With businesses scrambling to harness the potential of data, there’s an overwhelming increase in demand for professionals with data skills across industries. In fact, there are more roles to fill than available data talent. The skills gap is so wide that most IT leaders declare their data initiatives suffer due to a lack of data talent.
No wonder these are exciting times for those interested in a data science career. However, the various data science roles can leave you a little puzzled before you decide on a career path. For instance, data science and data engineering are two terms that are often confused.
If you’ve landed here searching for the difference between a data engineer and a data scientist, you’ll find the answer in this guide. Read on as we tackle the data science vs. data engineering conundrum by exploring their unique roles in the data ecosystem, the skills required for each role, their career prospects, and the earning potential of each role. But that’s not all. We’ll also cover how you can sharpen your data skills with a data science course.
Understanding Data Science & Data Engineering
The confusion between data science and data engineering arises because they are similar-sounding terms. So, first, let’s look at the definition of each term.
What is Data Science?
Data Science is the art of extracting knowledge, insights, and actionable information from data. In a world where there are countless ways in which data is generated, it becomes quite a daunting task. To make it happen, data scientists combine various scientific methods, algorithms, statistical techniques, and cutting-edge technologies to analyze and interpret data.
- Role: At its core, a data scientist’s primary role is to uncover patterns, trends, and correlations within data sets. They utilize advanced statistical tools and programming languages like Python or R to transform raw data into actionable insights.
- Tasks: Data Science encompasses various activities, from data collection and cleaning to exploratory data analysis, machine learning modeling, and data visualization.
This means that it can help businesses make informed decisions, identify opportunities, and even predict future outcomes.
What is Data Engineering?
Data, despite being available for everyone, is challenging to work with. This is where data engineering comes in. Data engineering is dealing with the messy, unstructured nature of raw data.
- Role: Data Engineers process and clean the data, making it suitable for analysis by Data Scientists. They help store them and make them useful. The work of data engineers helps lay the foundation of the data ecosystem.
- Tasks: A data engineer is responsible for designing, constructing, and maintaining the infrastructure and pipelines. These structures are responsible for collecting, storing, and processing data efficiently and reliably.
In a world with an excess of data, Data Engineers ensure that the data is available in the right format, at the right time, and in the right place. Only then can Data Scientists and other stakeholders work with it.
What’s the Difference Between Data Engineers and Data Scientists?
Data Scientists and Data Engineers, despite sharing some overlapping skills, have distinct and complementary roles within the data ecosystem. Both are integral to the data-driven success of organizations, each contributing unique expertise to the process. Let us look at where they are linked.
Data Engineers are the architects and builders of the data landscape. They gather, transform, and move raw data into organized pipelines, employing programming languages to construct intricate data infrastructure. This meticulous preparation ensures that data is readily accessible and primed for analysis by Data Scientists.
Data Scientists are interpreters and innovators who take the refined data and breathe life into it. Armed with programming languages, they delve deep into data, extract insights, construct predictive models, and transform numbers into actionable intelligence. Their role extends beyond data analysis; they are storytellers who communicate findings to stakeholders, driving informed decision-making.
Together, Data Engineers and Data Scientists are a dynamic duo. As we have discussed so far, the major link between them is that they both deal with data. In a data-centric world, they play an integral role in creating use of the enormous data available.
Data Science vs. Data Engineering Comparison Table
The table below highlights the key differences between Data Engineers and Data Scientists.
|Architects of data infrastructure handle raw data.
|Analyze data, build predictive models, and create insights.
|Design, build, and maintain data pipelines ETL processes.
|Explore data, develop models, and communicate findings.
|Data warehousing, ETL, Hadoop, SQL, data pipeline design.
|Statistics, machine learning, data visualization, and programming.
|SQL, Python, Hadoop, Spark, NoSQL databases.
|Python, R, SQL, TensorFlow, Pandas, and data visualization tools.
|Education & Qualification
|Bachelor’s or higher in computer science or related field.
|Advanced degrees in data science, statistics, or related fields.
|There is a steady demand for data engineering roles in various industries. The predicted job growth rate is 21% from 2018-2028.
|High demand for data scientists across sectors. The employment rate is expected to grow from 36% from 2021 to 2031
|Competitive salary, which changes with location and experience. The average salary in the US is $115,510 per year, according to Glassdoor
|Salaries vary with experience and location. Average Salary is $152,308 per annum (Glassdoor)
Choosing Between Data Science vs. Data Engineering as a Career
For aspiring data professionals, the decision to pursue a career in either Data Science vs. Data Engineering is a major and slightly confusing. Let’s chalk out the career paths clearly so you can make an informed choice.
