Coding Dojo

Illustration of man near a sign post that points in one direction saying Data Science and another saying Computer Science

Data Science vs Computer Science: Which Career Is Better?

Careers in data science and in computer science have great earning potential and immense career growth opportunities. However, these jobs have different requirements. 

So, what is the difference between data science and computer science, and which career is better? 

In this article, we’ll look at the differences between data science and computer science careers, including descriptions of each field, salary information, potential jobs, and which one is right for you. 

You’ll also learn about the different skills you might need, and how you can take an intro to Python coding course or a data science bootcamp through Coding Dojo to pick up in-demand skills. 

If you are curious about your career options in these fields and want to know more about what you will need to study, then read on.

What is Data Science?

Data science is a field of study that reviews large amounts of data using tools and techniques to discover patterns in the data, interpret it, and use that information to make business decisions. 

Many job opportunities await data scientists because data science is a critical tool used in a very wide range of industries today. 

Over the years, data science has grown in popularity as people have begun to fully understand and implement it. Now, businesses use data science techniques to grow their profits. 

Data science is a useful area of study because data scientist opportunities have been projected to grow 36 percent from 2021 to 2031 – much faster than the average for all technology occupations.

What is Computer Science?

Computer science is the field of study that encompasses facts about computers and computational systems. 

This field includes a large range of topics, including artificial intelligence, cyber security, graphics, programming languages, software engineering, and the theory of computing.

Data Science vs Computer Science: Required Skills

There are some common skills and some overlap in the software used by these two different professions. One skill used in both careers is data visualization, which allows you to share your results with the non-technical stakeholders of your company in an easily understandable way.

However, these two careers also require different skill sets and the use of different software tools.

So, what do you need to learn?

Required Data Scientist Skills

  • Programming languages like Python, SQL, and Scala
  • Mathematics skills including calculus, linear algebra, and statistics
  • Data analysis and visualization tools including Tableau and Power BI
  • Web scraping tools like Scrapy or BeautifulSoup
  • An understanding of machine learning and artificial intelligence

Required Computer Scientist Skills

  • Programming skills, including programming languages Python, Java, and C
  • Data science and visualization
  • Mathematics skills including algebra and calculus
  • Technical writing abilities
  • Digital marketing skills including search engine optimization (SEO) and email marketing

Data Science vs Computer Science: Roles and Responsibilities

People who are talented at logical thinking and breezed through mathematics courses might find themselves wondering which of these two careers they should choose. After all, data science and computer science seem to include similar responsibilities and require related sets of skills.

So, what is a data scientist, and what is a computer scientist? It’s actually in the roles and responsibilities that we see how these two careers really differ. 

While they share some common software tools and skill sets, the roles of a data scientist and computer scientist vary when it comes to the actual day-to-day work they do.

While they sometimes employ data science in their work, a computer scientist does not need to be a data expert. On the other hand, a data scientist lives and breathes data – what it means, what patterns emerge, and how the story data tells can be used to grow a business.

So, what exactly are the differences between the roles of a data scientist and a computer scientist?

What Does a Data Scientist Do?

A data scientist uses complex machine learning algorithms to build predictive models. 

They capture data, process it, analyze it, and then report on the data to their company’s stakeholders. 

Data Scientist Roles and Responsibilities

  • Data acquisition, data entry, signal reception, data extraction
  • Data warehousing, data cleaning, data staging
  • Data processing: mining, modeling and summarization
  • Analyzing: text mining, predictive analysis, regression
  • Data reporting: visualization

What Does a Computer Scientist Do?

A computer scientist mainly deals with software and software systems – their theory, design, development, and application. 

Computer Scientist Roles and Responsibilities

  • Creating/revising computer algorithms
  • Evaluating new computer systems
  • Writing new programming languages or code
  • Designing models to solve computer problems
  • Creating user-friendly web design

Data Science vs Computer Science: Job Types

There is massive opportunity for those who seek a career in data science and in computer science. 

Computer scientists enjoy a range of options across different industries and can find exciting work as web developers, systems architects, and even AI engineers.

Computer scientists can easily find employment as a mobile app developer, an information security analyst or a UX designer – some exciting options for someone seeking a role that can be both technical and creative!

On the other hand, data scientists rank highly in research about the best technology jobs and many experts predict that data science is the career of the future.

Data scientist skills are in particularly high demand these days because the field is growing exponentially – data science is being adopted in a large number of industries that include digital marketing and media/entertainment.

As a data scientist, you can earn a substantial living as everything from an intelligence analyst to a software engineer.

So, what are some career options in these fields?

Data Analyst Jobs

  • Medical and Health Care Analyst
  • Market Research Analyst
  • Business Intelligence Analyst
  • Intelligence Analyst
  • Operations Research Analyst

Computer Scientist Jobs

  • IT technician
  • Database administrator
  • Information security analyst
  • Web developer
  • Hardware engineer

Data Science vs Computer Science: Average Salary

Becoming a computer scientist or a data scientist can come down to a decision based on how many opportunities there are in the market, job security, and, of course, salary.

Both of these career paths are financially rewarding and lucrative, with salary ranges from just over $70,000 to over $200,000 and beyond.  

While average salaries vary depending on your level of experience and what company you work for, you can be sure to find a position that has a strong starting salary.

The median annual wage for data scientists was $100,910 in 2021 – and it continues to grow.

Here’s what you can expect for entry-level data scientist and computer scientist salaries. 

Entry-Level Data Scientist Salary

The average salary for an entry level data scientist position is $79,479 US for a job located in the United States of America. Data scientist jobs are in demand internationally, and many can be done either remotely or are offered as hybrid positions.

Entry-Level Computer Scientist Salary

Entry-level computer scientists can earn an entry-level salary of $72,210 US for a job in the US. Computer scientist jobs can be found worldwide, and a number of them are done remotely or in a hybrid work environment.

Data Science vs Computer Science: Which Career Is Right for You?

Earning potential is often a priority for people who are researching career options. So, it is good to note that both data scientists and computer scientists have high earning potential. Data scientist starting salaries edge out computer scientist entry level salaries, but as careers progress, earning potential changes.

But salaries are not the only consideration for a career choice, because people also want job satisfaction.

What does job satisfaction come down to? It means different things to different people – satisfaction can mean flexibility, task variety, making a valuable impact on society and feeling challenged by the work you do.

Here are some characteristics of these career paths that might help you decide. 

Reasons to Learn Data Science

  • Big market for data scientists
  • Range of opportunities in different industries
  • High earning potential
  • Data science is a fast-growing field
  • Job security is almost guaranteed because of high demand

Reasons to Learn Computer Science

  • Exciting and challenging field
  • Great earning potential
  • Don’t need a university degree
  • Computer scientists are in constant demand
  • Flexible career path with many options

Learn Data Science at Coding Dojo

You may have noticed that a data scientist’s salary at the beginning of their career is higher than a computer scientist’s salary. What a great reason to learn data science

Data science is a new field with explosive growth in a variety of exciting and meaningful industries, including medicine, transportation and communications. 

There is also creativity in this field because they present data to non-technical stakeholders in a visual way that is easy to understand. Data visualizations, for example, are fun to create using various artistic software and are an important component to data science.

Coding Dojo makes it simple to learn data science. Part-time studies are available at Dojo’s Data Science bootcamp while you retain a job or have other responsibilities! 

Sign up for the bootcamp and learn in-demand data science skills today!