Data Science => Data Science Concepts Discussion => Topic started by: 1212490604 on October 12, 2021, 10:33:57 am

Title: Business Analytics vs. Data Science
Post by: 1212490604 on October 12, 2021, 10:33:57 am
Business Analytics vs. Data Science ? Which Path Should you Choose?
Title: Re: Business Analytics vs. Data Science
Post by: 1212490604 on October 12, 2021, 10:36:25 am
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:

Build a business plan to decide how many employees a bank needs to do XXX business in 2021
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.

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.