | ||
| Microsoft Excel. | Group discussions. | ||
| Tableau. | SWOT diagrams. | ||
| TensorFlow. | Decision matrix. | ||
| Apache Spark and many more. | Pareto analysis, etc. | ||
| 6 | Skills required | Mathematics. | Critical thinking |
| Statistics. | Problem-solving skills | ||
| Data Visualization. | Mathematics | ||
| Deep Learning. | Behavioral science | ||
| Big Data. | Design skills | ||
| Programming and many more | Analytical skills | ||
| Business-oriented approach and more. | |||
| 7 | Data | It is a tool used for the improvement of a business. It also helps in the development of a business. | It is a tool used for making decisions. It makes the process reliable and easy. |
| 8 | Applications | Education. | Business. |
| Healthcare. | Management. | ||
| Finance. | Law. | ||
| Banking. | Public health. | ||
| Media. | Education. | ||
| Sports. | Military. | ||
| Education. | Finance and more. | ||
| E-Commerce and many more. | |||
| Although there is a wide range of applications of decision science, it is majorly confined to business and management-related activities. | |||
| 9 | Challenges | Protecting data from cyberattacks is a challenge in the field of data science. | Critical thinking is a skill that develops with time. It isn't easy to adapt to out-of-the-box thinking. It is a must for accurate decision-making. It is challenging for the organization and the employees. |
| Data is present in large amounts. Protecting this data can become difficult due to increasing cyber risks. |
Conclusion
At first glance, data science and decision science may appear similar, but they are quite different. Data science focuses on analysis, while decision science deals with the decision-making process. However, both disciplines are equally essential for a successful business or organization.
MBA in decision science is an excellent career option. It is great for anyone interested in pursuing a career in business. If you are an MBA aspirant, this course could be a great way to boost your career.
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