Data Analyst vs Data Scientist: A Comparative Guide

Data Analyst vs Data Scientist

Due to advances in technology, there has been an increase in data usage. As a result, various industries are analyzing data to help streamline and automate their processes. Different industries also use data to forecast behaviors, challenges, and wins. 

With all the ways businesses can now use data, data-based careers have never been more prominent. Two professions in particular that use data are data analysts and data scientists. While very similar in their roles and responsibilities, data analysts and data scientists differ from one another. Discover the differences between a data analyst vs a data scientist. 

What is a Data Analyst?

A data analyst examines large datasets to uncover trends and actionable insights that can help companies optimize processes, solve problems, and make informed business decisions. Analysts must collect, clean, and analyze data using different statistical and computational methods to examine data for such insights. 

What is a Data Scientist?

A data scientist is someone who uses his or her expertise, along with artificial intelligence and data mining techniques in the form of statistical models and computer science, to collect, analyze, and interpret complex data, create predictive models, and make business forecasts. The goal of a data scientist is to find data patterns and extract insights that will help companies make informed decisions and drive business value. 

Prerequisite Requirements to Becoming a Data Analyst

To become a data analyst, you’ll need a strong background in mathematics, statistics, and computer science, along with programming language proficiency. While having an official bachelor’s or master’s degree in these areas is no longer a technical requirement to working in the field of data as an analyst, it can make it easier to get a data analyst job. 

Regardless of whether you have the technical education to be a data analyst, you should always gain practical, real-world experience in the field and build a strong portfolio of skills. More than anything, practical, real-world experience and a strong portfolio will help further your data analyst career.

Prerequisite Requirements to Becoming a Data Scientist

To become a data scientist, you’ll need working experience with big data, artificial intelligence, and data mining techniques, along with proficiency in programming languages and a strong background in mathematics, statistics, and computer science. To be successful as a data scientist, you’ll also need great communication skills and a passion for solving complex problems and making predictions with data. 

Benefits of Becoming a Data Analyst 

One key benefit to becoming a data analyst vs a data scientist is that as a data analyst, you’ll learn all there is to know about data analytics, which is the foundation for all data professions, including the job of a data scientist. Another major benefit of becoming a data analyst vs a data scientist is that you’ll better understand exploratory data analysis. 

Becoming a data analyst also allows you to improve your ability to collaborate with stakeholders and participate in feature creation. A career as a data analyst will even help you articulate insights and master visualizations. Because data analytics is a component of data science, becoming a data analyst could also be a great stepping stone to one day becoming a data scientist. 

Benefits of Becoming a Data Scientist 

Just as there are benefits to becoming a data analyst, there are benefits to becoming a data scientist. One is that data science can better help you build your knowledge of mathematics. Data science can also help you become more comfortable with programming languages. 

Becoming a data scientist will even still allow you to learn data analytics. This is because data analytics is a component of data science. 

In addition, becoming a data scientist will likely earn you a data science certification, which will give you more freedom in the future. Working as a data scientist will even help you gain the ability to work across different data disciplines. 

Skills Needed to Become a Successful Data Analyst

If you want to succeed as a data analyst, you’ll need to master some hard and soft skills. Hard skills are the technical skills that allow you to do your job. Soft skills are the personal qualities that help make you more effective at your job and a more pleasant person to work with. The hard or technical skills that you’ll need to master as a data analyst include the following:

Hard Technical Data Analyst Skills

  • Proficiency in SQL and other programming languages
  • Advanced Excel skills, including the ability to use pivot tables and various lookup functions
  • Experience working with data visualization tools such as Tableau or Power BI
  • Understanding of statistical analysis, mathematics, and modeling techniques
  • Familiarity with database management systems and project management
  • The ability to prepare detailed reports
  • Understanding the business context around data

Soft Data Analyst Skills

The soft skills that you’ll need to be successful as a data analyst include the following:

  • Excellent written and verbal communication skills
  • Critical thinking 
  • Problem-solving skills
  • Attention to detail 
  • The ability to work accurately with large datasets
  • Strong organizational skills 
  • The ability to manage time effectively
  • Collaboration and teamwork skills to work across departments and with stakeholders
  • A passion for learning that will help you stay up-to-date with emerging technologies and trends in the field

Skills Needed to Become a Successful Data Scientist

Just as there are hard and soft skills that all data analysts need, there are also hard and soft skills that data scientists need to be successful. These hard and soft skills are detailed below. 

