Business analytics and data science are often used interchangeably but are different at many levels. While both are used in solving business problems and decision-making processes in organisations, both have different job descriptions and skills requirements. The syllabus for these two fields is also, therefore, different at many levels.
“Today, both Data Science and Business Analytics have become an integral part of how business problems are answered. Both involve data gathering, modelling, and insight gathering and are used interchangeably. However, they are quite different from each other, particularly the scope of the problem each address,” says Anish Agarwal, Director – Data & Analytics, India, NatWest Group.
Echoing the same thoughts, Dr Siddhartha Ghosh, Professor & Head of the Dept. of Artificial Intelligence (AI) at Vidya Jyothi Institute of Technology (VJIT) said that Business Analytics and Data Science has revolutionised the computer and software field. Adding a note on the importance of degree he said, “A degree gives you the entry path or recognition, and when it comes from the best institutions or universities of the world, it gives you an extra edge over others. A degree or course also is a scientific and systematic approach to give one the best learning experience which he or she can use in career development and knowledge transfer.”
Business Analytics Vs Data Science: What Are The Skills Required
Business analytics deals with researching information and analysing data to help find solutions. A large part of their work is to analyse business trends and present business simulations and planning. The requirements in terms of tools are Excel, SQL, Python and techniques such as statistical methods, forecasting, predictive modelling, storytelling and more.
Data science, on the other hand, requires specialised skills with a background in mathematics, programming, statistics, and problem-solving to utilise data in new and creative ways. Data Science focuses more on building algorithms and techniques for handling and finding insights from data. Some of the skills expected from data scientists are programming, linear algebra, computer science fundamentals, with an understanding of tools such as R, Python, Keras, PyTorch, deep learning, NLP and more.
Agarwal explains that data science is the umbrella that encompasses statistics, data analytics, artificial intelligence, machine learning, deep learning and neural networks to mine, process, analyse, and interpret large datasets. Business Analytics is but one part of Data Science that further branches out into Statistical Analysis and Business Intelligence.
Difference In Curriculum
Given the differences in work profiles that these two bring, the courses are designed to cater to these needs. A degree in business analytics will be more suitable for someone who likes to get involved in the business side of it with lighter technical skills. Data science, on the other hand, focuses more on coding and technology. However, both require love for numbers, coding and programming, structured thinking approach, good knowledge of statistical concepts and more.
Business Analytics
Most business analytics courses focus on developing analytical skills suited for business management. It covers training in business knowledge such as strategy, finance, accounting and technical aspects such as statistics, programming, stochastic models, time series analysis, operations management and more. Most business analytics course curriculum focuses on building a strong foundation in data management while handling planning and focusing on predictive analytics.
Data visualisation is a crucial component for those in business analytics, as presenting data in a way that it provides actionable options to business leaders is a primary part of the job. It also focuses on building corporate communication skills required to convey findings.
Data Science
Most of the courses in data science are related to a degree in computer science. It focuses on statistics, applied math, programming, machine learning, and more. It focuses on coding for algorithms that are used in finding trends and for predictive analytics. It also focuses on the knowledge of developing algorithms to find connections between different datasets to detect trends.
Some of the other things taught at data science courses are more complex areas such as artificial intelligence, system architects to prepare data for advanced analytics, analyse data structures, data visualisation and more.
Which Course To Pick
Both business analytics and data science courses consist of many common subjects. While both require an understanding of fundamentals such as programming and statistics, weightage for other skills may differ in both the courses. For instance, business analytics focuses more on business analytics, business intelligence, whereas data science is more about data mining, data warehousing, AI, deep learning, cloud computing etc.
“A degree program in Data Science is one of the best streams young aspirants can opt for in today’s world. The value of Data Science skills is no secret to complete in new-age jobs. Among various options of learning, degree programs offer the most comprehensive, structured, and robust way of learning to build practical skills. So, if you are at the career stage that allows you to pursue a formal degree in Data Science, just go for it,” said Sumeet Bansal, CEO & Co-founder, AnalytixLabs.
Having said that, from a hiring perspective, experts mostly look at the skills that you have acquired rather than degrees. Ankur Sharma, who is the head of Analytics at Instamojo, said that getting some real hands-on experience through an internship or a job is going to be far more valuable in enhancing one’s data science skills. The degree courses are typically not very well aligned with what is needed in the industry.
“I would recommend pursuing a degree in business analytics or data science only if you are really new to the field, or do not already have a degree in mathematics or engineering. For some, pursuing a degree course can also serve as a great motivator for them to learn the analytics chops with the right dedication, which is hard to gather, especially if you have been away from college for a while,” he said.
Preeth Joseph, Head of Talent Management, BRIDGEi2i, also believes that one’s degree is immaterial given one’s passion for the subject. “Whether you’re an engineer, a statistician, or a mathematician, your enthusiasm and passion can be deciding factors about your career in the AI industry. If you love solving problems, enjoy coding, have a passion for numbers, and revel in unravelling stories from data, you’re already halfway there!”
Wrapping Up
The decision to pick up the course largely depends on the individual interests and skills that one aims to acquire. Every university has curriculums designed which are specific to them. However, it more or less remains as outlined above. Deciding on which degree to pursue depends on the inclination towards the skills which are usually more detailed for data science courses as compared to business analytics.
Bansal said that there are plenty of post-grad certification programs that offer well assimilated and job-oriented learning with high ROI. “One must choose a program that offers well-rounded learning, comprising tools, techniques, and industrial applications,” he said.
“As we rely more and more on progressive innovations such as smartphones, IoT, smart virtual assistants, self-driving cars, we are only fuelling the growth of data on a global scale. In the upcoming decade, we will witness the mass democratisation of these advanced technologies. If you want to be a part of the next tech revolution, you better invest your time in learning Data Science now!,” said Agarwal on a concluding note.