Building a Career in Data Science
Data Science might be your calling if you have a penchant for exploring data, uncovering hidden insights, and using them to shape business strategies. As mentioned, the career path revolves around analysis, modeling, and data visualization using Python, R, and TensorFlow tools. Data Scientists are at the forefront of making data-driven decisions and often collaborate with cross-functional teams.
Before you jump to conclusions, weigh the pros and cons for better decision-making.
Pros of Data Science
- High demand: Data Scientists are in high demand due to their ability to extract actionable insights.
- Attractive paycheck: They can command substantial salaries with experience.
- Diverse opportunities: Data Scientists find roles in various industries, from healthcare to finance.
Cons of Data Science
- Advanced skills: It requires a strong grasp of statistics and machine learning.
- Constant learning: Keeping up with rapidly evolving changes in the field can be challenging.
Building a Career in Data Engineering
Data Engineering might be your niche if you want to build robust data pipelines and ensure data flows seamlessly. This role focuses on database management, ETL processes, and infrastructure using tools like Hadoop and SQL. Data Engineers work closely with Data Scientists to provide them with quality data.
Pros of Data Engineering
- Critical foundation: Data Engineers lay the groundwork for effective data analysis.
- Steady demand: Businesses always need reliable data pipelines and infrastructure.
- Versatility: Skills in data engineering can lead to roles in various industries.
Cons of Data Engineering
- Less visible impact: Data Engineering’s work is often behind the scenes.
- Complexity: Managing large datasets and optimizing pipelines can be challenging.
To explore both paths, consider enrolling in a comprehensive Data Science bootcamp with a leading university. More on this in the coming sections.
Can a Data Engineer Become a Data Scientist or Vice Versa?
While it is confusing whether there is a clear distinction between the two, it can also cause aspirants to think, can I be one or both?
The simple answer is: Yes.
Transitioning from one role to another is possible with additional training and upskilling, especially because there is a lot of overlap between the skills and knowledge.
Professionals in both fields will be proficient in programming languages and familiar with data pipelines. With these skills in tow, a strong foundation exists for a relatively seamless job shift.
Data Engineer to Data Science
Data engineers possess a solid understanding of data architecture and infrastructure, making them well-suited to transition into Data Science roles. To bridge the gap, they would need to acquire additional skills like statistical analysis, machine learning, and hypothesis testing, which are essential for Data Scientists.
Data Science to Data Engineering
Conversely, Data Scientists can shift into Data Engineering by improving their knowledge of data infrastructure, ETL processes, and data pipeline management. This transition may require gaining expertise in tools and technologies commonly used by Data Engineers.
In essence, the transition is possible but challenging. Both roles share a common foundation. A willingness to learn and develop key skills is enough to help professionals shift from one field to another.
Take Your Next Steps for a Glowing Career in Data
Because today’s world thrives on data, data professionals are invaluable business assets. If you want to carve a career in data, data science and data engineering are fantastic options. As discussed earlier, a little introspection on your interests can help you choose where to take your career. Both offer stellar opportunities.
For those looking to kickstart their career in data, a reputed Data Science course will arm you with the necessary skills and practical training, help build a solid foundation in data science, and prepare you for interviews to land your dream data job. Or, you could opt for a shorter Data Science bootcamp to brush up your data skills and build more advanced skills, gain hands-on experience, and earn certification to boost your CV.
1. Can a data engineer be a data scientist?
Yes, a data engineer can become a data scientist with additional training and skills in statistics and machine learning.
2. Which is better — data science vs. data engineering?
The choice between data engineering and data science depends on individual interests and career goals. Neither is inherently better; both are valuable and offer diverse opportunities.
3. Is data engineering harder than data science?
Data engineering and data science complexity vary based on individual aptitude and preferences. Neither is universally harder; it depends on personal strengths.
4. Which pays more — data engineer or data scientist?
Data scientists often command higher salaries than data engineers, especially at senior levels, because they extract actionable insights and drive strategic decisions.