Data Science vs Business Analytics
“Analytics allows the leaders of today and the future to be measured not only by decisiveness and responsiveness but by the accuracy and effectiveness of their foresight”.
- Kevin Mackey, Business Intelligence and Analytics Practices Leader at Point B Management Consultants.
“In the next ten years, data science and software will do more for medicine than all of the biological sciences together”.
- Vinod Khosla
The two quotes, the first one about business analytics, and the second one about data science, both show the importance of both the domains. Both the domains look the same at first sight and are usually used interchangeably, but they are different from some aspects. One thing common in both of them is- both of them are seeing skyrocket growth.
The current market size for Data Science is around USD 38 billion and that for Business Analytics is around USD 67 Billion.
By 2025, the market size is expected to reach USD 140 billion and USD 100 billion respectively.
What does this imply?
Of course, a surge in demand for the two job profiles.
There are 14368 job openings on Naukri.com for data scientists. On the other hand, there are 55040 job openings for business analytics on the same job portal.
While there is a great demand for data scientists, it can be a smart move to go with a data science certification and make a career in the exponentially growing field.
What is Business Analytics?
The field that drives practical, data-driven changes in a business is referred to as Business Analytics. This involves the practical application of statistical analysis to focus on dispensing feasible recommendations.
In the process of Business Analytics, the use of statistical methods and technologies is done to analyze the data collected from various resources to gain new insights thereby improving strategic decision-making.
What is Data Science?
It is difficult to define Data Science exactly as it is an interdisciplinary field that involves the usage of scientific methods, processes, algorithms, and systems in order to extract meaningful insights from the data that can be structured or unstructured.
It is referred to as interdisciplinary as it is a blend of math, data engineering, scientific methods, domain expertise, visualization, advanced computing, and hacker-mindset.
Comparison between the two
While both the domains are related to collecting the data from different resources, analyzing the data to make it structured, draw conclusions, and report meaningful insights; there are some differences between the two that make them the two separate domains.
Let us observe the terms. Business Analytics, clearly, is related to business issues such as ROI, costs, profit, product performance, and other business issues.
On the other hand, Data Science is related to the questions that can be answered from the data provided, such as geographical conditions, customer preferences, seasonal factors, etc.
|Data Science||Business Analytics|
|Who coined the term?||DJ Patil (LinkedIn) and Jeff Hammerbacher (Facebook) coined the term Data Science in 2008.||Frederick Winslow Taylor coined the term in the late 19th Century.|
|Notion||An interdisciplinary domain that includes data collection, creating algorithms, and systems to draw useful insights from the data.||This includes the usage of statistical concepts to draw insights from the data collected.|
|Involvement of Coding||Requires coding widely. Data Science is a blend of computer science and traditional analytics practices.||Requires minimal coding. Business Analytics is oriented more towards statistical analysis.|
|Recommended Programming Languages||Java, C/C#/C++, Python, R, MATLAB, SAS, SQL, Scala, Stata, Julia, Haskell.||Java, C/C++/C#, Matlab, Python, R, SAS, Scala, SQL.|
|Usage of Statistics||When a Data Scientist is done with analysis, algorithm building, and coding, then statistics are used at the end.||Business Analysis is completely based on statistical analysis.|
|Type of Data Needed||Both structured and unstructured data.||Significantly structured data is required.|
|Average Salary||At mid-career, a data scientist can earn up to USD 128,750||At mid-career, a business analyst can earn up to USD122,627.|
|Future Trends||Artificial Intelligence and Machine Learning, Automation of Data Management, Conversational Analytics.||Tax Analytics and Cognitive Analytics, Augmented Analytics, Data Quality Management, Data Visualization, Data-driven Culture.|
You have seen the head-to-head comparison of both the domains. The greater salaries and better future trends are observed in Data Science. Moreover, Data Science is a superset of Business Analytics, so if you are a data scientist, you can perform business analysis as well. While the business analysis was there in the late 19th century, data science is a relatively new domain. But, it has seen exponential growth. This growth is fueled by huge amounts of data generated every second across the globe. This data continues to grow every day.
To become a data scientist can be a great career option, as there are many job opportunities and good career growth as well. Taking up an online training course is the best option. Besides choosing learning hours according to your convenience, you get to choose from different modes of learning too (blended learning, instructor-led, and online). The doubt-sessions are also carried out to help you with any learning issues. Also, you can learn at your own pace.
You don’t need to worry about the stuff you need to learn, it is all kept ready by the online training providers. The only thing you need to do is enroll yourself in an online training program by some accredited learning institute.