22 Jun 2022

Key Difference Between Data Analyst And Business Analyst

Ekeeda Moderator

Works at Ekeeda

Key Difference Between Data Analyst And Business Analyst

Business Analytics and Data Science – these two terms are co-related and undisputedly both these sectors are undergoing skyrocketing growth.  At present, Business analytics holds a market size of around $70 Billion and Data Science $40 billion. The market size by 2025 is expected to reach by $100 Billion and $140 billion respectively. This means we can expect an upsurge in demand for these two flourishing profiles. Of these, Data science is the hottest career field in the market and quickly gaining popularity over business analytics.

There are millions of aspirants and analytic professionals who want to choose between ‘Business Analytics’ or ‘Data Science’ as their career; but they’re not sure about the prospects & distinction between these two roles. Before we dive into your choice, you should be clear about which path you wish to take, right? After all, it can turn into a career-defining point. 

Here’s what we will suggest - follow our blog and you will get complete clarity between these two roles and introduce a methodology to decide which is the best - business or data science learning path based on factors like education, skills, and others.

Difference Between Data Analyst And Business Analyst: Simple Analogy

Let’s take an example of an electronics start-up. It will create job families such as: Scientists, Engineers, and Management professionals. Now we will take time and see what kind of role they will play in the firm.

We can decide their role from a basic understanding level:

  • Scientist – The role of a Data Scientist is to work on complex, distinct problems such as determining the data on chipsets, circuit boards, electronic components, materials, etc. While these problems will not give direct gain to the firm, they’re crucial for advanced developments. In near future, these developments will help start-ups have non-linear growth.
  • Engineer - Understand these developments and apply industry-oriented techniques to convert them into production. For instance, make an assembly line to manufacture graphic cards, and chipsets using the right tools and machinery.
  • Management – It will run the business and solve business-related issues on a daily to monthly basis. For instance, find the right marketplace, and strategies to open a store for electronics products. Decisions regarding sales & marketing of these products create a buzz in the market and others.

Data Science And Business Analytics Job Role: Technical

  • Data Scientist – He will work on complex and specific problems, derive valuable insights from data, and bring non-linear growth to the company. For instance, making a credit risk solution for banking or using images of electronic components and assessing to insurance company automatically. 
  • Data Engineer – He or she will bring the outcomes derived by the data scientists into production by using industry best practices. For instance, deploying the ML model built for credit risk modelling on banking software. 
  • Business Analyst – He or she will run the business and take decisions on a day-to-day basis. BA will communicate with the IT team and business side in tandem to enhance the output.

This is a very basic term that you need to keep in mind while differentiating the role from Data Analyst To Data Scientist and Business Analysts.

Although these terms are commonly used in the industry, the exact role could depend on the experience of your company in data initiatives. Now that we have our basic terminology clear, let us see the kinds of problems solved by various Data scientists and Business Analysts.

Data Scientist And Business Analyst: Solutions

To understand the difference between a business analyst and data scientist, you should know the problems or projects they are working on. Let us take an example – Imagine you’re a C. A and you decide to implement two important projects. You have a team of data scientists & business analysts. How will you define the job? Here are two problem statements: 

  • Work out a plan to decide how many employees the company needs to do business in 2022
  • Build a model to predict which transaction is Fraud

Take your time to understand the problems and figure out which problem is the best suited for your profile. In the first problem, you will have to make several business assumptions & incorporate macro changes in the strategy. This will require more business expertise & decision-making. It will be the job of a business analyst.

The second problem, it requires processing vast behavourial data from customers and understanding their hidden patterns. For this, an individual should have a good understanding of problem formulation & algorithms. A data scientist would be the ideal person to tackle this kind of specific & complex problem.

Data Science And Business Analytics: Skills & Tools

Data Scientist has to be proficient in Linear Algebra, Programming like C++, Java, and Python, and computer science fundamentals. Some instances include varying from building recommendations to personalized messages. 
The common tools used by Data Scientists include R, Python, Keras, Numpy, Pandas, PyToch. And the commonly used techniques are Statistics, Machine Learning, Deep Learning, and NLP. 
On the other hand, Business Analytics should be proficient in presenting business simulations & business planning. A major part of their job role will be to analyze business trends and techniques. For instance, web analytics or pricing analytics. Some tools extensively used by business analysts are Excel, Tableau, SQL, and Python. And the most common techniques will be – Statistical Methods, Forecasting, Predictive Modelling, and storytelling.

Well, we can see the key differences in both the roles, but structure thinking & problem foundation are the key skills to perform well in your respective domains.

Data Science And Business Analytics Roadmap: Career Path

A Data Scientist's strengths lie in coding, maths, and research abilities. He or she requires continuous learning through the career journey.

A business Analyst needs to be more of a strategic thinker & should have a strong ability in project management. He or she tends to take business, strategic, and entrepreneurship roles as they progress through their career. While Data scientists adapt more to tech-entrepreneur roles because they are sound in technical background.

Refer to the following career path to see a more in-depth route from the start of Data Science & Business Analytics Journey: 

  • Data Scientist > Sr. Data Scientist > Chief Data Scientists > Data Scientist Leader > Product Roles / Entrepreneurship
  • Business Analyst > Sr. Business Analyst > Analytics Manager > Analytics Industry Leader > Strategy Leader/Organization Leader

On An Ending Note –

Over here we have covered basic points about the key difference between Data Science and Business Analytics. If you wish to learn in-depth about these two crucial business elements, you can take up the course and build a strong foundation. 

There are a lot of business analytics courses in the market that will answer curious questions like: What is Business Analytics? What Is Data Science? A career in Business Analytics Or Data Science, Spectrum Of Business Analytics, MIS, BI, Predictive Modelling, AI & ML, Skills Required in Business Analytics Roles, Data Science Roles, etc. 

However, if you’ve set your heart on a data science role, check out Ekeeda's ultimate Data Science Online Course designed for students and young professionals. What’s more? Get industry-relevant training, 1:1 live classes, 100+ assignments and capstones, and placement assistance in top techs and start-ups such as Datascope, Tiger Analytics, Cloudera, Brainvire, IBM, Yalantis, Peerbits, and more.

Set a clear Data Science Roadmap which defines the milestones in your career journey. Use this roadmap to track your Data Science Journey, see where you stand in preparation and what is your next move!

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