Data Analyst vs. Data Scientist: Which Should You Pursue?

It can be difficult to differentiate data analysis from data science, as the fields are closely related in many ways. Broadly speaking, if you love problem solving, data-driven solution making, and critical thinking, both careers could be a great choice for you. 

While both options draw from the same basic skill set and work toward similar goals, there’s a difference between a data scientist and a data analyst in education, abilities, daily tasks, and salary ranges. Here, we will take a closer look at each career path to help you identify which job better matches your interests, experience, and goals. 

Differences Between Data Analysts and Data Scientists

Data analysis and data science can be easily confused because they draw from the same basic skills — not to mention the same broad educational background (e.g., advanced mathematics, statistical analysis). However, the everyday duties of each role are very different. In its most basic form, the difference is what they do with the data they gather. 

A data analyst analyzes collected data, organizes it, and cleans it so it is clear and useful. They make suggestions and decisions based on the information gathered. They work as part of a team that turns raw numbers into information that will help businesses make smart decisions and investments. 

A data scientist develops the tools a data analyst will use. They create algorithms, build models, and design data capture systems. Data scientists are always thinking about new ways to capture, store, and view data, and are creative problem solvers. 

Data analysts and data scientists tend to have similar educational backgrounds. Most have bachelor’s degrees in areas such as math, statistics, computer science, and artificial intelligence. They have a comprehensive understanding of data, markets, communication, and machine learning. They can work within advanced software, databases, and Python programming. 

Employees in either field can advance their skills through programs such as data boot camps to increase their efficiency and effectiveness at work. Bootcamps can provide you with the technical and practical skills necessary to start or advance your career and understand how the position fits into the business structure.

Data Scientist vs. Data Analyst Responsibilities

In both the data science and data analysis fields, professionals need to be comfortable with data management, information management, spreadsheets, and statistical analysis. They must manipulate and structure data in a way that is useful and understandable to business stakeholders. They also measure how well companies perform against defined KPIs, as well as uncover trends and explain unexpected variances. 

While the job responsibilities often overlap, there are differences in the roles of data scientists vs. data analysts and the methods they use to achieve these objectives. 

What Does a Data Analyst Do?

Data analysts are master translators. They use large data sets to understand what is happening in the market and how business decisions impact the way customers view and interact with the business. They are driven by a desire to understand the motivations and actions of people through analysis of collected data. 

Everyday data analyst tasks may include:

  • Analyzing past and current trends and patterns
  • Developing financial and operational reports
  • Performing forecasting in programs like Excel
  • Creating dashboards
  • Interpreting data and communicating it clearly 
  • Cleaning data by reviewing reports and correcting coding errors

Sample Data Analyst Job Description

When looking for a job as a data analyst, you may find the titles differ within certain businesses. However, the duties and responsibilities will all be similar. This is one example of a job description you may see while searching for employment:

You will be responsible for managing a master data set, developing reports, and correcting problems with data collection. You should be attentive to details and proficient with data analysis tools and databases. You will be responsible for managing and designing data collection systems and will ensure the integrity of the data sets. You are comfortable with large data sets and can translate complex data into a lay language to assist the business in making well-educated decisions. As part of the management team, your expertise with data interpretation will be essential to business growth and goals. 

What Does a Data Scientist Do?

Data scientists create the structure to capture data and better understand the story it tells about the market, the business, and the decisions made. They are architects who can build a system that supports the amount of data needed and makes it useful for understanding trends and informing the leadership team. 

Tasks regularly performed by data scientists include:

  • Data mining and scrubbing
  • Statistical analysis of collected data
  • Training and developing machine learning models
  • Creating automation that simplifies the daily tasks of data collection and interpretation
  • Developing infrastructure that can handle big data
  • Employing predictive analytics to identify future trends and influence them
  • Communicating insights to the the management team, helping support data-driven decision making

Data Scientist Sample Job Description

While the exact requirements for a data scientist will differ based on the industry, all data scientists are expected to be comfortable working with large sets of unstructured data. A typical job description for a data scientist may look like this:

As a data scientist, you will collect large data sets, analyze the results, and format it in a useful and understandable report. You will be comfortable experimenting with data collection and the common tools used to manipulate outcomes and excel at finding solutions to business problems. You will be a fast thinker and flexible problem solver. You must be familiar with AI and machine learning systems, and you must be able to visualize and conceptualize large amounts of data for the rest of the management team. We are looking for someone committed to using data to improve business decision-making, refine our marketing, and enhance our product offerings. 

A graphic that illustrates the differences between what a data analyst and data scientist does.

