If you ask any data science hiring manager for interview advice, they’ll tell you to work on a real problem and upload your code/presentation online.
If you ask any data science hiring manager for interview advice, they’ll tell you to work on a real problem and upload your code/presentation online. It’s hard to argue against this since who doesn’t want someone who can already do the job?
To a student (undergrad or PhD) without any experience, however, this advice is frustrating. The dark secret is that most hiring managers cannot tell you exactly what they’re looking for either. We just know that, with experienced candidates, the interview is smoother and our confidence high. Talking to candidates without experience, on the other hand, we are often left with a sense that something is missing. This post is mostly meant for students but new managers might get some clarity as well.
Before I breakdown the specifics, let me tell you what just happened before a hiring manager talks to you on the phone: we just came out of a different meeting, recalled what the position was for, and started skimming your resume while we dial your number. The resume helps us understand your likely strengths/weaknesses and hopefully provides some material for a good conversation. At this point, we are tired from work, bored from the repetitive questions, but really hoping you can be the one to end the hunt.
On the bright side, we are rooting for you. On the other hand, we have interviewed enough people that this conversation will likely feel like a replay. Thanks to your dedicated instructors from school, your class projects are likely too manicured to provide the small inspiration or educational snippet we enjoy, not too different from watching a good episode on the Discovery channel (back in the days).
If you think this seems prone to bias, you’re not wrong. Fortunately, we are usually required to write down what we thought about the interview and how we are judging your various talents around communication, technical skills, and fit for the role. This documentation process is quite good at filtering out biases since I always write reviews with the belief that the candidate will see them someday.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.