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More data is being generated today than at any point in human history. In fact, in 2021 alone, it was estimated that the global population produced more than 2.5 quintillion bytes of data per day—and this figure could double by the end of 2023. 

It can be difficult, if not impossible, to conceptualize how much new data is making its way into the world with each passing second, but regardless, there is a need for knowledgeable professionals who can make sense of it all. 

What Is Data Science? 

The term “data science” refers to an interdisciplinary field that involves the extraction of valuable insights and knowledge from data using algorithms, processes, and systems. Data science draws upon expertise from several domains, including statistics, mathematics, and computer science, to transform raw data into actionable information and knowledge. 

Data science is about using large volumes of data to solve problems, find patterns and trends, and help make business decisions. 

When we talk about data science, we must consider different aspects of learning where computing skills, mathematics and statistics, and business skills come together. 

Why Is Data Science Important? 

Data science is important because with so much data being generated on a daily basis, organizations, government agencies, businesses, and other entities need a way to make sense of all that information. When used properly, data science can help businesses mitigate risk, improve security, personalize customer experiences, and deliver the right products and services to customers. 

Data scientists and those who work in similar positions can interpret and manage data to help businesses improve their practices, make better-informed decisions, and achieve their goals. Without data science, it would be more difficult and time-consuming to make sense of large amounts of information, given the comparatively slow processing time of the human brain. 

What Do Data Scientists Do? 

Data scientists use machine learning algorithms to build predictive models. Increasingly, we see data used as a product within well-known companies. These algorithms are used to recommend series on Netflix or products on Amazon; to tell you how long it will take for the Uber you requested to arrive, or to show the ads you designed to the customers most likely to buy. They are also the basis for complex systems like Tesla’s self-driving cars. As you can see, the list of applications where “machine learning” models or algorithms are used and data science is applied is long. 

What Skills Are Needed to Work in Data Science? 

There are several hard and soft skills that data scientists need to possess in order to do their jobs well. First and foremost, many data scientist jobs require some level of programming knowledge in such commonly used languages as Python, SAS, and SQL. Likewise, a thorough understanding of statistics and probability can serve a data scientist well on the job, as this knowledge is required to comprehend and analyze large amounts of data. 

Interpersonal skills also come in handy in data science. Contrary to what some people may believe, data scientists do not sit alone in front of screens all day. They regularly interact and collaborate with others, so effective communication skills are a must. Some data scientist roles require regular presentations of data findings, so experience and confidence in public speaking are also helpful. 

Data Science Languages 

The field of data science uses a number of programming languages, each of them fulfilling its own unique purpose. The exact programming languages a data scientist needs to know will depend on their position and duties. However, some of the more commonly used programming languages include: 

  • Python - Perhaps the most popular programming language among data scientists, Python offers a wide range of applications that include automating tasks and data visualization. 
  • R – This language has grown in popularity over the past few years, primarily due to its accessibility and versatility. It is extremely powerful when it comes to performing statistical operations and can handle vast amounts of data at once. 
  • SQL– This language is considered a standard for relational databases, and the fact that it does not require an existing knowledge of programming makes it easy to learn. 
  • SAS - This programming language has been around for many years, so it has an established use with many systems. Today, it is most often used in business intelligence and advanced analytics. 

Which Industries Use Data Science? 

Many industries can benefit from data science in some way. However, healthcare is perhaps one of the more prominent industries for data scientists, as the healthcare sector alone accounts for about 30 percent of all global data volume. Data scientists working in healthcare help to maintain electronic health records, manage disease registries, organize data from scientific research, and much more. 

Cybersecurity is another industry that relies on data science. Specifically, data scientists could be called upon to track cybercrime data, identify patterns, and make recommendations to protect organizations from attacks. 

Some other industries where data science is relied upon include: 

  • Insurance 
  • Retail 
  • Telecommunications 
  • Big Tech 
  • Agriculture 

Data Science Career Paths 

A career in data science could involve a number of roles that require different skills and levels of experience. Below is just a sampling of some common data science career paths that you may want to consider for yourself. 

Data Analyst 

A data analyst uses structured data to solve problems within a business or organization. Those working in this role are often required to use SQL, SAS, and Python on a regular basis while also being able to handle data visualization tasks and understand the basics of operating a successful business. These professionals also work alongside business analysts, so being able to effectively collaborate and communicate between disciplines is a must. Likewise, understanding the differences between data analytics vs. data science is vital in this role. 

