There was a time when data scientists were supposed to be data engineers as well. The role has been divided into two as the domain of data has developed and changed, with data collection and handling becoming more complicated and unmanageable, and organizations wanting more solutions and observations from the data obtained. Regarding Data Science Bootcamp Training, one can follow at data science bootcamps.
Data engineers design and manage the systems and structures that collect, retrieve, and organize data, whereas data scientists analyze that data to predict patterns, gain business insights, and address questions that relate to the organization. To enquire about information related to courses offered, it can be followed at data science course fees.
Data science jobs have been in great demand in recent years, with the Bureau of Labor Statistics expecting a 22% increase in employment growth from the Year 2020 to 2030, much faster than the typical growth of other occupations.
This need shows no signs of decreasing as organizations remain focused on producing, collecting, and analyzing big data to help them run their operations. If one wants to inculcate Data science tools in his/her skill set, get enrolled for a data science Bootcamp Training.
Is There a Difference Between a Data Engineer and a Data Scientist?
Even though data engineers and data scientists have some talents in common, and data scientists were once expected to fulfil some of the functions of data engineers, the two jobs are distinct and distinct. Data engineers gather useful information. They transform and move the data into “pipelines” for the Data Science Technical Team. Depending on the task, they could employ programming languages like Java, Scala, C++, or Python. Data Scientists, on the other hand, examine, test, aggregate, optimize, and display data for the organization.
Data engineers create and maintain the technologies that data scientists use to access and analyze information. The job entails developing data models, constructing data pipelines, and monitoring ETL (extract, transform, load). Data scientists use data that has been cleansed to construct and train predictive models. Managers and executives are then informed of their findings.
Know more about role of unstructured data in data science.
Data Engineer vs Data Scientist
The following parameters can be used to explain data engineer vs data scientist or data engineering vs data science.
- Various Responsibilities
- Languages, Tools/Technologies
- Requirements and Education
- Job Prospects
1. Various Responsibilities
A data engineer creates, builds, tests, and maintains infrastructures including databases and large-scale processing systems. Data engineers deal with raw data containing human, machine, or instrument faults. The data may not have been vetted and may contain questionable records, it will be unformatted and may include system-specific codes.
Data engineers will be responsible for recommending and, on occasion, implementing methods to increase data reliability, efficiency, and quality. To accomplish so, they’ll need to use a range of languages and tools to connect systems or look for ways to obtain fresh data from other platforms so that system-specific codes, for instance, may be turned into information for data scientists to process. Their focus is on merging a range of big data technologies to create free-flowing data pipelines that enable real-time analytics. Complex queries are also written by data engineers to guarantee that data can be easily accessed.
Data scientists focus on gaining significant insights from the data which data engineers have prepared for them. They conduct online experiments, formulate hypotheses, and uncover trends and forecasts for the organization using their understanding of statistics, data analytics, data visualization, and machine learning algorithms.
They also work with corporate executives to understand their special needs and communicate complex findings in a way that a general business audience can grasp, both verbally and visually. The data scientist must be familiar with distributed computing because he/she will require access to data that has been analyzed by the data engineering team, but he/she will also need to be able to report to business stakeholders, which necessitates a focus on storytelling and visualization.
2. Languages, Tools/Technologies
Even though the technologies used by both parties are highly dependent on how the function is defined in the firm, data engineers are frequently seen using certain tools/technologies like SAP, Cassandra, Oracle, MySQL, Riak, PostgreSQL, MongoDB, neo4j, Sqoop, etc.
To create models, data scientists will employ languages like SPSS, R, Python, SAS, Stata, and Julia. Without a question, Python and R are the most widely used technologies in this field.
3. Requirements and Education
Data engineers are typically software engineers who are fluent in programming languages such as Java, Python, SQL, and Scala. They may also have a degree in math or statistics, which allows them to apply various analytical approaches to commercial challenges.
Most firms search for people with a bachelor’s computer science degree, applied mathematics, or information technology when hiring data engineers. Applicants may also be expected to keep a few certifications in data engineering.
Data scientists are frequently faced with vast amounts of data and no specific business problems to tackle. The data scientist will be required to study the data, formulate the appropriate questions, and explain their findings in this scenario.
