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Expand Your Data Science Toolkit with Data Engineering

By Carlos Russo • April 16, 2021

Christopher Paul High on Unsplash

Companies are pushing hard to expand their data science and analytics programs, and with that, the need for robust data engineering capabilities has arisen. Individuals who augment their data science expertise with a solid data engineering skill set can fill an important gap and maximize their data science operations at the same time. 

In today’s market, we continuously hear feedback from industry experts about the importance of not only building successful data science models, but also guaranteeing data quality and efficient storage (both for structured and unstructured data), deploying models to production, ability to work with big data tools, writing efficient code, utilizing cloud computing, and others.  Any data science and analytics expert would be able to significantly support their organization by mastering these areas.”

-- Roberto Reif, Metis Executive Director of Data Science 

What exactly is data engineering?

At its core, data engineers are data pipeline custodians who manage data repository ingestion, organize and clean data models, and feed those models into a production database for use by business analysts, data analysts, and data scientists.  

Data engineers typically perform an ETL (Extract, Transform, Load) operation model to transform raw source data into clean, structured, analyzable data. 

Extracting Big Data

A data science program’s success relies mainly on the data engineer’s ability to pull data from various sources and formats. Source material could be anything including customer data, financial data, biometric information, smart appliance performance history, etc. 

Transforming Big Data into Clean Data

Once the data engineer pulls the data (or extracts it) into a repository (most often a data lake when talking in terms of use for data scientists), the engineer must clean the data to rid it of duplicates, errors, missing data, outliers, and other issues. 

Loading Clean Data into a Central Repository

Data engineering is responsible for loading (or storing) the cleaned data in a repository that’s easily accessible for consumers. 

Why Data Engineering is Crucial for Data Scientists

A data scientist is only as successful as the quality and availability of data. Data engineering is vital because it delivers scalable data pipeline solutions addressing the questions data scientists attempt to answer. 

Depending on your organization’s size, you may have a dedicated data engineer, or you may be required to wear a couple of hats, such as data scientist and data engineer. The more you understand all that data engineering entails, the better you can bridge the gap between data organization and data analysis. 

How Data Scientists can Expand their Data Engineering Toolset 

Data scientists who can wear two hats, both curating and exploring vast repositories of data, are in the best position to know what they need, understand how to get it, and know what to do with it once it's there. Expanding your skillset with data engineering requires learning how to manage unstructured and structured data in various database implementations using advanced querying and scripting. 

Our Data Engineering for Data Scientists course equips you precisely with the right skillset. You’ll learn all you need to interact effectively with data engineering, including general programming skills, SQL and NoSQL database management, distributed systems, cloud computing, and more. 


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