Data Science vs Business Analytics: Differences between the two, which career path is right for you

In today’s digital era, data is the new fuel enabling human to work with Artificial Intelligence for the betterment of society. As Data has become vast and complex, there is a special need for intelligent systems to handle and process the data and draw meaningful insight from it. 

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Data Science and Business Analytics are high demand streams collaborating in the process of transforming the data into valuable insights. Both of these streams focus on fast analysis of complex data which supports better decision making and driving business growth. Although both streams focus on quickly analyzing huge volumes of data, their approach and application differ. When deciding to pursue a career in these sectors, it is essential to understand these differences. 

What is Data Science?

In a broad sense, data science literally means the science of data. Having said this, how is this science different from our traditional understanding about science? The difference lies in the fact that the core aspect of this science is related to applying innovative techniques to decipher data as clearly and as authoritatively as possible. This field of data science deals with the science of devising and applying scientific methods to gain insights from data.

By applying computer programming and algorithmic strategies as well as techniques such as statistical modeling, and machine learning, it facilitates the acquisition of information about data. Further with the advent of high-end processors and computing facilities, processing huge amounts of data is now a reality and this has subsequently led to evolution and growth of Data Science. Advanced techniques in the domain of artificial intelligence, machine learning and deep learning have enabled data scientists to analyze complex data and also build suitable models to address new data challenges.

Data Scientists work with vast amounts of data, often of different types and from multiple sources, to uncover trends, patterns, and predictions. Important skills to become a data scientist include understanding of sophisticated algorithms and coding skills, often in Python, R, Spark, Tensorflow, PyTorch, Java and Matlab to create models that can predict future events or outcomes as well as visualize the data. This typically requires a high level of technical skill in the fields of mathematics, computer science, problem solving and engineering. 

A Data Scientist is responsible for analyzing customer behavior, developing recommendation systems (like those seen in Netflix or Amazon), or developing predictive models for multiple and varied use cases applied in real world scenarios. Common roles and responsibilities that Data Scientists take on are Behavioral Analysis, Fraud Detection in Banking, Predictive Maintenance in Manufacturing, Medical Image Analysis in Healthcare, Sentiment Analysis in Social Media and Marketing, logistics and supply chain management etc.

What is Business Analytics?

Business Analytics focuses on analyzing data and delivering insights in a compelling way, often using visualization tools and statistical techniques to tell a story. Business Analysts must develop skills in data cleaning, interpreting information, and creating clear visuals to quickly reveal trends. Strong logical reasoning is also important to identify patterns that can address business challenges. Effective communication is essential to share these insights with management, aiding in informed decision-making. Additionally, Business Analysts recommend strategies to leadership based on data trends to help drive business improvement.

The study of Business Analytics covers courses like data engineering, business intelligence, data visualization, financial analytics, operations research, and the use of visualization, business analytics tools. These subjects provide Business Analysts with the necessary skills to perform their roles efficiently and offer valuable insights that drive decision-making and business success. Business Analysis study provides solutions to problems like Customer Retention Analysis, Manufacturing Process Efficiency, Credit Risk Assessment, Efficient Patient Scheduling etc. 

Key differences between Data Science and Business Analytics

Career Implications: There are several high-demand career options in both data science and business analytics. Jobs in data science, which includes professions like machine learning engineer, data engineer, artificial intelligence specialist, and data scientist, are becoming more and more crucial in sectors including technology, banking, oil, and gas. To promote innovation, streamline processes, and make data-driven choices, organizations depend on data scientists. However, business analytics offers positions like business analyst, marketing analyst, and operations analyst, especially in industries like marketing, retail, healthcare, and finance. Business analysts continue to be in great demand as the AI-driven era progresses, offering firms insightful information and assisting them in making wise business decisions. 

Both professions provide substantial job potential and are essential in today’s data-centric environment.

Opportunities for Placement: Data scientists are essential to businesses in both established and developing industries in order to evaluate large data, streamline operations, and reach well-informed conclusions. Top employers for data science positions include financial institutions, consulting businesses, healthcare organizations, and major tech companies like Google, Amazon, and Microsoft. In order to promote innovation and stay competitive, startups and e-commerce companies also look for data science specialists.

Similarly, sectors like healthcare, consulting, retail, and technology are major employers of Business Analytics professionals, offering competitive salaries. Many industries also offer internships, which improve the chances of securing high-paying positions. The global demand for both Data Science and Business Analytics roles makes them highly sought-after career paths.

Things to consider before choosing Data Science or Business Analytics

Data Science studies covers mathematics, statistics, algorithms, and problem-solving to handle data. Students having an interest in coding, problem solving, mathematics, and logical reasoning are well suited for career in Data Science across industries. Business Analytics studies involves processing of complex datasets with the help of Business Analytics Tools to identify trends and patterns. It is mostly appealing for students having interest in analyzing business data with the support of tools like SQL, Excel, Statistics, Tableau, Power BI, Rapid Miner etc. A very-well understanding of business context is crucial for performing business analytics. 

If you like and are passionate for programming, logical reasoning, problem solving and mathematics, Data Science is one of the right choices for you. However, if you prefer collaborating with business leaders, analyzing trends, and offering strategic recommendations, Business Analytics could be a better match. In this 21st Century of AI era, both fields are highly sought after and attracting, but your decision will depend on whether you are more inclined toward technical problem-solving or deriving business insights. Both Data Science and Business Analytics streams provide fulfilling career opportunities. Understanding the differences between the two will help you make a well-informed decision that aligns with your strengths and career aspirations.

(Dr Sushila Palwe is the programme coordinator of BTech CSE(AI-DS) and Dr Preeti Kale is the programme coordinator of MTech DSA at MIT World Peace University, Pune)