What Is Data Analyst Role

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Data analyst and data scientist are two popular data-related careers in the technology industry. Although the jobs are related and have many related work tasks, they are not the same.

What Is Data Analyst Role

So what is the difference between a data analyst and a data scientist? Are the responsibilities and skill sets different? What are the salaries and job prospects for both career paths? In this guide, we will cover the following:

Data Analyst Vs. Data Scientist: What’s The Difference?

How do the roles of data analyst and data scientist compare? Data scientists typically have the skill set of a data analyst and additional skills, explains Jenna Bellassai, senior data reporter and former chief data scientist at Forage.

“Many people who become data scientists start out as data analysts, but not all,” Bellassai said. “A data analyst specializes in manipulating data to create reports or dashboards, while a data scientist combines data analysis, software engineering, and machine learning to create statistical models.”

Another key difference is that the role of the data analyst has become ubiquitous. As a result, analyst skills are in high demand across industries, domains and companies of all sizes, says Aswini Thota, Chief Data Scientist at Bose Corporation, who has over 12 years of experience in data analytics.

On the other hand, data scientists have a deeper knowledge of mathematics and computer science. Data scientists are valued for their ability to build accurate models for strategic decision making.

How To Hire The Right Data Professional For Your Business

When considering careers for data analysts and data scientists, it’s important to consider some of the factors that distinguish each of these data-oriented jobs. “Data analysts use their extensive domain knowledge and analytical skills to explain the factors that drove the outcome,” says Thota. “In contrast, data scientists develop predictive models that can ‘learn’ hidden relationships and rules within historical data to predict future outcomes.”

While some job elements of these professional data overlap, significant differences emerge when comparing responsibilities, skills, and salaries.

A data analyst’s responsibilities include understanding business problems, identifying relevant data sets, cleaning and de-cluttering data, and analyzing it to help businesses make decisions. They begin collecting data from various sources, such as databases, spreadsheets, and other business systems.

On the other hand, the responsibilities of a data scientist are mainly to develop and implement predictive models and algorithms to solve strategic business problems. They begin to understand the problem they are trying to solve and identify the data needed to solve it.

Business Analyst Or Data Analyst? — Data Budd

However, the roles are also similar. “The primary goal of data analysts and data scientists is to use data to inform business strategy and decision making,” explained Thota. “Leveraging historical data assets to derive insights that drive business growth.” Data collection, data cleaning, and data analysis are responsibilities shared by data analysts and data scientists.

While data analysts and data scientists require similar skills to clean, transform and analyze data, each career requires both hard and soft skills.

“Data scientists need to have a broader understanding of statistical modeling and machine learning algorithms, as well as programming and cloud computing skills,” Thota said. “On the other hand, data analysts must be proficient in using data analysis tools, data visualization and reporting techniques, and have good communication and collaboration skills to work effectively with stakeholders.”

Soft skills that help data analysts in their work include strong communication and collaboration skills to work effectively with team members.

Data Engineer, Data Scientists, Data Analyst, And Business Analyst — What Are The Differences And Which Career Should You Choose?

Soft skills that can benefit data scientists include problem-solving skills and the ability to identify and solve complex data problems and the ability to work in a team environment.

In the following table, Thota highlights the skill sets required for data analyst and data scientist roles, as well as some common skills useful for both positions:

Although data analysts and data scientists have some overlap in their skills and responsibilities, data scientists can command a higher salary. “Data scientists, on average, earn higher salaries than data analysts because of their specialized skill set and strong understanding of computing and machine learning,” explains Thota.

According to Glassdoor, the average gross salary for data analysts in the U.S. is around $70,000, with higher-level titles earning upwards of $114,000. In contrast, the average annual salary for data scientists is over $126,000, with the potential to earn over $162,000 in gross salary for senior data scientists and those with extensive experience.

How To Become A Data Analyst In 2023: 5 Steps To Start Your Career

The US Bureau of Labor Statistics (BLS) says job prospects are above average for operations research analysts, a job category that includes data analysts. From 2021 to 2031, data analyst jobs are projected to grow 23%, faster than the average for all occupations.

For data scientists, the job outlook is better, according to the BLS. This area is expected to see employment growth of 36% between 2021 and 2031, and 40,500 new jobs are expected to be added during that time.

Data analysts and data scientists use data to inform business strategy and decision-making, deriving insights from data that drive business growth. These two high-demand tracks offer professionals the opportunity to make data-driven decisions to shape the future of an organization.

However, each role has a unique approach, so the path you choose should reflect your career interests. If you’re more interested in using data to flex your domain knowledge and analytical skills and help explain the factors that drive results, you might enjoy being a data analyst. But if you enjoy building models and algorithms that can dig deep into historical data to predict future outcomes, you’d probably prefer to be a data scientist.

Business Analyst Vs. Data Analyst: Differences, Skills & More

When determining the most appropriate career path, Thota emphasizes that it is important to understand the complexity of the problems you want to work on. “While data analysts often work with structured and well-defined data sets to solve specific business problems, data scientists may work with unstructured data sets to predict and quantify the unknown,” he says.

It’s important to think about your programming ability and your penchant for tactical versus strategic solutions.

“Although both data analysts and data scientists use programming tools like SQL, Python, R and Tableau, the way they use them and the goals they want to achieve may differ,” Thota said. “Do you prefer communicating insights or building scalable systems? Data analysts are valued for the insights they bring to the table, data scientists are expected to build scalable models that provide predictive capabilities.”

He added that business leaders often rely on data analysts to provide quick insights or tactical recommendations to help correct course or mitigate immediate risks. Data scientists, on the other hand, should be comfortable with longer results. “Most projects that data scientists work on are strategic, and it can take months or years to fully utilize predictive models,” concludes Thota.

Organizational Data Analyst Role Functions

Robin Madell spent more than two decades as a corporate writer, business journalist and communications consultant in New York, San Francisco and Los Angeles. NOTE: Most data and analytics roles are undefined. This article focuses on the role of the data analyst, often described as a BI analyst. Depending on the organization, there is also some overlap between the role of a business analyst and a data scientist.

The role of the data analyst has evolved rapidly in recent years. With the explosion of data complexity and business expectations, they face many challenges.

In the early years and in companies with less analytical maturity, the data analyst became a general role. He has been responsible for almost the entire marathon of data and analysis. From preparation and visualization to vision analysis and communication.

Growth and innovation in the field of data analytics has changed the definition of data and the requirements of the data analyst role. This led to the need for specialist roles and a strong emphasis on the last mile. In this way, data analysts are more focused on analysis, insights and decision making.

What Does A Data Analyst Job Description Look Like?

In line with this idea, Cassie Kozyrkov defines the main role of data analysts as “fact finding and inspiration for you” and focuses on the fast nature/pace of the work saying “The game of analytics is about optimization. inspiration”. -per minute”.

Speed ​​to actionable information plays a key role in increasing the value of analytics and helping data deliver on its promise. Therefore, it should be defined as the North Star metric for data analysts. (Read more about acceleration here.)

However, most companies are far from it, due to various challenges. Let’s look at what holds data analysts back.

Data complexity and business expectations have increased. At the same time, traditional BI tools have not changed much in the last 20 years, creating a gap in many data sets. Here are the main challenges facing data analysts today:

Data Analyst Vs Data Engineer Vs Data Scientist

There is a general problem with defining roles in data and analytics. This isn’t just for data analysts. His role with data scientists and BI analysts (and

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