Careers FAQ

#Careers - FAQ

Questions are asked by you (students) and answered by students with professional work experience

Want to add your experience? : You can make a pull request here

##Q: What's to know about finding a job?

Gilad [@giladgressel] : Finding a job is a multi-faceted process. There are a number of steps involved

1. Write you resume, linkedin, github profiles.

2. Network network network

3. Got an interview? Great, now get ready for it!

Interview preparation is a must!

Maxime [@m_leclerc] : Udacity's Technical Interview is great and gives unlimited credits to practice interviews on pramp.com. In addition, you get additional ML interview videos in the MLND. For me the book Programming Interviews Exposed: Secrets to Landing Your Next Job got me most of my jobs. It's a good complement to Udacity's material and walks you through from day 1 writing your resume all the way to negotiating your salary when you get an offer. I think that if you're going to do any coding in your job even if you apply for a ML position it's better to be prepared for coding interviews.

Kunal [@kyadwadkar] : Another popular book has been Cracking the Coding Interview. The book has good medium/hard difficultly level problems. I think Gayle (the author) has a related site CareerCup has crowdsourced information on real questions asked (though take those and the solutions with a grain of salt), and a site which conducts mock interviews (I personally have never used it, so cannot comment on its usefulness). Other resources I've found useful is InterviewCake which I've used mainly for design interview preparation and The Algorithm Design Handbook to brush up on your algorithms.

Gilad [@giladgressel]: Another good practice site leetcode

##Q: Do you think that by finishing the Machine Learning Nanodegree (MLND) the student will be fully prepared for the job?

Gilad [@giladgressel]: No, honestly I don't. But - I also don't think a student with a PHD would be fully prepared for the job either. I don't believe that school educates you for how to do a job, schools teach you the skills that you need to learn how to do your job. Any job that you start will have a learning curve, and you will have to learn at the job. Definitely prior experiences will help and guide this, but you will still have to learn at that job.

Do I think the MLND prepares us well to learn on the job? I think it gives a reasonable starting place, that is probably the equivalent of focusing on Machine Learning as an undergraduate or even at a masters level, depending on the school.

This all falls under one large caveat, what kind of student are you?

You need to be a student that is inquisitive and willing to work hard and research everything you want to know more about. The MLND will not tell you everything there is to know about machine learning! It will not expose you to everything that exists.

It will give you an overview of the landscape of machine learning and it will have you practice in depth on one particular project (your capstone).

Machine learning is an ocean in itself, you can't be ready for everything it has to offer. When you get to a new job, you will be expected to specialize in the task at hand, and that will require research and work. Don't expect to know everything before you start.

This is true for any job in any field.

##Q: I'm planning to switch careers and Machine Learning/Data Science is a career option I'd like to explore. Do you have any resources to help ? Kunal [@kunalyadwadkar]: I highly recommend the book Working Identity - Herminia Ibarra as a good place to go to when exploring alternative careers in general. Besides that, I found this video on how to be a data scientist in 6 months to be a good resource. Tips therein (on how to stay focused, to network etc) are useful, but take the title with a grain of salt - everyone is different. You may take longer (or shorter) time to get to a job which satisfies you.

##Q: Will I be able to get a job in ML, after completing this nanodegree? Gilad [@giladgressel]: This is a very hard question to definitively answer. It depends on a lot of things, mostly you. If you are willing to work very hard and hustle, and live or are able to re-locate to an area that has jobs you want, I'd say yes.

I'll update this more later when I have more, time, but there are a number of factors involved here, it's not an easy question with an easy answer.

##Q: Have Graduates from this program received jobs in the field of ML? Gilad [@giladgressel]: We don't know currently, please let us know if you do know :)

##Q: What kinds of jobs have ND+ graduates received? Gilad [@giladgressel]: Same as above for now.