Mechanical engineering and data science may appear vastly different on the surface. Mechanical engineers create physical machines, while data scientists deal with abstract concepts like algorithms and machine learning. Nonetheless, transitioning from mechanical engineering to data science is a feasible path, as explained in this blog.
It’s time to talk about the different data engineering projects you might work on as you enter the exciting world of data. You can add these projects to your portfolio and show the best ones to future employers. Remember, the world’s most successful engineers all started where you are now.
What should you put on your data science resume to stand out against the competition? We’ve talked to an elite resume writing service to get the details on everything you need in your resume to get hired fast.
You have a programming background. So do data scientists. So switching to a career in data science should be pretty easy, right? Yes, and no. While it’s not a seamless transition, it’s certainly doable, and we’ll tell you how.
The increasing demand for artificial Intelligence (AI) and data science experts, driven in part by the COVID-19 economic crisis, is showing no sign of abating. Many employers are failing to identify viable job candidates, much less interviewing or hiring them. What’s the biggest obstacle holding them back? In our experience, it is often a poor job posting.
Historical data can be an authoritative source of intel for a company looking to make smarter and faster decisions. When efficiencies are possible, it’s best to use the tools available to achieve them.
Scams and fake offers are an unfortunate reality of online job searches across industries.
Employees at risk of being replaced by automation need to transform themselves into data facilitators .
At The Data Incubator we’ve worked with hundreds of Fellows looking to enter industry and our alumni work at companies including LinkedIn, Palantir, Amazon, Capital
