9 Best Comparison between Data Science Vs Business Analytics
To understand several aspects of a business with respect to different verticals, it is common for organisations to use both data science and business analytics.
While both of these technological advancements include modelling, gathering, and analysing of data, there are some differences. Business analytics is inclined towards business-specific issues of cost, management, and profit. However, data science training solves issues related to customer preferences, seasonal campaigns, geographical considerations, etc.
In a way, business analytics is a subset of data science.
Nevertheless, both data science courses and business analytics share a common goal: improving business efficiency.
This article will discuss the basics of data science and business analytics along with drawing a comparison chart between both the aspects.
What Is Data Science?
A data science course in India includes tasks related to data analytics and data warehousing. It uncovers data insights related to questions we don’t know of. The actionable insights are achieved through various techniques such as predictive analysis, computer science, machine learning, and statistics.
All these techniques combined find solutions to the issues we don’t know of yet. This is also the major goal of data science, finding the questions we should be asking rather than looking for specific solutions.
What is Business Analytics?
Business analytics is primarily concerned with answering specific questions or validating business processes, policies, and information structures. It is related to the following aspects:
- Knowing what and how a business executes tasks.
- Identifying methods to improve existing activities.
- Determining the correct steps to implement new business aspects: processes, procedures, and features.
- Designing new aspects and analysing their business worth.
Data Science vs Business Analytics
The data which is collected by the organisation on a daily basis is analysed with business analytics. This analysis offers useful insights for the marketing, sales, and product development department.
Basically, business analytics can be correctly divided into three sections: descriptive, predictive, and prescriptive.
Descriptive tracks and traces the company or product performance. Predictive offers a peek into the future possibilities and how a particular decision or product might turn up in the future. And prescriptive draws from past experiences to offer recommendations for processes, product development, and tasks.
Data science, on the other hand, is more inclined towards mathematics. Of course, this makes data science jobs far more difficult and crucial, when compared to business analytics.
Data science takes all the data produced by business analytics through mentioned methods and uses algorithms along with various statistical procedures to answer questions. These questions are related to the what, when, why, and the how of a task.
Nine Key Differences in Data Science and Business Analytics
| Differentiating Factor | Data Science | Business Analytics | |
| 1 | When | First used in 2008 | First used in the 19th century |
| 2 | Concept | Involves algorithm generation, coding, and data inference | Involves statistical concepts |
| 3 | Application | Technology | Technology |
| Academic | Finance | ||
| Finance | Marketing | ||
| Sales | |||
| Retail | |||
| 4 | Code | Coding is essential, which is paired with analytical practices | Due to statistical orientation, less coding is involved |
| 5 | Languages Used | C, C#, Java, C++, R, Scala, SQL, Python, Julia, Stata, Haskell | C, C#, Java, C++, R, Scala, SQL, Python |
| 6 | Statistics | The concept is dependent on algorithm and coding, after which statistical aspects are used | The concept is dependent on statistical analysis and its concepts |
| 7 | Data Needs | Structured or unstructured data | Structured or cleaned data |
| 8 | Challenges | Data unavailability | Lack of domain expertise |
| Inability to implement results in business decisions | Lack of structured data | ||
| Lack of result clarity | Lack of effective tools for management | ||
| Lack of communication | Inability to implement results in business decisions | ||
| Lack of IT team coordination | |||
| 9 | Future Trends | Integration with artificial intelligence and machine learning | Integration with cognitive analysis |
Which Career Path Should You Choose?
Data scientists are more popular than business analysts because data science is a complex field. You can pursue a data science course to gain advanced knowledge on the subject. However, if you don’t want to work on the mathematical side of data, you can choose business analytics as well.
Both the fields have immense possibilities, and you only have to gain the required knowledge and start looking for prospective opportunities.
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