Data Science Vs Business Analytics: Which Career Path Should You Choose?
Updated on Apr 24, 2024 | 6 min read | 5.7k views
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Updated on Apr 24, 2024 | 6 min read | 5.7k views
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Data Science vs Business Analytics as a domain of work is one confusion that every student of data science and analytics struggles with, and understandably so. These terms are often used interchangeably in popular discourse when in reality, there are fundamental differences between these two domains.
In this article, let’s break down the difference between data science and business analytics to help you understand each better.
Let’s start by understanding the problems that business analysts and data scientists solve.
Here’s an interesting example to understand this.
Suppose you manage a bank – you are responsible for implementing two important projects. With you is a team of data scientists and business analysts. The two projects are:
Which one do you think should be mapped to which team?
If you think deeply, you’ll realise that the ask of the first problem is more about making business assumptions and modifying the strategy by making macro changes. To do this successfully clearly requires good business understanding and decision making skills. On the other hand, the second is about finding patterns from data and making meaningful decisions.
Thus, while the first project maps rightly to the business analysis team, the second one to the data science team.
With that settled, let’s now dive deeper into both of these domains and understand the skills required to excel in them.
The role of Business Analytics is to act as a gap between business operations and IT by using analytics techniques and providing data-driven suggestions. As a result, business analysts must have a good business understanding and necessary data skills – like statistics, computer science, programming, etc.
A business analyst acts as a mediator between IT and business domains. Their goal is to find the best ways to improve processes and enhance productivity by using data, technology, and analytics.
Here are some important skills required if you wish to excel in Business Analytics:
Communication skills: Both verbal and written communication skills are important for a business analyst. Since they fill the gap between two important domains, they act as primary communicators and information providers. In such a scenario, it becomes more important to be clear and concise in your communication.
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Data science is an umbrella term that includes algorithms, statistics, computer science, and allied technology to take a deep dive into big data and find patterns from it. The goal of data science is to make informed, data-backed predictions by studying previous trends, habits, etc.
Data scientists work with different algorithms – ranging from native algorithms to machine learning algorithms to business data and identify patterns. These patterns are useful for predicting future behaviour or outcome. They also create different hypotheses, test them based on the available data, and accept or reject them based on the test results. The overall goal is to make better predictions that lead to overall business goals.
The primary skills required for a successful career in data science include –
Business Analytics | Data Science |
Statistical study of business, business goals, business data to gain insights and develop better strategies and processes. | Study of data using methods derived from computer science – like algorithms, mathematics, and statistics – to find patterns and make future predictions. |
Deals primarily with structured data. | Works with both unstructured and structured data. |
This is more statistics and analytics oriented – it does not require much programming. | Heavily relies on programming to create models which identify patterns and derive insights. |
The entire analysis is statistical. | Statistics is just one part of the entire process and is performed at the end – after programming the required models. |
Mostly important for the following industries – healthcare, marketing, retail, supply chain, entertainment, etc. | Mostly important for the following industries – e-commerce, manufacturing, academics, ML/AI, fintech, etc. |
Business Analysts tend to progress in more business-oriented strategic roles, which also involve entrepreneurship. Contrarily, data scientists are more into research and programming, which makes them better suited for being project managers or head data scientists.
Here is a concise table listing the different career options available in Business Analytics and Data Science field. Please note that the job roles are increasing in their level of position from top to bottom.
Data Science | Business Analytics |
Data Scientist | Business Analyst |
Sr. Data Scientist | Sr. Business Analyst |
Chief Data Scientist | Analytics Manager |
Data Science Lead | Analytics Lead |
Product roles/entrepreneurship | Organisational leadership roles |
Both Business Analytics and Data Science are extremely inviting and innovative fields. If you are interested in understanding data, you will find yourself satisfied in either of these fields. However, there are subtle differences between the two – we hope we clarified that for you in this article!
If you are looking for a career in Business Analytics, check out our Job-ready Program in Business Analytics. All you need is an aptitude for Mathematics, and our experienced faculty will take care of the rest for you. Our course will take you through all the important concepts and tools, including Python, Tableau, Excel, MySQL, etc. And, with our career assistance, we ensure that your journey with us is meaningful forever.
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