HomeBlogData ScienceData Science vs Business Analytics – Key Differences You Should Know
Both Data Science and Business Analysis include the collection of a huge amount of data and process it to find certain results, but there is a huge difference between them. Data Science can be considered a superset of Business Analysis. In Layman's terms, Business Analysis mainly focuses on business-oriented problems and well-known and established methods to solve those problems whereas Data Science involves finding the best way to predict certain results using various algorithms. The following article would elaborate on the difference between data science and business analytics and even give a vivid description of each field and how it is different.
“Data is what you need to do ANALYTICS Information is what you need to do BUSINESS ”
By John Owen
Let’s first discuss each domain in its individual capacity. The term “Data Scientist” word was coined by Jeff Hammerbacher and Dr. Patil in 2008. A person who studies Data Science and makes use of it to solve real-world problems is known as Data Scientist. It is an interdisciplinary field that includes statistics, data analysis, data visualization, scientific techniques, artificial intelligence, machine learning, and even deep learning which is in high demand nowadays. The scope of work ability for data scientists includes gathering and collection of data from various data sources such as web data, media data such as images and videos, sensor data, cellular-client data, and preprocessing the data (cleaning, aggregating, and normalizing) in a common general form, and applying certain algorithms to bring out the best of data-driven insights.
Since the late 19 century, “Business Analytics” as a term was introduced by Fedrick Winslow Taylor. A person who practices principles of Business Analytics is called a Business Analyst. This particular field mainly focuses on profits gained by an organization and all relevant decisions are taken primarily keeping it in mind. The main goal is to drive the business value of an organization and vital changes that are predominantly required to gain certain checkpoints of business. The primary techniques used are data mining, statistical analysis, and predictive analysis, along with that in-depth knowledge in business operations. These techniques are blended to analyze and convert data into resourceful information in order to expect certain outcomes that will help managers to take key point decisions for business growth and its continuous functions.
| Criteria | Data Science | Business Analytics |
Terms | The term was coined by Jeff Hammerbacher and Dr. Patil in 2008. | The term was put in place by Fedrick Winslow Taylor in the late 19 century. |
| Definitions | Field of data inference, optimization of the algorithm, and data-driven insights. | Identifying business needs and their solution using historical data, in order to make quick decisions for strategic operations. |
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To learn Data Science, you have to be proficient in computer science, algorithms, and analysis, linear algebra, and programming. You must also know statistics and a higher working idea about concepts of machine learning (supervised, unsupervised, and reinforcement learning), deep learning (feedforward and recurrent neural networks). Coding is mandatory in data science as most of the time perfect algorithms are needed to find out using many robust processes which are written manually by a programmer and vary from situation to situation.
Business Analytics professionals must have skills in presenting business planning, optimization techniques, analytical skills, predictive modeling, and storytelling. Tools that are efficiently used in Business Analytics are Excel, Tableau, SQL whereas commonly used tools for data science are Python, R, Matlab, sci-kit-learn, Keras, PyTorch, and Pandas. You can easily notice these data science tools being mentioned as the data science course subjects in all our data science and allied courses.
| Criteria | Data Science | Business Analytics |
| Data | Both structured and unstructured data are required by data scientists. | Mostly structured data is preferred by Business Analysts. |
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| Coding Skills | For data science, coding is required. | Coding is not mandatory. |
| Statistics | Statistics is used at the end of the analysis following coding. | The concept of business analytics is solely statics. |
It may seem difficult, but good data science courses make it easy to learn all the necessary skills to build expertise in the field.
There is a significant effort made by a Data Scientist and a Business Analyst in the growth of the organization thus both of them play a crucial role. We will be seeing how both of the roles actually differ from each other. A data scientist looks after problems that are specific and a bit complex which bring non-linear growth to the organization. For example, he can build a computer vision system that uses images for employees' attendance or assess certain data that may predict policies that are dangerous and risk-taking for an insurance company, even he can build up a credit card fraud detection system for a banking application. But a business analyst carries on and takes day-to-day crucial decisions in order to handle client businesses and operations to increase revenues of the organization. For example, whether a certain feature on E-commerce websites must be made available or not, or if a credit card fraud detection application is applicable for certain banking clients, is the computer vision attendance system is effective enough.
Let's drive into a more vivid situation to understand the difference. Suppose you are a manager of ABC Bank and currently you are facing two major problems.
Consider who you would need to work on each of these scenarios.
Let’s look into the first scenario, where a person needs to make a decision that’s going to affect the working principle, business as well as profit of an organization. The certain strategy requires major decisive thinking whether such offer adhere new customers to make move into the bank or the existing customer may leave the bank and move to competitive banks in the market. A detailed analysis of the records of such customers and their income must be done along with optimality of bank and its finances to provide such attractive offer to carry on. Thus these sorts of logical decisions based on statistical calculations must be handled by a person having master's in business analysis.
