Business Analyst Vs. Data Scientist – Which Career Path Should You Choose?
Introduction
Everywhere I look, the terms Business Analytics and Data Science are used consistently.One thing is certain, though: both businesses are experiencing explosive expansion. Currently, there is a $67 billion industry for business analytics and a $38 billion market for data science. The market is anticipated to be worth $100 billion and $140 billion, respectively, in 2025. This indicates that a sudden increase in demand for these two profiles is imminent.
Many aspiring analytics professionals I've met aren't clear about the differences between "Business Analytics" and "Data Science," which they want to pursue as a career. You should be certain of the direction you want to go before making your own decision, right? Making this decision could define your career!
Here, I’ll explain the distinctions between these two roles and also introduce a methodology to choose between the two career paths based on a variety of factors including education, skills, and others.
Business Analyst vs Data Scientist – A Simple Analogy
Let's use an amazing new electric vehicle startup as an example. Today, this startup is well-known for fostering work families. A scientist is one of the three career families they have decided to develop, with engineers and management professionals making up the other two. Now I want you to take a moment to consider the kind of position they might have in the business.
Their function can be inferred from the general degree of understanding:
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Scientist - Focus on solving complex, unique issues like how to construct an effective battery or how to enhance the vehicle's design. Even though the company may not directly benefit from these issues, they are essential for further development. Additionally, these advancements may, in the future, assist startups in experiencing non-linear (exponential) growth.
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Engineers may take these innovations and put them into production using industry procedures. Creating an assembly line, for instance, to produce these cars with the appropriate equipment
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Management: Day-to-day operation of the company and problem-solving about it. To build a vehicle store, for instance, one might need to locate the ideal market. Decisions concerning these products' marketing and sales, among other things.
Let's now transform these jobs into data-based profiles.
Data-Based Functions:
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Data Scientist: He works on intricate and particular issues to help the business experience non-linear growth. Creating a credit risk solution for the banking sector, for instance, or using car photos to automatically analyze damage for an insurance business.
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Data Engineer: He would use industry best practices to implement the conclusions reached by the data scientist in production. Using banking software, for instance, to deploy the machine learning model created for credit risk modeling
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Business analyst: Manage the company's daily operations and make judgments. He will be in constant communication with both the business and IT sides. With the top Business analytics course in Bangalore, you can become an IBM-certified business analyst.
Problems that Business Analysts and Data Scientists Solve
It is crucial to comprehend the issues or tasks that business analysts and data scientists work on in order to distinguish between them. Let's use a fascinating example. Consider yourself a bank manager, and decide to carry out two significant initiatives. A business analyst and a data scientist work on your team. How will you go about mapping the project?
Two issues are listed below:
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Create a business strategy to determine how many staff a bank will need to operate in 2021
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Create a model to identify transactions that are fraudulent Spend some time comprehending the issues. Which problem, in your opinion, fits each profile the best?
Making many business assumptions and incorporating macro changes into the plan are required for the initial problem statement. A business analyst will be responsible for making the necessary decisions because this will demand more business knowledge.
The second problem statement requires processing enormous amounts of customer behavioral data and discovering hidden patterns. The expert should be well knowledgeable about issue formulation and algorithms for this. A data scientist would be the best candidate to take on this kind of particular and challenging issue.
Skills and Tools Required in Business Analytics and Data Science
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Business Analytics
Business analytics professionals must present company simulations and business planning with skill. They would be mostly in charge of analyzing business trends. Web analytics/pricing analytics, for instance.
Excel, Tableau, SQL, and Python are a few of the tools that are frequently used in business analytics. Statistical methods, forecasting, predictive modeling, and narrative are the most frequently utilized tactics.
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Data Science
A data scientist needs to be knowledgeable in linear algebra, programming, and the foundations of computer science. Data science tasks might range from creating recommendation engines to sending customized emails.
A data scientist typically uses R, Python, scikit-learn, Keras, and PyTorch, and the most popular methods include statistics, machine learning, deep learning, natural language processing, and CV. Structure thinking and problem-solving are crucial abilities for both jobs to succeed in their respective fields.
Career Paths
While a business analyst must be more of a strategic thinker and has excellent project management skills, a data scientist has strengths in coding, mathematics, and research skills and needs to continue studying throughout their career.
Business analysts frequently take on managerial, strategic, and entrepreneurial positions as their career develops. In contrast, data scientists—who typically have strong technical backgrounds—tend to take on more closely related roles to tech entrepreneurs.
Final Notes
If you want to learn more about data science and business analytics, you are welcome to enroll in Learnbay's online data science course in Bangalore, to establish a solid foundation on DS and AI.
