In this article I’m using version 5.9.34 because it’s the last version of the branch 5.9 available on npm (I installed i

Data Science and Data Engineering share more than just word data. Skills and tools are shared between both roles, whereas the differences lie in the concepts and goals of each respective role. For example, a Data Engineer will use Python as well as a Data Scientist (or another programming language), but a Data Engineer will use Python for a script or integration, whereas a Data Scientist will use Python to access the Pandas library as well as other Python packages to perform an ANOVA to test for statistical significance for example.

These similarities below are the skills and tools that both roles use. Mainly used by both are the programming languages and tools that help to deploy a Data Science model. Depending on the company, a Data Scientist could expect to work more on deployment, or the same could be said about a Data Engineer.

It is important to note that sometimes a Data Engineer can take on the role of a Machine Learning Operations Engineers (MLOps). Similarly, a Software Engineer can work as a Data Engineer or MLOps Engineers — it really depends on the company.

Most importantly in Data Science, is the ability to communicate. You will most likely have to work with a Product Manager or stakeholder to go over weaknesses in the business before you even look at the data in your company and start your Data Science model building process.

For skills, these even share similarities with Data Scientists. However, it is more what is focused on by each role. For example, a Data Scientist uses SQL for their role, but they do not usually create tables.

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— a lot of these ideas are going to require you to have some money saved up, and that is why it is so important to build up savings in your 20s, which will allow you to make some of these other moves to earn significant passive income.

Data Engineering works around the Data Science process at some companies, but it can also stand completely alone. I will be discussing more of the relationship between the two roles and processes. Before a Data Scientist executes its model building process, it needs data. A Data Engineer can help to gather, ingest, transform, and load that data into a usable format for a Data Scientist (and for plenty others in the business). A database is often set up by a Data Engineer or enhanced by one. The process that helps to push suggestions or predictions for a Data Science model is also built sometimes by a Data Engineer. For example, using a service to store training and testing data as well as model results that can be injected into a new database table can be executed by a Data Engineer.

These concepts below are ones to keep in mind as Data Science is not just code and programming, but a role that helps to solve business problems. Efficiency and saving money go hand-in-hand, and they are especially prevalent for Data Scientists. These are useful for any role, but for Data Science, the goal is to automate a process from the benefit of a Machine Learning algorithm. In turn, this model will save money and time. You will need to know how the specific algorithms work so that you can also optimize for the best algorithm. Statistics is important to know, especially when you are A/B testing and setting up experiments for a product.

For concepts, a Data Engineer can expect to not work on the model itself — for example, tuning parameters, discovering new features for the model, and considering the accuracy of the model, but they will still need to be familiar with the model so that they can help with integration. It is important to note here too that some Data Engineers do not touch a Data Science model at all. Once again, it depends on the company.

Feel free to comment down below to discuss what skills you use as a Data Scientist or Data Engineer. Perhaps you do not work with Data Science models at all as a Data Engineer and focus purely on data warehousing, or you actually focus on strictly creating features from SQL querying that ulimately will be injected into Machine Learning algorithm tested by a Data Scientist.

I hope I introduced some clarity to you for what really defines these two very similar, yet different roles. The reason for the variability in these positions can be caused by the occurrence that one is more new, Data Science, so a Data Engineer can either focus on more data-ETL work as they have been in the past, or they can also work with a Data Scientist to help build and deploy a model.

If there is ever any confusion on Data Science and Data Engineering roles, the best source of truth, is that from the Hiring Manager — who will untimely layout the foundation of your everyday work and expectations of if you are more SQL oriented, Python, or Machine Learning deployment-focused. Also, it will depend on where and what the needs of the company are.

Disclaimer: I discovered this vulnerability in February and it was fixed in May 2020 (version 5.10.2 and new branch 6.x) due to the change of the web driver from Zombie.js to puppeteer.

Feel free to comment down below to discuss what skills you use as a Data Scientist or Data Engineer. Perhaps you do not work with Data Science models at all as a Data Engineer and focus purely on data warehousing, or you actually focus on strictly creating features from SQL querying that ulimately will be injected into Machine Learning algorithm tested by a Data Scientist.

At work I had to vet different software detection solutions and one of them was Wappalyzer. Following the line of my previous research about scraping software being pwned by malicious websites [1] [2] and Wappalyzer being a tool analyzing third-party websites, the natural question was: would it be possible to be pwned by a malicious website if I run Wappalyzer against it?