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Data Science vs Data Analytics: What’s the Difference?

Defining the scope and skills of these two different professions. Plus, learn about how you can set yourself up for success within both of these in-demand roles!
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While these two terms are often used interchangeably, they describe two very different fields with distinct scopes, focuses, and roles. Both use statistics and programming to analyze statistics in order to assist businesses in decision-making, but do so in different ways. According to Wikipedia, “data science is an interdisciplinary field focused on extracting knowledge from data sets… to solve problems in a wide range of application domains.” Data science looks at the big picture of large amounts of information that is both structured and unstructured. It then uses the information to pose questions that will promote change and innovation. Predictive analytics is a large part of this discipline and more sophisticated tools and processes are utilized.

In contrast, data analytics uses mostly structured data to answer questions that are already posed. This discipline includes collecting, organizing, storing, and analyzing figures. According to cio.com, this field is responsible for describing current or historical trends and for presenting any findings.

 Data ScienceData Analytics
SkillsetData modeling, predictive analytics and engineering/programmingBI tools, intermedia statistics, programming, regular expressions (SQL)
ScopeMacroMicro
ExplorationsMachine Learning, A.I., unstructured big dataData visualization techniques, design principles, structured big data
GoalsDiscover new questions to drive innovationUse existing information to uncover actionable data

To generalize, data analytics looks at what and why something happened. Data science looks at what happens next.

What is Data Science?

This growing field is used by organizations to look at large sets of information to provide insights for business strategies and operations. This discipline takes information about customers to create direct marketing campaigns and help businesses run more efficiently, whether it be with their finances or inventory production. 

Top Skills and Tools Needed

This field requires a toolbox that includes both hard technical skills as well as soft skills in order to be successful, including: 

  • Programming Language Proficiency
  • Big Data Knowledge
  • Project Management Knowledge
  • Business Model Knowledge
  • Presentation Skills
  • Communication Skills
  • Leadership Skills
  • Mathematics Ability
  • Computer Science Ability
  • Software Development
  • Modeling and Simulation
  • Advanced Statistics Knowledge

Roles of This Field

Professionals in this discipline are responsible for creating models, algorithms, and predictive models. They are proficient in programming and are able to design their own programs for modeling. The information provided by these models is used to help organizations make important decisions and solve complex problems to help make their business more successful, competitive, and efficient and is used to move companies forward.

 

What is Data Analytics?

This discipline uses current and historical figures to identify trends for organizations. Businesses can use these findings to pinpoint what strategies worked and which were not successful.

Top Skills and Tools Needed

This analytics-focused career requires both technical and soft skills, like the ones listed below:

Roles of This Field

Professionals in this discipline look at large amounts of information and figures to discover and present trends to organizations as well as answer questions about the inner happenings of businesses. They maintain databases, interpret figures, write reports, and develop presentations. For example, these analysts may look to answer why quarterly sales dropped or increased, which marketing campaigns were successful, or which product had better sales.

 

Which Career Path is Right For You?

Top industries that use data science include machine learning, software development, corporate analytics, AI, and search engine engineering. Banking, healthcare, property management, and agriculture are some of the industries that capitalize on analytics. So which path is right for you? 

Data analysts dig deep into databases and love math, statistics, programming, and everything numbers. Data scientists must be able to do work with information like analysts, but also must be able to create programs from scratch and have knowledge of how the business world works so that they can make predictions and suggestions based on figures and statistics. Professionals of both disciplines should understand the industry that they are working in. Remember, data science looks at the big picture to make predictions for the future, while analytics digs into established numbers to report and present trends and results. While both work with statistics and figures, the way at which the fields approach and work with this information is unique.

In general, it takes less time to become a data analyst and many entry-level positions can be found after completing a bootcamp or associate’s degree. Data scientists can learn skills needed through bootcamps but, in most cases, need a bachelor’s degree or even a graduate’s degree. 

When considering which path to pursue, it is important to contemplate which subjects you are interested in, the industry you may want to work with, and the amount of education required and time needed to enter the field.

DetailsData ScienceData Analytics
InterestsMath, statistics, programming, predictive tools, business principlesMath, statistics, programming, databases, visualization
IndustriesMachine learning, software development, corporate analytics, AI, and search engine engineeringBanking, healthcare, property management, and agriculture
EducationBootcamps, associate’s degree, bachelor’s degreeBachelor’s degree or graduate degree most common
Daily ActivitiesCreating programs for predictive analysis, design models, present models, work with business to make next stepsMaintaining databases, analyzing figures, answering specific questions, writing reports, developing presentations

Career Opportunities

The Bureau of Labor Statistics predicts that from 2019 to 2029, these scientists will be one of the 20 fastest growing professions with a job outlook growth of 31%. Data analysts, like market research analysts, are expected to grow 18%. This is a much larger rate than the 4% average for all professions. 

Companies continue to analyze collected figures to make more informed decisions, protect websites from fraud, remain competitive in their industry, cater to consumers, and remain profitable. With technology and advances in computer programming continuing to collect more information digitally, the future for both of these career paths is promising.

Salary

According to indeed.com, the average salary for analysts in the United States is $70,063. In comparison, the average salary for scientists is $119,287. Many job postings offered cash bonuses, which would increase the average salary. It is important to note that compensation increases based on education and experience for both of these careers. 

Next Steps

Careers in both field paths are in demand in many different industries. Read more about data scientists and data analysts to help you find the best profession for you and how to get there.

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