Data Analytics vs. Data Science Education Requirements

Most companies looking to hire a data scientist or data analyst will expect applicants to have at least a bachelor’s degree in a related field. For some positions, companies may even expect you to have a master’s degree or Ph.D in fields like data science, computer science, statistics, applied mathematics, finance, or even psychology. 

During your studies, you should focus on classes in higher mathematics, like statistics, algebra, and calculus. Computer science classes will also give you useful skills in database design, data analytics, and management. Taking classes in business will help you to understand how the data will be used to make decisions. And finance, business theory, and economics courses can prepare you to connect data sets and real-world business decisions. 

Whether you want to pursue data science or data analytics, you’ll be required to learn basic data collection and interpretation skills which are taught in bootcamps like The Data Analysis and Visualization Boot Camp at Texas McCombs. This is a great way to refresh your skills and quickly gain new and updated knowledge about the field online, with flexible hours that accommodate your work schedule. 

Data Analyst Education

Nearly all entry-level positions in data analytics require a bachelor’s degree. Common fields of study include information technology, computer science, or statistics. Minoring or taking additional classes in database management, project management, and business theory will also make you more marketable. 

As a data analyst, you may want to consider continuing your education with a master’s degree or certificate program. Many schools offer programs in data science, data analytics, or big data management that will introduce you to the newest technologies available in data capture, as well as provide internship opportunities with corporations that are leaders in the data analysis field. 

Certificate programs, bootcamps, and independent study are all great methods of advancing your skills in data analysis. Consider starting with these four books that every data analyst should read. 

How Long Does It Take to Become a Data Analyst?

Most bachelor’s degrees take four years to complete. If you choose to pursue an advanced degree, a master’s degree in data analysis usually takes around two years to complete. 

Learning the specific systems used in data analytics can be done in a few months. There are certificate programs and bootcamps that will teach you to use SQL, Python, or advanced Microsoft Excel skills. You can complete a data analysis bootcamp to refresh or increase your skills in less than a year. You’ll study all the tools and technologies you will use as a data analyst and be prepared to use them in real-world settings like finance, business, health care, and government. 

Data Scientist Education

For an entry-level position in data science, a bachelor’s degree is expected. Programs at many colleges or universities will prepare you for a career in data science. Popular majors include information technology, applied mathematics, computer science, economics, finance, and statistics. For those interested in a career in a specific industry sector, consider programs like artificial intelligence, mathematical biology, or computational science. 

While a bachelor’s degree can get you started as an entry-level data scientist, you’ll likely want to continue your education in order to remain competitive. Advanced degrees that will help you find a data scientist job include statistics, computer science, physics, mathematics, and social sciences. 

How Long Does It Take to Become a Data Scientist?

Data scientists are expected to have a bachelor’s degree, at minimum, which usually takes four years to complete. Since many earn higher degrees, it can take six to eight years in total to earn the educational requirements for a data scientist. 

There are many options available for those interested in studying data science. While traditional college and university classes are still widely available and used, there are also online and self-paced courses you can take advantage of as well. Becoming a data scientist demands curiosity, excellence in prioritizing tasks, and considerable communication skills so you can translate complex topics into understandable information. In addition, having aptitude in logic, advanced mathematics, and programming is helpful too. 

A bootcamp is a great way to sharpen your skills and gain the experience you need to enter the field of data science. A data analysis bootcamp can teach you the technical skills you’ll need to move forward in a practical, real-world environment. For example, The Data Analysis and Visualization Boot Camp at Texas McCombs is a 24-week course that will teach you the in-demand data analytics and visualization programs and have an impressive portfolio that shows you can manage real-life scenarios. Consider joining today to jump-start your career as a data scientist.

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Data Science vs. Data Analytics Skills

Data scientists and data analysts both engage in the data insight progression of pre-processing, analysis, visualization, and prediction. To be effective in this progression, they must have an understanding of higher mathematics, relevant coding languages, a variety of data management and data analysis software packages, database operations, and data visualization tools. In addition to this knowledge, it is critical that they understand the business applications of their data insights (e.g., what business questions do their internal clients have to answer) and what data elements will effectively satisfy those needs.

