According to the Bureau of Labor and Statistics, the number of data analysts hired will likely increase by 25% from 2020 to 2030. As for data scientists, the future looks even brighter—employment is expected to increase by 36% from 2021 to 2031 over a similar period.

Clearly, demand for data analysis is on the rise—now is a great time to land a career that satisfies that demand. And at AI/ML Jobs, we know a thing or two about starting a career in the industry.

In this guide, we’ll look at the roles, responsibilities, and requirements of data analysts and data scientists. Then, we’ll break down the key differences between each to help you decide which role is right for you.

Let’s begin!

Data Analyst vs. Data Scientist: An Overview

Here are the key takeaways from these two roles:

• Both data analysts and data scientists cover a wide range of data analytical tasks.
• Both roles can be found in a wide range of industries.
• Data scientists tend to have more seniority, experience, and responsibilities than data analysts.
• The median wage of data analysts is $86,200 while data scientists’ median wage is $98,230.
• Some of the key differences between data analysts and data scientists include their scope of work and qualification requirements.
• Data analysts generally require a Bachelor’s degree in a relevant field. However, in most cases, a data scientist must have a Master’s degree, while some have PhDs.

What Is a Data Analyst?

A data analyst’s job involves sorting and analyzing data sets to provide solutions or answers to business questions or problems.

Pretty much every modern industry collects data—including: 

• Entertainment (e.g., streaming platform algorithm performance)
• Healthcare (e.g., Electronic Health Records)
• Retail (e.g., customer purchase histories)

A data analyst’s job is to make sense of the data and provide insights that will help the company improve its products or services, find new opportunities, or increase profits.

Data Analyst Roles & Responsibilities

In today’s digital world, data is omnipresent—and there are endless forms to analyze. This means the data analyst role is broad in scope and can vary based on the industry.

Generally speaking, a data analyst’s responsibilities may include:

• Analyzing trends on various levels (e.g., local, national, or/and global) to understand how these trends will affect the business and industry.
• Clean data (i.e., removing or modifying incorrect data, irrelevant data, duplicated data, etc.) to smoothen out the analytical process.
• Develop and maintain data systems and databases.
• Identify and correct code issues by filtering data through reviews of reports and performance indicators.
• Employ automated tools to extract primary and secondary source data.
• The analytical process to determine the meaning and quality of data.
• Preparing final analysis reports to communicate relevant information to stakeholders.
• Exploratory data analysis projects (i.e., scrutinizing data to find patterns).

Data Analyst Requirements & Skills

For such a complex and wide-ranging job, data analysts must meet certain requirements and have the specified skills needed to meet their responsibilities.

Skills and requirements of a data analyst include:

• Superb analytical skills.
• BS in Mathematics, Computer Science, Information Management, Economics, or Statistics.
• Prior work experience in the field of data analysis or business data analysis.
• Relevant technical expertise (e.g., database design development, data mining, and data models).
• Experience with reporting packages.
• Knowledge of databases including SQL.
• Detailed understanding of programming languages such as XML and JavaScript.

Requirements and skills vary depending on the industry and type of position. For example, statistical visualization, econometrics, and machine learning skills may be preferred for one role, while database design and development may be preferred for another. 

Additionally, the ability to extract meaningful insights from large datasets is a must.

What Is a Data Scientist?

Data scientists work to uncover explanations for pertinent organizational phenomena using data. By collecting, analyzing, and interpreting data, these professionals empower organizations to make more informed choices.

The job of a data scientist amalgamates many of the tasks associated with roles like:

• Scientist
• Mathematician 
• Statistician
• Programmer

Data scientists leverage scientific principles and cutting-edge analytic techniques like machine learning and predictive modeling to achieve their objectives. Although data scientists and data analysts may seem interchangeable, they are characterized by distinct qualities that differentiate them.

In the next sections, we’ll dive into these distinctions!

Data Scientist Roles & Responsibilities

As with data analysts, the roles and responsibilities of a data scientist can vary widely depending on their specific employer or industry.

