Difference Between Data Science and Business Analytics

Data Science: It is the complex study of the large amounts of data in a company or organizations repository. This study includes where the data has originated from, the actual study of its content matter, and how this data can be useful for the growth of the company in the future.
The data related to an organization is always in two forms: Structured or unstructured. When we study this data, we get valuable information about business or market patterns which helps the business have an edge over the other competitors since they’ve increased their effectiveness by recognizing patterns in the data set.
Data scientists are specialists who excel in converting raw data into critical business matters. These scientists are skilled in algorithmic coding along with concepts like data mining, machine learning, and statistics.
Data Science can be used in the healthcare sector, in the fraud detection sector, internet search, airlines, and so on.

Business Analytics: Business analytics is quite similar to data science in the sense that both of them involve analyzing data but in this, we take it a step further and focus on the steps to be taken to positively affect the business after analyzing the data.
Hence, we can say, Business Analytics is the study of data in a way that we are able to make decisions for the business in the long run. It aims to collect data from various business models and interpret it to solve a business goal or target.
It is usually used to improve the company’s overall performance in the market by strictly making business-focused decisions.


Below is a table of differences between Data Science and Business Analytics:

S.No. Data Science Business Analytics
1. Study of complex data using algorithms to find a pattern Analyzing data to find business insights using statistics
2. More use of algorithms and pure code More use of statistical analysis and business concepts
3. Usually two types of data- structured and unstructured Usually data is taken from a business model (structured)
4. This is relatively a new concept Has been around since the 19th century
5. It is the superset of business analytics It is a part of data science
6. Very vague and gives generic results Gives business specific results
7. Cost of investing is high Cost of investing is low
8. Can be used to enhance Machine Learning and Artificial Intelligence in the future Can be used for Tax Analytics and Cognitive Analysis in the future
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