Data scientists must therefore have a thorough understanding of various methodologies in big data platforms, data mining, machine learning algorithms, and statistics. They must also be updated with all the recent advancements as they must handle data sets that include a variety of formats in order to operate their algorithms successfully and efficiently.
Data scientists should be comfortable with programming languages like SQL, Python, R, and Java, as well as tools like Hive, Hadoop, Cassandra, and MongoDB.
4. Job Prospects
Aside from the increased interest in data management challenges, businesses are searching for more cost-effective, adaptable, and scalable data storage and management solutions. They want to shift their data to the cloud, and in order to do so, they’ll need to create “data lakes” to supplement existing data warehouses.
In the next years, data flows will need to be rerouted and replaced, and therefore, the focus on and quantity of job posts for data engineers has steadily increased over time.
The data scientist profession has been in high demand since the beginning of the buzz, but nowadays, firms prefer to assemble data science teams rather than hire unicorn data scientists with communication skills, originality, wit, curiosity, technical expertise, and so on. It’s difficult for recruiters to locate people who possess all the attributes that employers need, and the demand obviously outnumbers the supply.
The medium market for data engineers is a little lower, they make an average of $124,000, with a much lower minimum and maximum salary, the minimum is $34,000 per year, while the maximum is $341,000.
When it comes to salary, the median sector for data scientists is $135,000 per year on average. The minimum is $43,000, with a maximum of $364,000.
Can a Data Engineer Become a Data Scientist or Vice Versa?
The answer would be yes, with some additional training, data engineers can become data scientists and conversely. Because of the convergence in abilities from programming languages to data pipelines individuals of both professions have the underlying knowledge and terminology to make a reasonably seamless job shift. However, because data engineers are more concerned with the architecture and infrastructure to support data scientists’ work, and data scientists are more worried about developing and testing hypotheses using data, both professions would need to brush up on additional data scientist skills before making the transition.
Data Engineers & Data Scientists Working Together
Data engineers & data scientists can work together or collaborate with each other because of the overlapping in abilities from programming languages to data pipelines. Data engineers are concerned with the architecture and infrastructure to support data scientists’ work, and data scientists are more worried about developing and testing hypotheses using data. In this way, data scientists and data engineers work together.
Working with data in today’s larger firms, you’ll often find a combination of data scientists and data engineers. Engineers and scientists presumably share the same end objective–successful data exploitation for their institution’s pathways
Data Scientist vs Data Engineer: Which Is Best for You?
Data scientist vs Data engineer which is better? It depends upon each and everyone’s skills and interests.
Data engineers are primarily concerned with the infrastructure and architecture that is used to store and organize data. They are strong developers that enjoy learning and using the latest systems, discovering new methods to make software and operations more efficient, and thriving on saving time and resources for a business. If you’re always seeking ways to better the things you make, find meaning in providing helpful tools that help others perform their jobs, and enjoy experimenting with new tools and technologies, data engineering could be the appropriate career choice for you.
Data scientists are analytical thinkers who really are inquisitive, 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 patterns and try to predict what will happen in the near future. A career as a data scientist may be perfect for you if you appreciate performing advanced statistical analysis, designing machine learning algorithms, and solving issues creatively.
So, in this article, we focused on the differences between data engineers and data scientists. We also discussed various technological requirements, educational backgrounds in both professions and their respective job prospects. Lastly, we discussed their similarities and how they work together.
Frequently Asked Questions (FAQs)
1. Which is better, data science or data engineering?
It depends upon each and everyone’s skills and interests. If one is into statistical analysis then the data scientist role is suitable for him, if he is into providing helpful tools in order to help others in their respective jobs, then data engineering is suitable
2. Does Data Engineering pay more than data science?
A data scientist can make 20% to 30% more than a data engineer on average.
3. Can a data engineer become a data scientist?
Yes, with some further training, data engineers can become data scientists and vice versa. Because of the overlap in abilities, from programming languages to data pipelines, individuals of both professions have the foundational understanding and terminology to make a relatively smooth job shift.
4. Is Data Engineering harder than data science?
Data engineering is a difficult task. It’s a highly specialized and demanding field. Anyone can gain the abilities required to become one with patience and devotion.