Data scientist vs business analyst: what is the difference?

What is the difference between data science and business analyst jobs? And what kind of training or education is…

required to become a data scientist?

There are a number of differences between data scientists and business analysts, the two most common business analytics roles, but at a high level you can think of the distinction as similar to a medical researcher and a medical technician. laboratory. One uses experimentation and the scientific method to seek out potentially revolutionary new discoveries, while the other applies existing knowledge in an operational context.

Data scientist versus business analyst comes down to the domains they inhabit. Data scientists delve into big data sets and use experimentation to uncover new insights from the data. Business analysts, on the other hand, typically use self-service analytics tools to examine curated data sets, create reports and data visualizations, and report targeted results, such as revenue by quarter or sales needed to achieve goals.

What does a data scientist do?

A data scientist takes data analytics and warehousing programs to the next level: what does the data really say about the business, and is the business able to decipher relevant data from irrelevant data ?

A data scientist needs to be able to leverage the enterprise data warehouse to dig deeper into the data coming out or to analyze new types of data stored in Hadoop clusters and other big data systems. A data scientist does not just report data like a typical business analyst does, they also provide data-driven business insights.

A job as a data scientist also requires strong business acumen and the ability to communicate data-driven conclusions to business stakeholders. Strong data scientists don’t just solve business problems, they will also identify the issues that have the most value to the organization. A data scientist plays a more strategic role within an organization.

Education, Skills, and Personality Traits of Data Scientists

Data scientists sift through all available data with the goal of uncovering previously hidden insight which, in turn, can provide a competitive advantage or solve a pressing business problem. Data scientists don’t just collect and report data — they also look at it from many angles, figure out what it means, and then recommend ways to apply the data. This information could lead to a new product or even an entirely new business model.

Data scientists apply advanced machine learning models to automate processes that were previously too time-consuming or inefficient. They use data processing and programming tools – often open source, like Python, R and TensorFlow – to develop new data science applications that take advantage of advances in artificial intelligence. These applications can perform a task such as transcribing calls to a customer service line using natural language processing or automatically generating text for email campaigns.

What does a business analyst do?

A business analyst – a title often used interchangeably with data analyst – focuses more on providing operational insights to business areas using smaller, more focused data sets. For example, a business analyst linked to a sales team will primarily work with sales data to see how team members are performing, to identify members who might need additional coaching, and to research other areas where the team can improve its performance.

Business analysts typically use self-service data visualization and analysis tools. Using these tools, business analysts can create reports and dashboards that team members can use to track their performance. Generally, the information contained in these reports is retrospective rather than predictive.

Training, tools and trends of data scientists vs business analysts

To become a business analyst, you need to be familiar with statistics and the basics of data analysis, but there are plenty of self-service analytics tools that do the heavy math work for you. Of course, you need to know if it’s statistically significant to join two separate data sets, and you need to understand the distinction between correlation and causation. But, on the whole, a deep background in mathematics is not necessary.

To become a data scientist, on the other hand, you need a solid background in mathematics. This is one of the main differences in the question of data scientists versus business analysts.

Many data scientists have doctorates in some area of ​​mathematics. Many have a background in physics or other advanced sciences that rely heavily on statistical inference.

Business analysts can usually learn the technical skills they need on the job. Whether a company uses Tableau, Qlik or Power BI – the three most common self-service analytics platforms – or another tool, most use graphical user interfaces designed to be intuitive and easy to learn. .

Data science jobs require more specific technical training. In addition to advanced mathematical training, data scientists need deep technical skills. They must master several common coding languages, including Python, SQL, and Java, which allow them to run complex machine learning models on big data stored in Hadoop or other distributed data management platforms. Most often, data scientists acquire these skills from a college-level computer science curriculum.

However, trends in data analytics are beginning to blur the line between data science and data analytics. Increasingly, software vendors are introducing platforms capable of automating complex tasks using machine learning. At the same time, self-service software supports deeper analytical capabilities, meaning data scientists are increasingly using tools that were once reserved for business analysts.

Companies often report the greatest analytics success when they mix teams, so data scientists working alongside business analysts can produce operational benefits. This means that the distinctions between data scientist and business analyst could become less important over time, a trend that could pay off for businesses.

Sean N. Ayres