Hard Technical Data Scientist Skills

  • Proficiency in programming languages such as Python, R, and SQL
  • Experience with data analysis and visualization tools such as Pandas, Matplotlib, and Seaborn
  • Understanding of machine learning and deep learning algorithms and frameworks such as TensorFlow and Keras
  • Familiarity with big data technologies such as Hadoop, Spark, and Hive
  • Expertise in advanced statistical analysis and mathematics
  • Machine learning and predictive modeling skills and techniques
  • Knowledge of data cleaning and pre-processing techniques
  • Experience with cloud computing platforms such as AWS and Azure
  • Knowledge of artificial intelligence techniques

Soft Data Scientist Skills

  • Excellent communication skills, including the ability to explain complex concepts to both technical and non-technical stakeholders
  • Critical thinking and analytical skills to design practical solutions to complex business problems
  • Ability to work collaboratively with cross-functional teams from different backgrounds and domains
  • A strong sense of curiosity
  • Ability to innovate and develop new techniques
  • Ability to solve complex problems 

Common Tools Data Analysts Use

Various specialized tools help data analysts and data scientists work with and analyze large datasets. Some of these tools are more specific to data analysts, while others are more specific to data scientists. 

Some of the most popular tools data analysts use include statistical software such as R and SAS, programming languages such as Python and SQL, and data visualization tools such as Tableau and Power BI. Data analysts also often use Excel to manage smaller datasets

Common Tools Data Scientists Use

Data scientists use all the tools data analysts use, plus machine learning frameworks such as Tensor Flow and PyTorch. They also often use big data technologies to process and analyze large datasets. Data scientists may also store and manipulate data using cloud computing platforms like AWS and Azure. 

Salary of a Data Analyst vs Data Scientist

In the United States, the average salary for a data analyst is $60,000 to $80,000 a year. The salary of a data analyst ranges due to factors such as work experience, work title, geographic location, and field of work. Data analysts with specialized skills can make more than those without specialized skills. 

On average, data scientists in the United States make between $85,000 and $120,000 or more a year. In competitive fields of work, such as technology and finance, they can make more than $150,000 annually. 

Data scientists who use AI and machine learning tend to make more than those who don’t use these tools. Data scientists in more senior-level positions also make more money than entry or mid-level data scientists. 

Data Analyst Career Path vs. Data Scientist Career Path

As similar as the roles of data analyst and data scientist are, the career paths for each position are different. Data analysts typically enter the data analysis field as entry-level employees. For example, data entry clerks and research assistants are great entry-level jobs for anyone interested in becoming a data analyst. Many data scientists enter the data science field as research assistants or junior data scientists. 

Once an aspiring data analyst gets promoted from an entry-level position as a data entry clerk or research assistant, he or she can move up to junior data analyst or business intelligence analyst. Once promoted again, a junior data analyst or business intelligence analyst can become a senior data analyst or take on a leadership role within the company. Examples of such leadership roles include data architect and business analyst. 

Once a data scientist gets promoted from a research assistant or junior data scientist, he or she can advance to roles such as data scientist or senior data scientist. With more experience, education, and specialized skills in areas such as big data and machine learning, data scientists can even get promoted to roles such as machine learning engineer or director of data science. 

Key Difference Between Data Scientist and Data Analyst Roles and Responsibilities

The key difference between a data analyst and a data scientist is that a data analyst’s role is primarily exploratory, while a data scientist’s role is experimental. Data scientists use AI and data mining techniques to create predictive models and make business forecasts. Data analysts simply uncover trends and actionable insights to help optimize processes or solve current business issues. 

Data scientists also have more expertise than data analysts regarding data usage. As a result, data scientists are more hands-on with advanced programming techniques and computing tools. Data scientists should also have more knowledge of developing data models and algorithms. 

Another way to describe the key differences between a data analyst vs a data scientist is by which form of data analytics each role uses. The three primary forms of data analytics that data analysts and scientists use are descriptive, predictive analytics, and prescriptive. 

Descriptive analytics answers the questions of what and why in terms of business problems. Descriptive analytics also uses historical data patterns, trends, and anomalies to provide actionable insights. 

Predictive analytics use past data trends and machine learning models to estimate future events. Prescriptive analytics then uses the outcomes of descriptive and predictive analytics to improve business strategies. 

Essentially, data analysts employ descriptive analytics and sometimes prescriptive analytics by way of recommendations, while data scientists employ descriptive, prescriptive, and, most importantly, predictive analytics. Therefore, data analysts don’t have the knowledge of AI and machine learning models of data scientists to host experiments and make forecasts to drive business value. 

Take Advantage of CFI Courses and Certifications for Data Analysts and Data Scientists

Whether you’re trying to build a career as a data analyst or a data scientist, you’ll need to continuously educate yourself on the field and build on your skill set. Fortunately, CFI contains various courses and certifications in data science and analysis that you can take advantage of. 

For example, you can learn about data analysis in Excel by enrolling in CFI’s Data Analysis in Excel Specialization. If your interests include business intelligence, CFI offers the Business Intelligence and Data Analyst Certificate Program. You can also learn about data science with CFI with the Data Science Analyst Certificate program.

Additional Resources

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