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Business Analyst Vs Data Science

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You’ve probably heard the statement “Data is the new oil”, and that’s for good reason.

Searched something on Google? Scrolled down your Instagram feed? Bought something on Amazon? Binge watched Netflix? Do you see a pattern here? Data science has become an essential part of our everyday lives.

People are becoming more aware of the significance of data, especially in business, and are flocking towards data-intensive careers. The “Business Analyst” and “Data Scientist” roles are the most popular ones and are often used synonymously with one another.If you’re wondering how they’re different and which is better for you, then this video/blog is going to show you how.

Business problems they help solve

A Business Analyst (can also go by the names of systems analyst, process analyst, or management consultant) in essence is a bridge between the business and information technology groups within the business.

A Business Analyst works with stakeholders across the organization, to understand the goals and needs of the business, and design functional solutions for it.

A Data Scientist on the other hand combines statistical methods and programming in order to create, analyze, and interpret data.

This analysis of data can be used to create actionable insights with respect to:

– improving sales and marketing efforts,
improving operation efficiencies,
– profit optimisation
– handling business risks like frauds, payment defaults, etc.

*Data scientists are highly valued by startups and are in high demand. Startups need data scientists way earlier than they need business analysts.*

What do they do on a day to day basis?

Typically, Business Analysts deliver insights and analysis for internal consumption by various departments like sales, marketing, product design, etc. within the company they work for. Their deliverables include analysis and not code.

This is used to drive changes within the company. This also means they traditionally don’t need computer science backgrounds.

On a day to day basis, Business Analysts –

  • – Gather Stakeholder/business process requirements
  • – Work on optimising systems and processes
  • – Liaise between IT and Business Operations

Business Analysts use tools like –

  • – SQL
  • – Tableau
  • – MS Excel

Data scientists build the infrastructure to integrate data sources, develop meaningful schemas, process the data, and enable analysis.

On a day to day basis, Data Scientists –

  • – Acquire, Process & Clean Data
  • – Integrate & store data
  • – Conduct Data – investigation & exploratory data analysis
  • – Choosing appropriate data models & algorithms
  • – Applying Statistical modelling, Machine learning & Artificial Intelligence
  • – Iterating on the results and presenting it to stakeholders

Data Scientists use tools like –

  • – Python
  • – Numpy
  • – Pandas
  • – SKLEARN
  • – SEABORN
  • – MATPLOTLIB

in 2020, the responsibility of a Business Analyst has become redundant. The Business Analyst Role is slowly being replaced by the Product Manager (PM) role.

Startups are realizing that in order to be efficient, instead of having a Business Analyst as an intermediate, it’s better to have Product Managers instead. This cuts down errors from miscommunication and time delays.

The Payscale

At the entry-level, a business analyst can expect to earn anywhere between $50k to $70k annually.

As a Data Scientist, you can expect an annual salary from anywhere between $110,000 to $150,000 as a Data Scientist.

The payscale varies depending on the state you live in & the size of the company.

Getting Started

Hopefully, this information helps you decide which of these is best suited for you.

If you’re interested in becoming a data scientist, TECH I.S. offers a Data Science program. We offer 1 on 1 mentorship from expert Data Scientists, with 24 x 7 mentor availability. This means you have complete control of your learning schedule. The program also comes with Job placement assistance

Click here to book a FREE session on the foundations of Data science and get a personalized learning roadmap.