Some of the tools that both of these roles may use include:

  • C++
  • JavaScript
  • HTML/CSS
  • Python & Python Libraries
  • R
  • Structured Query Language (SQL) 
  • Apache (Hive, Pig, Spark)
  • Domino
  • Hadoop
  • Knime
  • Mozenda
  • Rapid Miner
  • Snowflake
  • Talend
  • MATLAB
  • Simulink
  • Qlik
  • SaS
  • Tableau
  • Microsoft Excel
  • Artificial intelligence
  • Machine learning (supervised and unsupervised)

Data Analyst Skills

The skills that employers are looking for in a data analyst include:

  • Data validation
  • Data cleaning
  • Data extracts
  • Data management
  • BI tool usage
  • Dashboarding
  • Charting
  • Complex metric and attribute development
  • Econometrics
  • Report development
  • Insight summarization/communication
  • ML model usage
  • Data visualization

Besides technical skills, data analysts should be able to present data in a way that is both understandable and compelling. Your job isn’t just on a screen; you are likely working with people who make decisions based on the information you study and interpret. You should be outcome-driven and able to keep focused on the goals of the client. There are several soft data analysts skills, like versatility and adaptability, that every employer is looking for in addition to your ability to competently use the systems and tools the job requires. 

Data Scientist Skills

Data scientists use many of the same skills, computer languages, and software as data analysts. However, their work also entails advising stakeholders on appropriate data strategies for the organization, establishing data management and analytic best practices for data users, creating the artificial intelligence constructs that others will use to gain data insights, automating standardized or repetitive analytical tasks through machine learning models, and using AI and ML themselves to provide advanced data and predictive insights to the organization. Key skills, in addition to data analyst skills, include:

  • Raw data categorization and management
  • Building ETL pipelines
  • Linear algebra and linear calculus
  • Discrete math
  • Graph theory
  • Information theory
  • Deep statistics and probability knowledge
  • Predictive modeling
  • Building statistical data models
  • Automate data analysis tasks via ML
  • Data mining and pattern analysis using AI
  • Data visualization fueled by AI/ML models

Data Analyst vs. Data Scientist Job Outlook

Both the data analytics and the data scientist fields have experienced explosive growth in recent years and show no sign of slowing. Businesses and their customers are creating huge amounts of data daily. Corporations rely heavily on this data to make decisions that will positively impact their business growth and profits. They are looking for highly-trained individuals who can work with the huge quantity of raw data collected and develop relevant, actionable insights. In a constantly changing marketplace, the need for professionals who are flexible, skilled, and committed to lifelong learning continues to grow rapidly. 

Data Analyst Job Outlook

According to CareerOneStop, job openings for data analysts are anticipated to increase by 25 percent through 2030 — and even higher in some sectors of the economy. Government, retail, professional services, software development, finance, healthcare, and oil & gas companies are all expected to need an influx of data analysts that enable them to make better business decisions, pinpoint problems, and create better-targeted marketing campaigns. California, Florida, Virginia, New York, and Texas have the highest levels of data analyst employment, according to the BLS.

Data Scientist Job Outlook

Data science is also a quickly growing occupation, with a projected growth rate of 31 percent, according to the BLS. Computer design corporations, scientific and technical consulting firms, insurance companies, government agencies, and online retailers are always in need of data scientists. Some of the areas with the highest concentration of data scientist jobs are California, Virginia, Maryland, Washington, and Texas according to the BLS.

Data Analyst vs. Data Scientist Salary 

As with other in-demand jobs, data specialists earn competitive salaries. Corporations are attracting new and knowledgeable talent with high wages, increased benefits, and great work-life balance. These jobs all require at least a bachelor’s degree, which also commands a higher pay rate. 

Data Analyst Salary

According to the BLS, data analyst salaries range broadly based on factors such as education, demonstrated skill set, and employment experience. While a beginning data analyst might earn between $48,050 to $63,070, a more experienced analyst could earn a median annual salary of $86,200. Other factors, such as industry and geographic region can impact salary as well. 

Data Scientist Salary

The BLS records a higher salary for data scientists due to their advanced education. Entry-level data science salaries range between $52,950 to $71,790, with a median annual wage of $103,930 for more experienced data science professionals. Of course, these amounts may vary depending on position location, a candidate’s level of experience, and the role’s industry.

A graphic that highlights the four states where data scientist employment rates are the highest.

Should You Choose a Career in Data Analytics or Data Science?

Either career is a great choice for someone interested in numbers, statistics, and business decision-making. As a data analyst or data scientist, you will make sense of large data sets, communicate trends and patterns and be involved in decision-making at the highest level in a corporation or government agency. 

Consider your career goals, interests, and how much time you plan to spend on higher education and advanced training when deciding between a data analytics and data science career. For either role, start your data analyst or data scientist journey with The Data Analysis and Visualization Boot Camp at Texas McCombs to learn in-demand skills applied in practical, real-world projects; enhancing your portfolio and marketability . 

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