Data Science vs. Business Analytics

Key Differences Between Data Science and Business Analysis:

Here are some of the key differences between data scientists and business analysts.

1. Data science is the science of studying data using statistics, algorithms and technologies, and business analysis is the statistical study of business data.

2. Data science is a relatively recent development in analytics, but business analytics has existed since the late 19th century.

3 Data science requires a lot of programming skills, but business analysis doesn't require a lot of programming.

4. Data science is an important subset of business analysis. Therefore, anyone with data science skills can do business analysis, but not vice versa.

5. Taking data science one step ahead of business analysis is a luxury. However, business analysis is needed for companies to understand how it works and gain insights.

6. Analytical Data Science results cannot be used for everyday business decision making, but business analysis is essential for critical administrative decision making.

7. Data science does not answer obvious questions. Questions are almost common. However, business analysis mainly answers very specific questions about finance and business.

8. Data science can answer questions that can be used for business analysis, but not the other way around.

9. Data science uses both structured and unstructured data, while business analytics primarily uses structured data.

10. Data science has the potential to make a big leap, especially with the advent of machine learning and artificial intelligence, while business analysis is still slow.

11. Unlike business analysts, data scientists don't come across a lot of dirty data.

12. In contrast to business analysis, data science relies heavily on data availability.

13. Investing in data science The cost of is high and business analysis is low.

14. Data science can keep up with today's data. Data is growing and diverging into many data types. Data scientists have the necessary skills to handle it. However, commercial analysts do not own it.


Data Science and Business Analytics Comparison Table

Below is the comparison table between Data Scientist and Business Analytics.

Comparison base	Data Science	Business Analytics
Coining of Term	In 2008, DJ Patil and Jeff Hammerbacher from LinkedIn and Facebook, respectively, invented the term Data Scientist.	Since Frederick Winslow Taylor's implementation in the late 1800s, business analytics has been in use.
Concept	Data inference, algorithm development, and data-driven systems are all interdisciplinary fields.	To derive insights from business data, statistical principles are used.
Application-Top 5 Industries	·         Technology ·         Financial ·         Mix of fields ·         Internet-based ·         Academic	·         Financial ·         Technology ·         Mix of fields ·         CRM/Marketing ·         Retail
Coding	Coding is needed. Traditional analytics approaches are combined with a solid understanding of computer science in this subject.	There isn't a lot of coding involved. Statistically orientated.
Languages Recommendations	C/C++/C#, Haskell, Java, Julia, Matlab, Python, R, SAS, Scala, SQL	C/C++/C#, Java, Matlab, Python, R SAS, Scala, SQL
Statistics	Following the creation and coding of algorithms, statistics is used at the end of the analysis.	The entire investigation is based on statistical principles.
Work Challenges	·         • Business decision-makers do not employ data science results. ·         • Inability to adapt results to the decision-making process of the company. ·         • There is a lack of clarity about the questions that must be answered with the data set provided. ·         • Data is unavailable or difficult to obtain. ·         • IT needs to be consulted.	·         • There is a notable lack of domain expert involvement. ·         • Unavailability of/difficult access to data  ·         • Dirty data ·         • Concerns about privacy ·         • Insufficient finances to purchase meaningful data sets from outside sources. ·         • Inability to adapt results to the decision-making process of the company. ·         • There is a lack of clarity about the questions that must be answered with the data set provided. ·         • Tools have limitations. ·         • IT needs to be consulted.
Data Needed	Both structured and unstructured data.	Predominantly structured data.
Future Trends	Machine Learning and Artificial Intelligence	Cognitive Analytics, Tax Analytics