What is not data engineering?

Many areas are closely related to data processing and your clients will often be contributors to these areas. Knowing your customers is important, so you need to know these areas and how they differ from data engineering.

Some of the areas that are closely related to data engineering are: IT information technology

Data Science.

Business analytics.

Machine Learning.

In this section, you will learn more about these areas, starting with data science.

Data science

While data engineering is defined by how you move and organize massive amounts of data, data science is defined by what you do with that data.

Data scientists typically query, explore, and attempt to extract valuable information from datasets. They can write ad-hoc scripts for use on a specific dataset, while data engineers strive to create reusable programs using software development best practices.

Data scientists use statistical tools such as k-means clustering and regressionsas well as machine learning methods. They often work with R or Python and try to get data-driven insights and predictions to help them make decisions at all levels of the business.

Note : Do you want to study data science? Take a look at any of the following sections:

Data Science with Basic Python Skills...

Collection and storage of data...

Mathematics for data science...

Pandas for Data Science...

Data scientists are often scientifically or statistically trained and their working style reflects this. They work on a project that answers a specific research question, while a group of data engineers focuses on building scalable, reusable, and fast internal products.

A great example of how data scientists answer research questions can be found in biotech and health technology companies, where data scientists research data on drug interactions, side effects, disease outcomes, and more.

Business analytics

Business Intelligence is similar to data science with some important differences. Where data science is focused on predicting and predicting the future, business intelligence is focused on providing an overview of the current state of the business.

Both of these teams are served by data development teams and may even work with the same dataset. However, Business Intelligence is concerned with analyzing business performance and generating reports from the data. These reports then help management make decisions at the business level.

Like data scientists, business intelligence teams rely on data engineers to create tools that enable them to analyze and report on data relevant to their business.