Data science jobs have been in great demand in recent years. The Bureau of Labor Statistics projected a 22% increase in job growth from 2020 to 2030—much faster than the average growth rate for comparable occupations. Moreover, this need shows no signs of decreasing as organisations focus on generating, collecting, and analyzing big data to aid their operations.
The following guide explains the key differences between two of the most well-known data science professions — data scientist and data engineer — and covers all you need to know to make an informed decision about which career is right for you, from powers and tasks to average earnings, education requirements, and the various routes that can lead to a dream job working with data.
Data scientists were once supposed to play the role of data engineers. However, the role has been split in two as the data area has developed and evolved, with data collection and management becoming more complex and burdensome. In addition, organisations want more answers and insights from the data collected.
Data engineers design and manage the systems and structures that store, retrieve, and organise data. In contrast, data scientists analyse that data to predict patterns, gain business insights and answer relevant questions to the organisation.
Although data engineers and data scientists have some talents in common, and data scientists were once expected to fulfill some of the functions of data engineers, the two jobs are distinct and distinct.
It's helpful to conceive data engineers and data scientists as complementing one other. Data engineers create and improve the systems that let data scientists accomplish their jobs. Meanwhile, data scientists make sense of the massive amounts of data that data engineers handle.
Data scientists focus on gaining new information from data engineers have prepared for them. They conduct online tests, formulate hypotheses, and uncover trends and forecasts for the organization using their understanding of statistics, data analytics, visualization of data, and machine learning algorithms.
They also work with corporate executives to understand their special needs and communicate complex findings so that a general business audience can grasp them verbally and visually.
A data engineer is a specialist who creates an information system for analysis. They concentrate on raw data production preparedness and features like formats, robustness, scaling storage systems, and security. Data engineers are responsible for designing, developing, testing, integrating, managing, and optimising data from many sources. They also construct the infrastructure and architectures that allow data to be generated.
Their main goal is to combine big data technologies to create free-flowing data pipelines that enable real-time analysis. Data engineers also write complex queries to guarantee that data is easily accessed.
Many data engineers and data scientists require a bachelor's degree in computer science and engineering disciplines such as analytics, mathematics, economics, or (IT) information technology. However, while many businesses prefer people with postgraduate degrees, it is possible to work in data science or data engineering without one.
Data scientists are frequently faced with vast amounts of data and no specific business problems to tackle. However, the data scientist will be expected to study the data, formulate the appropriate questions, and explain their findings in this scenario. Therefore, data scientists must understand various methodologies in big data infrastructures, data mining, machine learning algorithms, and statistics. They must also be up-to-date with all the latest technologies because they must work with data sets that come in various forms to run their algorithms successfully and efficiently.
Data engineers are typically software engineers who are fluent in programming languages such as Java, SQL, Python, and Scala. They may also have a degree in mathematics or statistics, which allows them to apply various analytical approaches to commercial challenges.
When hiring data engineers, most firms search for people with a bachelor's degree in computer science, applied math, or information technology. In addition, some data engineering certifications, such as Google's Professional Data Engineer or IBM's Certified Data Engineer, may be required of candidates. It also helps if they have prior expertise in creating huge data warehouses capable of performing Transform, Extract, and Load (ETL) operations on large data sets.
There is no one-size-fits-all approach to becoming a data engineer or data scientist, but here are some of the most frequent paths people have taken to get to their ideal careers.
Many data scientists begin their careers in an entry-level data science position through a junior data scientist position or internship. This entry-level position allows new data scientists to refine their technical skills and work on pre-defined tasks before designing experiments and addressing more challenging business problems.
Data analysts frequently transition into data science professions via self-teaching data science skills or enrolling in an online course or Bootcamp.
Data engineering is rarely an entry-level position. This results in many data engineers beginning their careers in software engineering or business intelligence/systems analytics or professions and platforms that expose them to the systems and infrastructure necessary for data science.
Many data engineers use jobs like solutions architect, data architect, and database developer to hone their data engineering skills, learn more about data processing and cloud computing, and acquire experience with ETL and data layers. Before moving into data engineering, some people may work in data analytics to better understand what data analysts and data scientists require.
Both data scientists and data engineers collaborate. Each level of expertise is equally valuable and has a bright future in technology. However, according to recent reports, just one out of every ten data science projects makes it to production. Why? Because data projects are complicated and time-consuming, both data scientists and data engineers must contribute fully. Projects fall between the cracks for various reasons, but onerous data sets frequently prevent the data team from progressing into the production pipeline phase. Data scientists cannot effectively examine data until a strong and dependable infrastructure is built by data engineers. Projects are postponed or abandoned when teams run out of resources and money.
Focusing on the day-to-day work of each role rather than job titles allows you to develop the exact skills required for the career you select. When you know whatever aspect of the data system interests you the most, your employment options become clearer. Are you unsure? Consider the following questions:
Despite the similarities in abilities, data scientists and data engineers have distinct responsibilities, and some personality types may be more suited to certain tasks.
Data engineers are primarily concerned with data storage and organization infrastructure and architecture. They are strong developers that enjoy learning and using technological advances, discovering new methods to make applications and systems more efficient, and thriving on saving time and resources for a business. If you're a thinker who's always looking for ways to better the things you make, find fulfillment in making supportive tools that help others perform their jobs, and enjoy experimenting with new tools and technologies, data engineering could be the appropriate career for you.
Data scientists are analytical thinkers who are enthusiastic, don't mind asking questions, and are eager to test their assumptions. Data scientists utilize data to not only make sense of what has already happened but also to foresee trends and try to predict what will happen in the future. A career as a data scientist may be perfect for you if you appreciate performing advanced statistical analysis, designing machine learning techniques, and solving issues creatively.
Data employment has risen over the last decade, according to Google Trends, and is only expected to grow in the future. Since 2010, the graph below illustrates an increase in interest in data employment.
Data science and data engineering jobs are available in a variety of industries. Companies, whether in government or medicine, recognize the importance of data science in all fields.
Hopefully, now you have understood the difference between Data Engineer and Data Scientist, and your question of Data Engineer vs. Data Science | Which is the best career Option? has been answered.
It is important that you wisely choose your vice and make a knowledgeable decision based on facts and your field of interest.