The Data Scientist vs. the Data Engineer?
The Data Scientist vs. the Data Engineer?
Data Engineer gains more hype vs. Data Science in 2020s.
Hey Guys,
I’m not a software engineer but the debate of Data science vs. Data Engineer rages on. How would you summarize this debate? I wanted to sort of answer some of the FAQs on this topic. I hope this summary is helpful to someone out there reading this.
But think about it, careers within the field of data science have in recent years seen soaring demand, with the Bureau of Labor Statistics forecasting a 22% increase in job growth from 2020-2030—much higher than the average growth of other occupations
Which is better data science or data engineer?
Simply put, the data scientist can interpret data only after receiving it in an appropriate format. The data engineer's job is to get the data to the data scientist. Thus, as of now, data engineers are more in demand than data scientists because tools cannot perform the tasks of a data engineer.
In the recent past, the general belief in the industry was that as more and more advanced automation tools are developed, the need for pure data scientists would erode. But that hasn’t played out (yet) and may not.
Data Engineers Earn More
What pays more data engineer or data scientist?
Data engineering does not garner the same amount of media attention when compared to data scientists, yet their average salary tends to be higher than the data scientist average: $137,000 (data engineer) vs. $121,000 (data scientist).
You do the math, over a career that’s a significant difference.
Data science is easier to learn than data engineering.
Why? Well there's simply more resources available for data science, and there are a number of tools and libraries that have been built to make data science easier.
It’s all a bit confusing as these titles are different at different organizations, for instance:
Can a data scientist become a data engineer?
At some organizations, data scientists are tasked with doing things that data engineers should. While data scientists aren't equipped with the skills to become data engineers, they can acquire the skills. On the other hand, it's far less common when data engineers begin doing data science.
Job Descriptions are Different
Today, the main difference between these two data professionals is that data engineers build and maintain the systems and structures that store, extract, and organize data, while data scientists analyze that data to predict trends, glean business insights, and answer questions that are relevant to the organization.
Builders vs. Storytelling
That is, Data scientists build and train predictive models using data after it’s been cleaned, and then they communicate their analysis to managers and executives. Data engineers build and maintain the systems that allow data scientists to access and interpret data. The role generally involves creating data models, building data pipelines and overseeing ETL (extract, transform, load).
Engineering vs. Communication
That’s not to say that data scientists aren’t technical, they just aren’t only working on Engineering.
Why data engineer is better than data scientist?
Data Engineers collect relevant Data. They move and transform this Data into “pipelines” for the Data Science team. They could use programming languages such as Java, Scala, C++ or Python depending on their task. Data Scientists analyze, test, aggregate, optimize the data and present it for the company.
So it all depends in the workflow where you prefer to be.
The science part of Datascience might not appeal to everyone:
As part of their job, they conduct online experiments, develop hypotheses, and use their knowledge of statistics, data analytics, data visualization, and machine learning algorithms to identify trends and create forecasts for the business.
While data engineers are really knee deep in the nitty gritty.
Does data science require coding?
All jobs in Data Science require some degree of coding and experience with technical tools and technologies. To summarize: Data Engineer: Moderate amount of Python, more knowledge of SQL and optional but preferable is knowledge on a Cloud Platform.
The past five years we’ve been trying to decode the difference between Data Science and Data Engineering and it may still in 2022 depend on the company, industry and the needs of the moment.
Many data engineers and data scientists hold a bachelor’s degree in computer science or a related field such as mathematics, statistics, economics, or information technology.
But think about it, with the increasing integration of AI and machine learning in data analytics platforms, the data scientist of tomorrow may no longer need to have degrees in quantitative fields or to develop algorithms from scratch. What do you think?
Data Science Still Out earns MBAs
Who earns more MBA or data scientist?
The recent placement data from Symbiosis Pune reflects that a postgraduate program in Data Science when compared to a general MBA degree has better placement opportunities in terms of average salary and highest package offered.
Data Engineering is still a tough sport and is considered a stressful job:
Is data engineering stressful?
Many factors force data engineers to work long, irregular schedules that take a toll on their well-being. In fact, 78% of survey respondents wish their job came with a therapist to help manage work-related stress.
A Dataquest blog explains that the data engineer usually lays the groundwork for the data scientist to “analyze and visualize data.” Some of the initial tasks performed by the data engineer may include managing data sources, managing databases, and launching tools to make the data scientist’s job easy. So, strictly speaking, the data engineer handles all the back-end tasks of data analytics that lay hidden from the public eye.
Different Types of Data Engineers
Substack’s own Seattle Data Guy has a YouTube that can be entertaining. He reminds me of a gamer YouTube:
Hard-Coded Data Engineeers
What Are the Requirements To Become a Data Engineer?
Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.
Types of Data Engineers
You could even consider different types of Data Engineers basically:
Having an idea of which one you want to be might help you learn the right skills from day one:
1.The Big Data Engineer
2. The Data Warehousing Engineer
3. The Data Analyst
4. The Data Architect
5. The Cloud Engineer
6. The Data Scientist
7. The Machine Learning Engineer
8. The Software Engineer
9. The Data Programmer
There’s a certain amount of data engineering taking place in all of these different roles.
Why are such technical distinctions important, even to those not working directly with data? Because few business professionals — and even fewer business leaders — can’t afford to not know the difference.
A Data Engineer is a person who works with data to build better software, but there are different types of data engineers that are more specialized in certain areas. Also a Data Engineering role from one company or one industry to another, may be relatively different.
Some believe Data Engineers Will Be More Important Than Data Scientists suggesting that data chiefs in modern enterprises are realizing that advanced and automated tools alone cannot deliver results, which are expected to be both superfast and at scale.
In reality, many data engineers take advantage of roles such as data architect, solutions architect, and database developer to perfect their data engineering skills, develop a deeper knowledge of data processing and cloud computing, and gain experience with ETL and data layers. Some may also work in data analytics to bolster their knowledge of what data analysts and data scientists need before transitioning into data engineering. This also continually changes depending on the Modern Data Stack and software being used.
Can I move from data engineering to data science?
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.
Is data engineering a lot of math?
Mathematics is necessary for programming or data engineering but it's not mandatory to have an academic degree or course on mathematics. But you have to be an expert on numerical analysis, statistics, probability and logistic analysis.
Is Python enough for data science?
Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include: Data science and data analysis. Web application development.
Should I learn Python before data science?
To work in data science, you'll need to learn at least one of two languages — Python or R. If you already have some experience with R, then it's best to go through with it before starting with another language. On the other hand, if you're new, start with Python due to its versatility.
What Are the Requirements To Become a Data Engineer?
Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.
So even on a programming level, the two roles are fairly different in knowledge levels and programming languages to focus on.
Is engineering harder than data science?
Is data science harder than software engineering? No, data science is not harder than software engineering. Like with most disciplines, data science comes easier to some people than others. If you enjoy statistics and analytical thinking, you may find data science easier than software engineering.
Do data engineers use C++?
C++ is one of the essential programming languages that can be used by Data Engineers. C++ can be used for computing large data sets along with processing around 1GB of data in a second. Through this, Data Engineers can retrain the data and maintain consistency with records.
Should I learn SQL or Python first?
One thing to remember is that SQL is a big first step to some more complex languages (Python, R, JavaScript, etc.). Once you understand how a computer thinks, it is easy to learn a new programming language to analyze your data.
Finally there was a YouTube video I found somewhat amusing on the topic of Data scientists vs. Data Engineering here that may still be relevant today:
Thanks for reading!
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