The roles and responsibilities of a data scientist can include:

• Working on prediction systems and ML algorithms
• Cleaning data before performing further analysis.
• Coming up with and expanding hypotheses relevant to the organization’s activities and goals.
• Employing ML tools to create and optimize data classifiers to ensure high-quality and accurate data classification.
• Data mining to discover important patterns and other usable/valuable data.
• Preprocessing structured and unstructured data.
• Reviewing large quantities of data in the search for patterns and answers to the questions.
• Exploring new or unused tools and technologies to formulate original data strategies.

Data Scientist Requirements & Skills

Again, as with a data analyst, a data scientist must possess a range of advanced skills and meet certain criteria to fulfill their responsibilities effectively.

Skills and requirements of a data scientist include:

• Relevant degree (e.g., data science or computer science).
• Programming skills (e.g., R, Python, SQL, etc.).
• Experience with data visualization tools.
• Wide-ranging knowledge of statistics such as a clear understanding of statistical tests, maximum likelihood estimators, and distributions.
• Extensive knowledge of machine learning methods (e.g., k-Nearest Neighbors, SVM, and Naive Bayes).
• Advanced mathematical skills and knowledge.

Key Differences Between Data Analysts and Data Scientists

Scope of Work

One major difference between data analysts and data scientists is the level and seniority of their tasks. Generally speaking, data scientists have greater responsibilities than data analysts. 

To provide some examples, data scientists may have the added responsibilities of:

• Coming up with their own questions regarding data vs. data analysts who tend to support teams with a pre-determined question/objective in mind.
• Greater focus on using machine learning and model development.
• Utilizing advanced programming to decipher and analyze data.

Responsibilities & Expectations

As data scientists often have greater seniority and increased responsibilities and expectations, these roles typically offer higher compensation packages.

According to Glassdoor, the median salary of data analysts stands at $65,553 annually, whereas this figure rises to $104,000 per year in the case of data scientists. These figures include base pay only. Inevitably, a key factor in determining these wages is the scope of work and responsibility involved in the task. 

Clearly, with more tasks and responsibilities, while a data analyst has the potential to work up the ladder, the data scientist stands several steps higher.

What They Do With Data

Data analysts work with structured data, while data scientists go beyond that, utilizing their scientific and analytical skills to apply advanced data techniques for more accurate predictions of future outcomes.

On the one hand, a data analyst may acquire data from primary and secondary sources, analyze data to find trends and patterns and work alongside the company’s leadership to determine what information is required.

Data scientists engage in more advanced and innovative data analysis while seeking to make sense of the unknown. This may include designing predictive models, developing tools to analyze data accuracy, and developing programs for data collection and processing automation.

Skills & Tools

The skills and tools that data analysts and data scientists require to perform their day-to-day tasks also differ. 

Data scientists typically build on the skills and tools used by data analysts. In essence, the work of data analysts lays the groundwork for data scientists to investigate more complex data structures using sophisticated techniques.

For example, a data analyst requires substantial knowledge of SAS, Excel, and business intelligence software. However, a data scientist must also have a clear grasp of software/tools like Hadoop, MySQL, TensorFlow, Python, and Spark.

Qualifications & Requirements

You probably won’t be shocked to learn that data scientists are held to higher qualification standards than data analysts. On the one hand, an IBM survey found the following qualification requirements for data analysts:

• 6% of jobs require that a data analyst have a Master’s degree or higher.
• 76% of jobs expected a data analyst to have 3+ years of relevant work experience.

However, according to a study from Burtch Works:

• 94% of data scientists have a Master’s degree or Ph.D.

Based on the findings of the two studies mentioned, it’s clear that Bachelor’s degree holders in data analysis have ample opportunities to secure employment. However, it’s important to note that data scientist positions require more extensive qualifications, with a majority calling for a Master’s or Ph.D. in a related field.

Conclusion

In this guide, we’ve looked at the role of a data analyst and data scientist while exploring the key differences between these roles.

At AI/ML Jobs, we know that getting your foot in the door of the AI/ML industry can be challenging. That’s one of the many reasons why we’ve built the world’s leading job board for AI and ML roles—to help anyone (from recent graduates to industry veterans) find the perfect AI or ML role for them.

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