Ever since data became the new currency of the 21st century, Big Data and Data Science job roles have diversified and branched out at an unprecedented pace. Data Engineer and Data Scientist are two of the most promising job roles with an upward career trajectory.
Although the role of a Data Scientist was proclaimed to be the “sexiest job of the 21st century,” Data Engineer is not far behind. In fact, Glassdoor states that the number of job openings for the Data Engineer profile is five times higher than that of Data Scientists. Be that as it may, both Data Scientist and Data Engineer are part of the same team that seeks to transform raw data into actionable business insights. If you would like to get a professional data science training, check out our data science courses from top universities.
Today’s post is all about the raging debate of Data Science vs. Data Engineering, as seen from the lenses of Data Engineer and Data Scientist job profiles.
Data Science vs. Data Engineering
Data Science is a broad and multidisciplinary field of study that combines Mathematics, Statistics, Computer Science, Information Science, and Business domain knowledge. It focuses on extracting meaningful patterns and insights from large datasets by leveraging scientific tools, methods, procedures, and algorithms. The core components of Data Science include Big Data, Machine Learning, and Data Mining.
On the contrary, Data Engineering is a branch of Data Science that is primarily concerned with the practical applications of data acquisition and analysis. It focuses on designing and building data pipelines that can collect, prepare, and transform data (both structured and unstructured) into usable formats Data Scientists’ perusal.
Data Engineering facilitates the development of the data process stack to accumulate, store, clean, and process data in real-time or in batches and prepare the data for further analysis. In essence, Data Engineers create support systems for Data Scientists.
As David Bianco states, “Data Engineers are the plumbers building a data pipeline, while data scientists are the painters and storytellers, giving meaning to an otherwise static entity.”
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Data Engineer vs. Data Scientist: A detailed comparison
Before we dive into the differences between Data Engineers and Data Scientists, we must first address these two profiles’ similarities. The most vital point of similarity between Data Engineers and Data Scientists’ profiles is their educational background. Usually, both professionals come from Mathematics, or Physics, or Computer Science, or Information Science, or Computer Engineering background.
Here are the core points of difference between Data Engineers and Data Scientists:
The main difference between Data Engineers and Data Scientists is one of focus. While Data Engineers are involved in building the infrastructure and architecture for data generation, Data Scientists are mainly concerned with performing advanced mathematics and statistical analysis on the collected data.
As mentioned earlier, Data Engineers design, build, test, integrate, and optimize data collected from multiple sources. They use Big Data tools and technologies to construct free-flowing data pipelines that facilitate real-time analytics applications on complex data. Data Engineers also write complex queries to improve data accessibility.
However, Data Scientists are more focused on finding answers to crucial business questions such as optimizing business operations, reducing costs, improving customer experience, etc. Using the data format offered by Data Engineers, Data Scientists ask relevant questions, find hidden patterns, hypothesize, and then reach fitting conclusions.
The skillset of Data Engineers and Data Scientists is quite different. Plus, their skill levels vary. For instance, a Data Scientist’s analytical skills will be much more profound than a Data Engineer’s analytical knowledge.
Data Engineer skills:
- Distributed systems
- System architecture
- Database design and configuration
- Interface and sensor configuration
Data Scientists skills:
- Cloud computing
- Data wrangling
- Database management
- Data visualization
- Probability & statistics
- Multivariate calculus & linear algebra
- Machine learning & deep learning
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Data Engineers work with advanced programming languages like Python, Java, Scala, etc., distributed systems, data pipelines tools (IBM InfoSphere DataStage, Talend, Pentaho, Apache Kafka, etc.), and Big Data frameworks like Hive, Hadoop, Spark, etc.
While Data Scientists also use Python and Java, they use advanced analytics and BI tools like Tableau Public, Rapidminer, KNIME, QlikView, and Splunk. Apart from these tools, Data Scientists heavily rely on ML libraries like TensorFlow, Theano, PyTorch, Apache Spark, DLib, Caffe, and Keras, to name a few.
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Top Data Science Skills to Learn
|Top Data Science Skills to Learn|
|1||Data Analysis Course||Inferential Statistics Courses|
|2||Hypothesis Testing Programs||Logistic Regression Courses|
|3||Linear Regression Courses||Linear Algebra for Analysis|
Both Data Engineers and Data Scientists have a promising career trajectory with hefty annual compensation packages. The top recruiters for these profiles include big names like Amazon, IBM, TCS, Infosys, Accenture, Capgemini, General Electric, Ernst & Young, Microsoft, Facebook, and Apple Inc.
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Data Engineers & Data Scientists: Two complementary roles
To conclude, we must acknowledge that the roles of Data Engineer and Data Scientist complement each other. A company that leverages Big Data must have professionals with both skillsets to harness data’s true potential. Data Scientists rely on Data Engineers to build adequate pipelines for data generation and analysis. Similarly, the data that Data Engineers prepare will be of no practical use without data scientists’ analytical operations.
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Thus, companies must create a Data Science team wherein Data Engineers and Data Scientists can complement each other’s skills and functionalities.
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