Today, both Data Science and Business Analytics have become an integral part of the tech and business sectors. Since both of these domains deal with data and the insights it has to offer, often the terms Data Science and Business Analytics are used interchangeably. However, they are quite different from each other, particularly concerning the scope of the problem each address.
In this post, we will expand on the Business Analytics vs. Data Science debate and shed light on some of their key differences. In the end, we will cover why learning data science, preferably by taking a data science course, is worthy for your career.
What is Business Analytics?
Business Analytics (BA) refers to the practices and technologies used for collecting, collating, processing, analyzing, and studying business data to monitor past business performance and to gain vital insights for future business planning.
Business Analytics uses various statistical/mathematical models, analytical modeling, quantitative analysis, predictive modeling, and iterative methodologies to extract meaningful information from data and converting them into business insights. It aims to leverage data for solving complex business problems while enhancing productivity, boosting revenues, and promoting data-driven business planning.
Business Analysts use a combination of mathematics, statistics, information systems, computer science, and operations research to understand large and complex datasets. The extracted information is then used to predict future events associated with consumer behavior and market trends accurately and to determine appropriate solutions to increase the business value, profitability, and customer satisfaction.
What is Data Science?
Data Science is an extremely popular field of study that has found application across the different sectors of industry. It is an interdisciplinary field that aims to decode and demystify large datasets (Big Data) using a combination of mathematics, statistics, computer science, information science, data analysis, machine learning, and the related branches of study. Data Science uses a host of scientific practices/techniques, processes, algorithms, and systems to extract valuable insights from structured, semi-structured, and unstructured data.
Essentially, Data Science has five core stages –
- Capture (data acquisition, data entry, data extraction, and signal reception)
- Maintain (data warehousing, data architecture, data cleansing, and data processing)
- Process (data mining, data modeling, clustering/classification, and data summarization)
- Analyze (predictive analysis, qualitative analysis, regression, and text mining)
- Communicate (data visualization, data reporting, business intelligence, and decision making
Business Analytics vs Data Science
Now that you know what the fields of Business Analytics and Data Science are, we can dive into the Business Analytics vs. Data Science debate.
As mentioned earlier, even though Business Analytics and Data Science seem alike, they have a vast difference in their scope. While Data Science seeks to offer actionable insights by uncovering hidden patterns in structured/semi-structured/unstructured data to address a business issue (example, customer behavior) from a broader perspective, Business Analytics is mostly confined to studying structured data to offer solutions to specific business challenges (for instance, business performance related to a particular client).
Essentially, Data Science is the umbrella that encompasses mathematics, statistics, data analytics, programming, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Neural Networks to mine, process, analyze, and interpret large datasets. Business Analytics is but one part of Data Science that further branches out into Statistical Analysis and Business Intelligence.
Here are the fundamental differences between Business Analytics vs. Data Science:
| Business Analytics | Data Science |
| It uses statistical and mathematical concepts and methods to extract information from structured data. | It is a multi-disciplinary field that uses mathematics, statistics, AI, ML, computer science, and information science to extract insights from structured as well as unstructured data. |
| It does not involve coding since it is highly statistics-oriented. | It combines computer science with traditional analytical practices, and coding is an essential component of it. |
| The complete analysis of data relies heavily on statistical concepts and approaches. | Statistical concepts and procedures are used at the end of the data analysis, followed by coding and algorithm building. |
| The top five industries leveraging Business Analytics are – Technology, Finance, Retail, and Marketing. | The top five industries leveraging Data Science are – Technology, Finance, e-commerce, and Academics. |
| It will create the most significant impact on Cognitive Analytics and Tax Analytics. | The most significant impact of Data Science will be seen in AI and ML. |
Why Learn Data Science?
As we rely more and more on emerging technologies such as smartphones, IoT, smart virtual assistants, self-driving cars, we are only fuelling the growth of data on a global scale. Each day, the world produces an unfathomable amount of data – nearly 2.5 quintillion bytes, according to recent estimates. Naturally, Data is the gold mine for us now. If you have data, you have the power to influence businesses and even the way of everyday living.
According to Hal Varian, Chief Economist at Google and Professor of Information Sciences, Business, and Economics at UC Berkeley:
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
Data Science and its related fields of study, such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Business Analytics, have emerged to give shape and meaning to the ever-growing pile of data. Without these technologies, data is of no value to anyone. Hence, in recent years, we have seen a massive surge in demand for Data Science professionals, including Data Scientists, Data Analysts, ML Engineer, Business Intelligence Developer, to name a few.
At present, Data Scientists are among the most in-demand and highest-paid professionals in the industry. Forbes maintains that for the past 3 years Data Scientist has been credited as the “best job in America” – with a median base salary of $110,000. However, there’s a severe shortage in the supply of skilled Data Science professionals compared to their demand. In a recent study by IBM, it was found that by 2020, the demand for Data Scientists will rise by 28%. The same study also states that Data Science and Analytics (DSA) job vacancies remain open for an average of 45 days – five days longer than the overall market average.
These stats show that there is a tremendous need for the world to churn out more Data Science professionals, and fast. Owing to the shortage of skills and talent in the Data Science domain, companies and enterprises are ready to pay high compensation to deserving candidates. Hence, there’s never been a better time to learn Data Science and cash in your Data Science skills!
In the upcoming decade, we will witness mass democratization of these advanced technologies, and if you want to be a part of the next tech revolution, you better invest your time in learning Data Science now!