On the other hand, certain machine learning algorithms must be implemented which will provide the best results in deducing whether a transaction made is a fraud or not or even chances of it being a fraud. Such a mechanism requires an algorithm that will work in real-time, as well as a pipeline, needs to be maintained for instant notification. The algorithm before deployment must ensure it provides us best result based on available historical data. Understanding such huge behavioral data of users and finding hidden patterns must be availed to a Data Scientist.
| Criteria | Data Science | Business Analytics |
| Study | A data scientist explores patterns and trends of all possible scenarios. | A Business Analyst explores patterns and trends specific to the business. |
| Challenges |
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| Trends of Future | Artificial Intelligence and Machine Learning | Cognitive and Tax Analysis |
In the age of Data revolution both the profession are in great demand and companies are willing to pay a huge lump sum for the skills in these domains. Data scientists are required in every sector and not just in technical domains. With rising in cutting edge ML technologies such as Chatbot, Personal Assistants, Weather Forecasting Prediction, Retail Industry Profit and Trends Prediction, Automated Driving Cars as well AIs in every domain requires data scientists. These high-paying jobs are scattered throughout the world thus a lot of on-site opportunities are available everywhere. The education of the candidate must include a Master's Degree with a background in Computer Science, Statistics, or Mathematics. Along with that, the candidate must also be a good storyteller and be highly efficient in understanding the patterns inside data. Programming languages such as Python and R having a great demand in this domain. One can earn certification in Python from the Knowledgehut data science with Python Online Course.
Roles that require Data Scientist are
For the Business analyst, the recruiters generally look for candidates having in-depth knowledge in management and data analysis. Generally, a business analyst belongs from a Management background, with high problem-solving skills. Rather than coding, the candidate must have deep knowledge of the business and its operation at the same time will be quick to figure out the requirements before solving a certain business problem. Roles that require Business analytics are:
While comparing the average salaries in India, Data scientists are paid more than a Business Analyst, as the cost of operation for a Data Science project requires more costly resources thus making the organization to hire a candidate with specific and experienced in the domain making the average salary for data scientist higher than that of a business analyst. Even skill-wise, data scientists are a little more advanced than business analysts as the former has to go through a lot of coding as well as manage unformatted big data in both structured and unstructured formats. Below we have data from Glassdoor for average salaries of both Business Analysts and Data scientists in India and in New York(US).
Average Salary of Data Scientist in India (Source: Glassdoor)
Average Salary of Business Analyst in India (Source: Glassdoor)
Not only in India, but we can also see the same significant salary difference in New York. After the Big Resignation of 2021, both jobs are having high demands in the market thus we can assume the persistence of data is more than average.
Average Salary of Data Scientist in New York (Source: Glassdoor)
Average Salary of Business Analyst in New York (Source: Glassdoor)
Both Data Science and Business Analytics has a wide varied range of applications and use cases. Data Science can be used in fields of technological, financial, academics, health care, retail, astronomy, climate, oceanography, stocks markets, and many more. There is no bound, up to which we can use data science to make predictions and create solutions to solve any problems. Similarly, business analytics can be used in retails, finance, eCommerce, CRM, and marketing fields.
Data Science can be utilized in every field in day-to-day life. In healthcare industries, electrocardiographs and heart monitoring require computer vision to identify various organs and even the data of each patient are analyzed to find common trends of diseases and illnesses. In Stock Market, to analyze whether the particular trend will be bullish or bearish, we can use data science to answer a lot of questions. Similarly, decisions regarding sales can be marked throughout the year and profits can be increased by identifying the patterns in sales. We even use Data Science even for climate prediction, weather forecasting, and oceanography. These applications of Data Science has made predictions of human over certain solution more confirmed and give confidence while taking the right decisions.
Business Analytics has highlighted its importance in fields that are particularly related to business use cases, mostly in the finance and retail industry. This domain helps business owners and management level authorities take the right decisions such as whether a certain scheme would be beneficial to the bank and its customer or launching a certain product would increase its sales or not. It even provides insights to analysts into which are the most likely feasible solution to a problem.
In our conclusion we will be answering one of the most demanded questions that everybody demands, “Which is better for me, Data Science or Business Analytics?”. Data Scientist has to keep focus more on data development as it gives insights in every direction, but a business analyst manages the data only in the domain of business requirements. Both roles are important in business development and progress. Thus if a person is more into decisive and logical skills with less knowledge in programming and computer science can proceed with Business Analytics. For any beginners, this domain would be easy to catch on to. But if one is interested in coding and is familiar with machine learning algorithms or even interested in data analysis can proceed towards Data Science. I hope this blog will answer most of your queries and give relevant information to make difference between Data Science and Business Analytics.
Yes, a business analyst can become a data scientist with relevant training in machine learning, programming, linear equations, and practicing packages such as Pandas, Numpy, Matplotlib, Keras in some hands-on projects.
As paper the current trends in the market and data provided by Glassdoor, the average salary of a Data Scientist is more than that of a business analyst in the same organization for a similar level of experience.
Yes you can move from business analyst to data scientist with some intensive training and hands-on real-life projects. As business analysis is a subset of Data Science, certain skills such as Machine Learning and extensive practice in Coding and Algorithms along with the usage of libraries such as Pandas, NumPy, and Sci-kit-learn are going push your skill similar to a Data Scientist.
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