Data Analyst vs. Data Scientist: What’s the Difference?

There are two options for you if you’re looking to work with big data and crunching figures. You can choose to become a data analyst, or a scientist. What are the differences between data analysts and scientists? Let’s take a look at both the similarities and career paths of each discipline.

Employers seek professionals with data-driven skills like analytics, machine learning, and artificial intelligence. Data scientists and analysts are highly sought after, with salaries often exceeding the national average.

What does a Data Analyst do?

An data analyst usually collects data to identify trends that can help business leaders make strategic decision. This discipline is focused on solving problems and answering questions through statistical analysis. To query relational databases, a data analyst may use tools like SQL. Data analysts may clean or format data to make it usable. They can also figure out how to deal with missing or irrelevant data .

Data analysts are part of an interdisciplinary team that helps to define the company’s goals, then manage the mining, cleaning, and analysis of the data. Data analysts use programming languages such as SAS and Tableau and communicate their findings using communication skills.

What does a Data Scientist do?

A data scientist will be involved in designing data modeling processes, creating algorithms, and predictive models. Data scientists might spend more time creating tools, automation systems, and data frameworks.

A data scientist, in contrast to a data analyst, may be more interested in developing new tools and methods that can extract the information needed to solve complex problems. To understand the implications of data, it’s a good idea to have business intuition and critical thinking skills. A data scientist is someone who has the ability to use innovative approaches to solving problems, not just mathematical and statistical knowledge.

Data Scientist and Data Analyst: Differences and similarities

Both careers require at least a bachelor’s degree in quantitative fields such as statistics, mathematics, or computer science.

Data analysts may spend more time performing routine analysis and providing regular reports. Data scientists may be responsible for designing the storage, manipulation and analysis of data. A data analyst can make sense of existing data. A data scientist creates new ways to capture and analyze data for analysts.

Both a career in programming and numbers is possible if you are passionate about math and statistics. Analysts are responsible for answering specific questions about an organization’s operations. Data scientists may be asked and answered important questions at a macro level.

Each role is focused on the analysis of data to gain actionable insight for their organization. However, it’s sometimes determined by the tools they use. Data analysts need to be familiar with statistical software, relational database software and business intelligence programs. Data scientists use Python, Java, and machine learning to manipulate data and analyze it.

Data Analyst vs. data scientist: Education and work experience

A bachelor’s degree in quantitative fields such as statistics, mathematics or computer science is necessary to become a data analyst/data scientist. Some analysts might have a bachelor’s degree in business with an emphasis or concentration in analytics.

Education According O*NET OnLine, 76% of business intelligence analysts hold a bachelor’s Degree while 14% have a master’s Degree. Predictive analytics professionals (PAPs), who are data scientists, are more likely to have an advanced degree. A Burtch Works study on the salaries of data scientists, and predictive analytics professionals was released in August 2020]. At least 94% of the data scientists in the study had a master’s or doctorate, and 86% of PAPs also had a master’s or doctorate. It was also revealed that professionals with advanced degrees tend to earn more than those with a bachelor’s.

Work Experience Professionals can move their careers in another direction by attending Master’s Programs in Data Science and Bootcamps in Data Science . Professionals with work experience may be in higher demand. Burtch Works discovered that 42% of data scientists and 37% PAPs had less than five years experience.

Data Scientist vs. Data Analyst: Roles and Responsibilities

The role and responsibilities of a data analyst or data scientist will vary depending on their industry and where they work. Data analysts may be responsible for determining the cause of an event, such as sales dropping, or creating dashboards to support KPIs. Data scientists, however, are more interested in what could or will happen. They use data modeling techniques and big-data frameworks like Spark.

You may find it helpful to carefully read job descriptions in order to get a better understanding about the expectations of a company. Sometimes, data scientist job postings may include the duties of data analysts and vice versa. Here are some common job responsibilities for data scientists and data analysts to help you understand the differences.

Data analysts:

• Data querying using SQL.
• Excel can be used to forecast and analyze data.
• Creating dashboards using business intelligence software.
• You can perform various types of analytics , including predictive, predictive, and descriptive analytics.

Data scientists:

• Data scientists can spend as much as 60% of their time cleaning data.
• Data mining with APIs and building ETL pipelines.
• Data cleaning with programming languages (e.g. Python or R.
• Machine learning algorithms are used to analyze statistics such as logistic regression, natural language processing, logistic regression and kNN.
• Programming and automating techniques such as libraries that simplify daily tasks using tools such Tensorflow to create and train machine learning models.
• Big data infrastructures can be developed using Hadoop and spark, and other tools like Pig and Hive.

Each role analyzes data to gain actionable insights that can be used to make business decisions. Data analysts use SQL, SAS, a business intelligence software, and SAS, a statistical tool, while data scientists use Python and JAVA to make sense of data.

Data Scientist vs. Data Analyst: Skill Comparison

While there is some overlap between skills and those of data analysts, the most important difference in analytics is that data scientists use programming languages like R and Python, while data analysts use SQL and Excel to query, clean, or make sense. The tools or techniques they use to model data are another difference: Data scientists typically use machine learning, while data analysts use Excel. Some advanced analysts might be familiar with programming languages, or may have a working knowledge of big data.

Here are some common skills that data scientists and analysts have in common.

Data Scientists vs Data Analysts

Job Outlook: Data Analyst vs. data scientist

The salary of a data analyst or data scientist will vary depending on the industry they work in and their employer. Data scientists have a bright future and the projected growth is between. O*NET estimates that data analysts could earn a median salary of $98,230.

Data Science vs. Data Analytics: What are the Differences Between These Two Careers?

Some data scientists might choose to focus their expertise on areas other than computer science. They can also pursue an online master’s program in data science to further their career.

Data scientist is a route that teaches data scientists how to process, analyze, model, and draw conclusions from data. Data lakes are used by data scientists to store unstructured data and allow for analysis.

Data analysts might be trained to use analytics technology, business intelligence and statistics to answer specific questions.

Data analysts and data scientists can benefit from soft skills in order to communicate their findings and work together. They must be able to understand the priorities and nuances of their organizations and use critical thinking and business intuition in order to communicate their findings and process.

Career Growth

An entry-level position for a data analyst could be where they report and create dashboards. Next, you might consider a position that requires advanced analytics or strategy. An advanced analyst might be interested in a managerial position and become an analytical manager after having worked for more than nine years. A data analyst might continue their education to improve their skills and become a data scientist.

There’s a skill gap in data science. There are more positions available than there are qualified professionals to fill them. These companies are seeking career-changers to fill these positions. They also need to train their employees. A data scientist currently employed may decide to pursue a doctorate in order to be able to work in more advanced data science positions.

Data Scientist vs Data Analyst FAQ

Which degree is better: Data science or data analysis?

Your professional and personal goals will determine which degree is best for you. A degree in data analytics is a good choice if you are interested in statistical modeling and data processing. A degree in data science may be right for you if you are interested in machine learning and big data.

Is it possible for a data analyst to become a data scientist

A data analyst can transition to a role as a scientist by recognizing similarities between the roles of data scientist and data analyst. Every person’s journey is unique, but there are common steps that can be taken to acquire relevant data science skills.

What are the most common skills of data analysts and data scientists alike?

Data analysts and data scientists share common skills such as data mining, data warehouse, statistics, and data visualization. Some data analysts might use Python or R depending on their job.

What salary is the difference between a data analyst and a data scientist?

O*NET Online reports that data scientists made a median income of $98,230 in 2020. O*NET and the Bureau of Labor Statistics do not report salaries for data analysts. In comparison, the median salary for a similar role like a financial analyst was $83,660 by 2020.