What is Data Science? A Guide to Careers and Skills in the Field
More data is being generated today than at any point in human history. In fact, in 2024 alone, it was estimated that the global population produced more than149 zettabytes, roughly equivalent to 1. 16 trillion. . .
More data is being generated today than at any point in human history. In fact, in 2024 alone, it was estimated that the global population produced more than149 zettabytes, roughly equivalent to 1. 16 trillion iPhones at 128 gigabytes of storage each!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. Check out this guide for a deeper understanding of thedifference between data science and computer science. Data science is about using large volumes of data to solve problems, find patterns and trends, andhelp 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 asPython,SAS, andSQL. 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. Programming and TechnologyExamples:Python, R, SQL, SAS, databasesUsed to clean and organize data, automate tasks, query databases,create visualizations, and build analytical models. Mathematics and StatisticsExamples:Probability, statistical analysis, patternrecognitionUsed to interpret results, identify meaningful trends, evaluaterelationships, and assess the reliability of predictions. Business and Industry KnowledgeExamples:Business goals, operations, customer needsUsed to connect data findings with practical questions, organizationalpriorities, and informed decision-making. Communication and CollaborationExamples:Data visualization, presentations, teamworkUsed to explain complex findings, collaborate across disciplines, andhelp others understand and act on data. The key connection:Effective data science combines these skill areas rather than relying ontechnical knowledge alone. Data Science LanguagesThe field ofdata 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 anddata 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?Most 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 forabout 30percentof all global data volume, and in 2025, surpassed 4,200 exabytes. Data scientists working in healthcare help to maintain electronic health records, manage disease registries, organize data from scientific research, and much more. Cybersecurityis 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:InsuranceRetailTelecommunicationsBig TechAgricultureData Science Career PathsA 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 AnalystA data analyst uses structured data to solve problems within a business or organization. Those working in this role are often required to useSQL,SAS, andPythonon a regular basis while also being able to handledata visualizationtasks 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 betweendata analytics vs. data scienceis vital in this role. Business Intelligence AnalystAre you more interested in the business side of the equation? In that case, a job as abusiness intelligence analystis another possibility for those with a data science background. Business intelligence 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 useSQLas well as financial modeling anddata visualizationsoftware in their everyday work. StatisticianA 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 bio-statistics, rely on experienced statisticians to help them make decisions. Data ModelerA 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 ScientistA data scientist builds data models using programming languages and software. Pythonis the most common programming language used in this field, although other languages likeSQLmay 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 ArchitectMany 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 includePythonand Java programming languages and data management and modeling tools. Big Data DeveloperFor 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 industry. Data EngineerThe 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 LearningEngineerThese days,machine learningis becoming increasingly prominent across the globe. This is even true in the data science industry, wheremachine learningis being used to create predictive models andalgorithmsto help identify patterns and make predictions about data sets. Amachine learningengineer, then, is a data scientist who works to create thesemachine learningsystems. These professionals may also work withartificial intelligenceto create specialized programming and code for the most accurate and reliable predictions. How to Pursue a Data Science CareerInterested 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 RequirementsIn general, working in a data scientist role typically requires a minimum of a bachelor’s degree, in a program such as data science, computer information systems or science, or a similar field. Depending on the position, some employers will prefer to hire a candidate with an advanced degree, such as a Master of Science in Data Science or a related field. Experience RequirementsThe level and amount ofdata science trainingand 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 CertificationsIn addition to having adata science majoror 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 ofcertifications to considerinclude:Certified Analytics Professional(CAP)IBM Data Science Professional CertificateOpen Certified Data Scientist CertificationSAS-CertifiedArtificial IntelligenceandMachine LearningProfessionalIs 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 online and on-campus data science and analytics degree programs with ourBachelor of Science in Data Science,Bachelor of Science in Computer Information Systems, orMaster of Science in Business Intelligence and Analytics, andMaster of Science in Computer Science. Regardless of which 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 torequest more information! Thank you for reading! The purpose of this blog is to provide general information to the reader, and as such, this information may not directly relate to programs offered by Post University. Please note jobs and/or career outcomes highlighted in this blog do not reflect jobs or career outcomes expected from enrolling in or graduating from any Post program.