The surfacing of machine learning, artificial intelligence, natural language processing, and other emerging technologies has boosted the adoption of data science. Companies are trying to manage massive amounts of data produced daily using big data.
With the rise in big data, the roles of data science and data engineering are increasing significantly. Often, they are used interchangeably. These roles may sound the same but are very different. Let us walk through the roles one by one.
Data Scientist
Data Science is an advanced level of data analysis driven by computer science and machine learning. Their job starts with data preprocessing where they clean, understand, and try to fill data gaps. They play a crucial role in setting up businesses. They look out for a recent market problem and use data analysis and processing to provide the best solutions. They analyze, process, and model data to achieve business objectives. The models created by them are valuable in extrapolating, analyzing, and finding patterns in the existing data. They have to have skills in computer science, statistics, and mathematics.
Data Engineer
Data engineers transform data from multiple sources into a single format. They help in building up systems that collect, manage, and convert raw data into useful information for data scientists and business analysts. Data Engineers build a data pipeline to facilitate data flow from one system to another. They help in cloud data integration, solving complex data problems, and address data plumbing issues. Their primary roles are to clean the data, compile and integrate database systems, scale to multiple systems, write complex queries, and strategize disaster recovery systems.
Data Engineers: Lesser-Known Cousin of Data Scientist
Data engineers are the less famous cousin of data scientists who are equally important. They prepare data infrastructure for analysis. They work upon things like format, resilience, security, and scaling of data. They focus on collecting the data and validating the information that data scientist uses to solve the problems.
Their primary focus is to build data pipeline by using big data techniques with real-time analytics. They also focus on writing complex data extraction queries so that data is easily accessible.
A large amount of data is managed over distributed networks. So, they must have a solid knowledge of the Hadoop system along with common scripting languages such as PostgreSQL, MySQL, etc.
Nowadays, many data-intensive projects such as e-commerce sites, financial networks use Artificial Intelligence. These projects created the role of data engineer to be of utmost importance.
In gist, roles of data engineers are:
- Build, test optimal data pipelines
- Automate manual processes
- Optimize data delivery
- Re-design current infrastructure for improved scalability
Data Scientist: The Ubiquitous Role
The role of data scientist has been projected as mandatory for all innovative technology projects. They focus on understanding human functions of vision, speech, language, decision-making; designing machines and software to imitate these processes. They are responsible for finding the best model for tasks like replacing complex decision-making processes, automating customer interaction keeping it as natural as possible, etc. They are responsible for conducting detailed market and business research that helps in identifying trends and opportunities.
They should have a sound knowledge of emerging technologies and model-building techniques. Data visualization and design thinking are also crucial for them. Typically, having good knowledge of R or python framework with one or more deep learning frameworks (such as TensorFlow) and distributed data tools (such as spark) is required.
Major roles of a data scientist can be summed up as:
- Develop custom data models and algorithms
- Build tools and processes to improve performance and data accuracy
- Use predictive modeling to optimize targeting, revenue generation, customer experiences
- Develop a framework for testing and model quality checking
Conclusion
The demands of both data engineers and data scientists are very high in demand. Both positions are equally paid around $100,000 per year. Their ever-growing demand has opened up doors of a new field called ‘Computational data science’ where data engineering is equally emphasized with AI concepts.
The data scientists dig into the research and visualisation of the data while data engineers take care of data flowing correctly from the pipeline. Both are equally essential and have a huge demand with limited supply. Choosing either one of them should be considered a great choice. They both work together, complementing one another to help businesses attain their goals.
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