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Business Analytics vs. Data Science – Which Path Should you Choose?

Introduction

“Business Analytics” and “Data Science” – these two terms are used interchangeably wherever I look. But there’s one indisputable fact – both industries are undergoing skyrocket growth.

Today, the current market size for business analytics is $67 Billion and for data science, $38 billion. The market size in 2025 is expected to reach $100 Billion and $140 billion respectively. This means we can expect a surge in demand for these two profiles very soon.

I have come across a lot of aspiring analytics professionals who want to choose “Business Analytics” or “Data Science” as their career, but they’re not even sure about the distinction between these two roles. Before diving into your own choice, you should be clear about which path you want to take, right? It could be a career-defining choice!

Here’s what I suggest. You can enroll in the free Introduction to Business Analytics course, where Kunal Jain, CEO, and founder of Analytics Vidhya, explains the difference between these two roles and also introduces a methodology to decide which path to choose (Business Analytics or Data Science) based on multiple factors like education, skills, and others.

Business Analytics Vs. Data Science

 

Table of Contents

  1. Business Analyst vs. Data Scientist – A Simple Analogy
  2. Types of Problems Solved by Business Analysts and Data Scientists
  3. Skills and Tools Required
  4. Career Paths

 

1) Business Analyst vs. Data Scientist – A Simple Analogy

Let us take an example of an exciting electrical vehicle startup. This startup is now big for creating job families. And, they have decided to create three job families, one is a scientist, and the other two are an engineer and a management professional. Now I want you to take time and imagine what kind of role they play in the company.

We can infer their role from the general level of understanding:

  1. Scientist – Work on complex, distinct problems such as finding a solution to build an efficient battery, or how to improve the design of the vehicle. While these problems may not give direct gain to the company but are crucial for advanced developments. And, in the future, these developments can help startups have non-linear (exponential) growth.
  2. Engineer – Take these developments and apply industry techniques to transform them into production. For example, making an assembly line to manufacture these vehicles using the right machinery.
  3. Management – Run the business and solve business-related problems on a day-to-day basis. For example, to find the right market to open a store for the vehicle. Decisions regarding the sales and marketing of these products and many others.

Now, let’s take these roles and convert it to data-based profiles.

 

Data-Based Functions:

Business Analytics Vs Data Science

  1. Data Scientist – He Works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.
  2. Data Engineer – He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on  banking software.
  3. Business Analyst – Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.

This is a very basic analogy that you need to keep in mind to differentiate the role of Data Scientist, Business Analyst, and Data Engineer.

Caution: These terms are losely used in the industry. The exact role can depend on the maturity of your organization in data initiatives.

Now that we have our basic analogy clear, let us see the kinds of problem solved by data scientists and business analysts.

 

Types of problems solved by Business analyst and data scientist

To understand the difference between a business analyst and a data scientist, it is imperative to understand the problems or projects they work on. Let us take up an interesting example. Imagine that you are a manager of a bank and you decide to implement two important projects. You have a team of a data scientist and a business analyst. How will you do the project mapping job? Below are two problem statements:

  1. Build a business plan to decide how many employees a bank needs to do XXX business in 2021
  2. Build a model to predict which transaction is Fraudulent

Take your time to understand the problems. What do you think, which problem is best suited for which profile?

The first problem statement requires making several business assumptions and incorporating macro changes into the strategy. This will require more business expertise and decision making, this will be the job of a business analyst.

The second problem statement requires processing vast behavioral data from customers and understanding hidden patterns. For this, the professional should have a very good understanding of problem formulation and algorithms. A data scientist will be a suitable person to tackle this kind of specific and complex problem.

 

Skills and Tools Required in Business Analytics and Data Science

Business Analytics

Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.

Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling and storytelling.

 

Data Science

A data scientist must be proficient in Linear algebra, programming, computer science fundamentals. Some examples of data science projects vary from building recommendation engines to personalized E-mails.

The common tools of a data scientist are R, Python, scikit-learn, Keras, PyTorch and the most widely used techniques are Statistics, Machine Learning, Deep Learning, NLP, CV.

And for both the roles, structure thinking, and problem formulation is a key skill to do well in their respective domain.

Career Paths for Data Scientists and Business Analysts

A Data scientist’s strengths lie in coding, mathematics, and research abilities and require continuous learning along the career journey whereas a business analyst needs to be more of a strategic thinker and have a strong ability in project management.

Business Analyst tends to take business roles, strategic roles, and entrepreneurship roles as they progress through career while we notice that data scientist are more of tech entrepreneur roles as they have a strong technical background.

You can refer to the following career path to see a more in-depth route from the start of data science and business analytics journey:

Business Analytics Vs Data Science

 

End Notes

I have tried to cover a few basic pointers which I learned from the free course “Introduction to Business Analytics“. If you wish to understand more about business analytics and data science. You too can go take up the course to build a strong foundation. Below is a broad agenda of the course:

  • What is Business Analytics?
  • Data Scientist vs. Data Engineer vs. Business Analyst
  • Career in Business Analytics
  • Spectrum of Business Analytics
    • Terms related to Business Analytics
    • Management Information Systems (MIS)
    • Detective Analysis
    • Business Intelligence
    • Predictive Modeling
    • Artificial Intelligence and Machine Learning
  • What kind of problems do Business Analysts work on?
  • Skills Required in Business Analytics Roles
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