The aspect of data science that focuses on the practical application of data collection and analysis is data engineering. Data scientists answer questions using large amounts of data. There, however, has to be a mechanism for collection and validation of this information along with mechanisms for applying it to real-world operations. This is done by data engineers.


Data science vs data engineering

When it comes to skills and responsibilities there is a distinct overlap between data engineers and data scientists. There are many differences between the two:


  • Difference in focus

The main difference between data science vs data engineering lies in the focus.  Data Engineers are focused on building infrastructure and architecture for data generation is the focus of data engineers. Advanced mathematics and statistical analysis is the focus of data scientist.


  • Difference in responsibilities

Data engineers build and maintain data infrastructure. Data scientists, on the other hand, have constant interaction with the data infrastructure. They conduct high-level market and business operation research to discern trends. For this, they need to use sophisticated machines and methods to interact with data. Data engineers support data scientists by providing infrastructure and tools. They assemble scalable, high-performance infrastructure.


  • Differences in Tools, Languages, and software

Data engineers usually work with tools such as SAP, Oracle, Cassandra, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. Data scientists, on the other hand, use languages such as SPSS, R, Python, SAS, Stata and Julia to build models. The most popular tools are Python and R. There are many more packages out there that are useful for data science projects like Scikit-Learn, NumPy, Matplotlib, Statsmodels, etc. In short:-


Data engineers:

Skills: Hive, Pig, Data streaming, NoSQL, SQL programming, Hadoop, MapReduce,
Tools: DashDB, MySQL, MongoDB, Cassandra

Data scientists:

Skills: Python, R, Scala, ApacheSpark, Hadoop, deep learning, statistics
Tools: Data Science Experience, Jupyter, and R Studio


  • Differences in educational backgrounds

Their computer science backgrounds are the one big similarity. That is where they branch off in different directions. Data scientists often study econometrics, mathematics, statistics, and operations research. They also possess business acumen Data engineers, in general, have prior education in computer engineering.


  • Differences in salary & hiring

When it comes to salaries, data scientists earn more than data engineers. The job market is greener for data scientists. There is a big demand for them in companies like Deloitte, Walmart, Microsoft.


Data Science Courses Online

For working professionals, there are a number of online data science courses available. The program normally includes Microsoft Excel, T-SQL, Power BI, Python, R, Azure Machine Learning, HDINsight, Spark, Azure Machine Learning. Online data science courses are offered by Amity University (Noida), Institute of Management Technology Online, Edwiser, Brainstation. However, the best online courses are provided by Harvard, MIT, IBM, and Microsoft.

A data scientist turns raw data into insights – an alchemist. Data engineers prepare big data infrastructure to be explored by data scientists. The skill sets of both, a data engineer and a data scientist are essential for the data team to function optimally.

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