Partial advice, in no particular order, to succeed in your DS interview:
Study through a book like Hastie et al.'s Elements of Statistical Learning or Barbers's Bayesian Reasoning and Machine Learning; forget videos for a long-term plan unless you decide to follow a long series of lectures. Do not get sucked into Deep Learning to begin with; it is cool and hip but realistically only a small proportion of ML/Stats jobs actually require Deep Learning; core competency on how and why to use regularisation and dimensionality reduction, will get one much, much further. You want to come across as being coherent and learned.
Answer questions in CV.SE, i.e. here. You want the experience of strangers judging you on your merits as a ML/Stats practitioner? Look no further, just click here. This is free practice; but it requires time.
Go to Glassdoor and write down all the Google, Microsoft, LinkedIn, Facebook, Amazon, etc. questions on their Data Science interviews mentioned. Also check general analyst questions from companies that might interest you. This should comfortably give you about 250-300 questions to go with. Some questions will be trivial, some will be hard and some will be out of scope. Learn the trivial ones (almost) by heart and learn how to answer the hard ones. Take a moment and think about out of scope ones so you avoid being totally flat-footed if they come up.
Do not give up and keep your cool. Personal anecdote: I have seen candidates with PhD in Statistics crumble under simple questions that they got wrong; myself included. Interviewing is a game, and a single game does not define you. Do not treat any interview as a write-off even it goes bad and do not panic if you get stuff wrong; all interviews are a learning experience and you will become better.
