Online Training Cracking the Data Science Interview by Udemy

Get yourself trained on Cracking the Data with this Online Training Cracking the Data Science Interview.

Online Training Cracking the Data Science Interview

I thought I bombed the interview. Beads of sweat drooled down the sides of my face. Blood rushed towards my head, as my face gave off a cool, red glow. The interviewer asked me to take the derivative of the cost function for Linear Regression, aka, Gradient Descent. At the time, I had no clue. As the time ticked, my hesitation permeated the entire room. Rejection flooded the nerves of my brain. It’s okay. I still had many more interviews to go. I’ll just take the loss and learn from it. Me: “Hmm, I’m not really sure, but I’ll talk out my thought process and see where it goes.” One week later, I received a call: Congratulations Jeff, we’d like to make you an offer. Boom. My first data science job offer. I realized that data science interviews weren’t only about answering questions correctly. Like any skill, the data science interview is a skill to be honed and practiced. You might be able to train your own neural network, win Kaggle competitions, build the next great image classifier or take another MOOC, but none of this matters if you can’t pass data science interviews. Data Science is the hot field. There are an infinite number of MOOCS, online courses, textbooks teaching the topic. Do you really need to take another MOOC on Deep Learning, when you should be putting yourself out there and interviewing? Do you really need to build another project, when you should be completing take-homes and going onsite? Yes, I know, rejection sucks. You will get rejected. It will hurt. I’ve gotten rejected 50+ times while recruiting for data science positions. But do you know the best way to avoid rejection? It’s being as prepared as you can possibly be. And the only way to do that, is answering real-life data science practice interview questions. And I promise you, if you complete my course, you will be prepared for the data science interview. In this course, I will show you:The one tactic I use to pass interviews, even if I get the question wrong. Real life questions Iwas asked in interviews by Facebook, Google, Quora, Slack, Evernote etc.The types of queries you should be writing for the interviews (Hint: Yes, they involve WINDOW functions)The trick to not bombing Python interviews The level of depth your Machine Learning knowledge should be at My framework for answer tricky, product/business case questions. This is where most data scientist falter.This course should give you the confidence to go into a data science interview and know what to expect. While I’m unable to cover every type of question asked, what I am giving you is a framework towards answering any type of technical question a company will throw at you. These are real-life questions I’d been asked at Facebook, Google and top Silicon Valley tech companies. I promise you that you will be satisfied or your money back. 30-DAY MONEY BACK GUARANTEEThis course is designed to prepare anyone looking to break into the field of data science for the technical interview. If you complete this course, take action and do not improve your data science interview skills, I will give you a full immediate refund. No questions asked. Who this course is for:This course is built for Data Scientists, Data Analysts, students or anyone who wants to break into data science. This is ideal for people looking for their first Data Science position at both large technology companies and startups.Anyone who has an upcoming interview for a data scientist position.

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Investing in yourself through Learning

As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.

And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.

Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.