Introduction:

As the mathematician and data science entrepreneur Clive Humby said in 2006 ”Data is the oil” to say how important data is nowadays; data now feeds entire industries and has enormous value, so we will talk about two topics around data, namely data science (or data science) and data engineering (or data engineering).

First of all, you have to know that data science and data engineering are branches that stem from big data and are sciences that complement each other.

Data engineering

Data engineering is the task of making usable the raw data received from several sources such as (mobile devices, computers, etc…). The data engineer is responsible for the creation of databases, hardware requirements, software as well as the security aspects necessary to extract the data. It captures the bad seeds (malfunctions, bad formatting, errors, etc.) contained in the raw data and ensures their cleaning in order to meet the needs of data scientists.

Data science

Data science is the use of methods to analyze massive amounts of data and extract the knowledge they contain (Discover data science). The job of the data scientist is to analyze the data prepared by the data engineer in order to produce a result that will be the subject of decisions within a company. Its role is to:

• Frame the problem;
• Collect data;
• Process data;
• Analyze data;
• Implementation of predictive model;
• Data visualization;

Data science and Data engineering

Data Scientist and Data Engineer are part of the same team that seeks to transform raw data into actionable business insights.

Data engineers are curious, skilled problem-solvers who love both data and building things that are useful for others.  Either way, data engineers together with data and business analysts are a part of the team effort that transforms raw data in ways that provides their enterprises with a competitive edge.

Data Scientists are engaged in constant interaction with the data infrastructure that is built and maintained by the data engineers. Data engineers work to support data scientists and analysts, providing infrastructure and tools that can be used to deliver end-to-end solutions to business problems.