Business Analyst 

Are you more interested in the business side of the equation? In that case, a job as a business analyst is another possibility for those with a data science background. Business analysts’ study and interpret large amounts of data to advise growing companies. This may include making recommendations to onboard new staff, increase efficiency, and cut costs. It is common for business analysts to use SQL as well as financial modeling and data visualization software in their everyday work. 


A statistician is a data scientist responsible for collecting and analyzing a company or organization’s data to solve specific problems and discover new insights. Industries such as agriculture, healthcare, and even biostatistics, rely on experienced statisticians to help them make decisions. 

Data Modeler 

A data modeler is a professional who designs computer databases and data models that can be used to turn even the most complex data into usable information that can be easily interpreted, organized, and managed. Data modelers are a valuable asset across many industries, and they often work alongside data architects and database administrators to optimize the results of their hard work. 

Data Scientist 

A data scientist builds data models using programming languages and software. Python is the most common programming language used in this field, although other languages like SQL may also be needed. These professionals usually work with other experts, including data engineers and data architects, to solve the most complex problems and make predictions. 

Data Architect 

Many businesses and organizations also need data architects to help them create the blueprints for their data management systems. This may include overseeing and managing the infrastructure needed to maintain data sources and databases, as well as training employees on how to access this information as needed. Data architects use a variety of tools in their work, but some of the most common include Python and Java programming languages and data management and modeling tools. 

Big Data Developer 

For those who enjoy working with enormous sets of data, a job as a big data developer can be both rewarding and challenging. This type of work involves developing software to process and interpret large data sets while working with data scientists and data engineers along the way. These professionals also develop applications and software to help end users understand and interpret large data sets, serving an important role in many industries. 

Data Engineer 

The main responsibility of a data engineer is to organize and manage large amounts of data, developing and optimizing data infrastructures to ensure that data is readily available and accessible to data scientists and analysts so they can carry out their work effectively. 

Machine LearningEngineer 

These days, machine learning is becoming increasingly prominent across the globe. This is even true in the data science industry, where machine learning is being used to create predictive models and algorithms to help identify patterns and make predictions about data sets. A machine learning engineer, then, is a data scientist who works to create these machine learning systems. These professionals may also work with artificial intelligence to create specialized programming and code for the most accurate and reliable predictions. 

How to Pursue a Data Science Career 

Interested in a career in data science? As you can see, there are various career paths you may pursue. Before you can jump in, however, there are some education and experience requirements you should know about. 

Education Requirements 

In general, working in a data scientist role will typically require a minimum of a Bachelor of Science in Data Science, Computer Information Systems, or a related field. While many employers have traditionally preferred candidates with such degrees, the landscape is evolving. Some employers now place greater value on practical experience, bootcamps, and certifications, which can complement or even substitute for a formal degree. However, for many positions, particularly at the senior level, an advanced degree such as a Master of Science in Data Science or a related field is still often preferred. 

Experience Requirements 

The level and amount of data science training and experience required will also vary from one position (and even one employer) to the next. Some entry-level data engineer or data analyst roles, for example, may require no formal work experience and instead rely on educational achievements. 

More advanced roles in data science, such as data architect or big data developer, may require a certain number of years of experience. Likewise, some employers may prefer to hire a candidate who has already worked in a particular field (such as insurance or healthcare) and thus understands the unique nuances of that industry. 

Other Data Science Certifications 

In addition to having a data science major or other relevant degree under your belt, there are some extra certifications you may want to consider pursuing. Additional certifications could help you stand out from other job applicants and help you become a better-rounded data scientist. Some examples of in-demand certifications to consider include: 

Is Data Science Right for You? 

Are you an analytical thinker who enjoys working with large amounts of data and solving problems? Then a career in data science may be right for you. Whether you aspire to work in data engineering, data analysis, or even machine learning/AI, obtaining the right educational credentials is an important first step. 


At Post University, we offer both an online and on-campus data science degree programs with our Bachelor of Science in Data Science. Regardless of which  location of the program is right for you, Post University offers the education, support, and resources to help you obtain your educational goals. Get in touch with our team today to request more information 

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Please note jobs and/or career outcomes highlighted in this blog do not reflect jobs or career outcomes expected from any Post program. To learn more about Post’s programs and their outcomes, please fill out a form to speak with an admissions advisor.