Data Scientist Vs. Business Analyst

Are you looking for a data-driven career? Do you want to be a Data Scientist or a Business Analyst? Standalone degrees for the same are not sufficient to make your mark in a highly competitive job field. You need to earn additional certification and maybe do the R programming course to carve a niche job role in the analytics field. The course teaches you all the skills needed to handle big data. 

Before that, let us examine what these job roles are. What are the differences or similarities, and how does taking an R programming course help steer your career in the right direction?

Who is a Data Scientist

A data scientist is a big data cruncher, who gathers, analyses, and interprets large sets of structured and unstructured data. He uses advanced analytics technologies, including machine learning and predictive modeling, to find patterns and trends, interpret the results to forecast outcomes, and create actionable plans.

Who is a Business Analyst

The Business Analyst enables change in an organization. He identifies technology solutions and guides businesses in improving processes, products, services, and software through data analysis. As an agent of change, the business analyst identifies and executes new opportunities for businesses.

Data Scientist vs. Business Analyst

The terms Business Analyst and Data Scientist are often used synonymously to describe job roles in the data space. A Data Scientist performs business analytics while a Business Analyst identifies deficiencies and opportunities for the organization.

Business analysts deviate from data scientists because their focus is on the business model itself. While a data scientist views problems through a statistical lens, business analysts approach business with a more integrated approach.

The key difference between the two job roles is that of the use of machine learning and feature engineering by the data scientist, and more interactive stakeholder meetings by the business analyst.

Differences

Skillsets: Data scientists use software engineering and analytical techniques.  Business analysts require knowledge of analytics, as well as skills related to communication, analytical thinking, negotiation, technical data manipulation, and management. They do not always use tools that require programming. The learning curve and skill training are different for both.

Responsibilities.  Data scientists automate the tasks of the business analyst and often provide insights as well. They focus on the predictive. Business analysts provide the specifications for the functioning of IT. They focus on the descriptive besides the predictive.

Salary.  The average data scientist’s salary is $100,560, according to the U.S. Bureau of Labor Statistics. Business analysts earn less, at an average annual salary of $75,575. 

Similarities

Both job roles work with data analysis and problem-solving.  They use visualization tools and predictive modeling while analyzing historical data for insights. Business Analysts may become data scientists with additional certifications in database architecture, big data, and big data technologies.

Skills

Languages and tools – Data scientists use the following languages: Python, R, SAS, and SQL. Besides, they use Object-oriented programming (OOP) and Jupyter Notebook. Business Analysts use Excel, SQL, and MS Office, besides visualization tools like Tableau, and Google Data Studio.

Technical Skills – Data scientists use Machine Learning algorithms, big data platforms, and technologies. Business analysts apply IT, Forecasting and Quality Assurance tools to achieve outcomes.

Soft skills – Data scientists need soft skills like problem-solving, domain knowledge, analytical mindset, and communication. Business analysts must have an abundance of presentation and communication skills, problem-solving, analytic and out-of-the-box thinking.

Goals

The goals of a data scientist are to devise cheaper and more efficient solutions and to make the business more efficient. The key goal of the business analyst is to bring about business transformation by determining KPIs, insights and solving business problems of the organization.

Which role is right for you

If you are wondering between a future career as a data scientist and a business analyst, you must explore the type of position you want and prepare for the same.

For instance, do you like working with technologies and tools? Do advanced techniques of machine learning and software engineering excite you? Are you from a pure-play technical or statistical background? Are you able to work with big data and big data platforms like Hadoop and Hive? Do you have good knowledge of database architecture? Does working independently to discover insights sound like your kind of scene? Surely then, you are cut out to be in a data scientist role.

On the other hand, do you like interacting with people? Are you good at summarizing and presenting reports? Will you be comfortable communicating with team members and stakeholders on the go? Are you bold enough to be the technical change-maker in your organization? Do you belong to a non-technical academic background? Then, you can be happy with a business analyst position. 

A Data Science career vs. A Business Analytics career

Data Scientists are involved in the front end of data collection and analysis. They use technical skills, design algorithms, deploy models to analyze, develop and deploy the data. Data Science looks at what drives the trends and patterns discovered in the data.

While a business analyst typically finds trends in the data and discovers ways to use that information to improve business operations. Business analysts use the trends in the data for business transformation in the technical and operational areas.

Courses and certification for data scientists place more emphasis on mathematics, statistics, and machine learning. They also teach coding for the algorithms deployed by the data scientists to discover the cause behind trends and for predictive modeling.

Courses in business analytics teach additional skills and knowledge needed for organizational change. Education includes data management and statistical analysis. Data visualization is also a key component for business analytics courses, as presenting data for actionable decisions is a primary part of the business analyst job role.

Summary

Whether you are thinking about a data scientist position or a business analyst position, consider earning an R programming degree online. Focus on building both technical data science skills and soft skills such as leadership, communication, and project management. These skills are beneficial in either position.

Additionally, a master’s degree in data science master will help you advance to a high-paying, in-demand data science role.