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How To Get A Data Scientist Job at Your Dream Company - My Journey Here!
- Applications: 475
- Phone interviews: 50
- Finished data science take-home challenges: 9
- Onsite interviews: 8
- Offers: 2
- Time spent: 6 months
- Know what you want, set your goal, work really hard to achieve that goal, and never settle for less.
- A growth mindset, it's really important (check out this growth mindset animation video if you haven't heard about it). Don't say "I'm not good at coding," "I'm not good at stats". It's not about talent. Don't use "talent" to describe others as an excuse for your laziness. What you need is to learn in the right way, and practice many times until you are good.
- Take note of all the interview questions you got asked, especially those questions you failed to answer. You can fail again, but don't fail at the same spot. You should always be learning and improving.
- Discuss questions you don't understand with other people if possible. I really appreciate the help from my fellow classmates and instructors at Galvanize, everyone was very supportive and willing to help each other.
- Go to local data science meetups, join data science learning groups, connect with people in the industry, send a personalized note when you are trying to connect with strangers on LinkedIn... Expand your network as much as possible, you don't know which one will open a door for you.
- Sometimes, the result is a combination of luck and preparation, and you are just not lucky this time. Don't always credit failure to yourself is not good.
- Don't interview with the companies that you want to work for at the beginning of your job search unless you think you are ready to go for them.
- Narrow down what types of jobs you want to do, and what types of jobs are not for you, this will save you a lot of time.
- Khan Academy: Very good to learn about basic concepts.
- Practical Statistics for Data Scientists: Good one, very practical, strongly recommend.
- Statistics course by Duke University on Coursera (Taught in R)
- brilliant.org: I purchased their membership when preparing my interviews, and I found it is one of the recommended materials in Facebook's onsite interview prep guide.
- Udacity A/B testing course by Google: I watched it twice and wrote a summary of this course.
- KDD papers and slides by Microsoft: A/B test is commonly asked in data science interviews but not many people outside the industry have ever done an A/B test before, so I searched and read ~15 papers when I was trying to learn about experiment design.
- Slides and videos on Exp Platform
- Company tech blogs, such as Airbnb data science blog
- Stanford University Machine Learning course by Andrew Ng on Coursera
- An Introduction to Statistical Learning: with Applications in R: One of the textbooks we used at Galvanize
- Machine Learning in Action: Another textbook we used at Galvanize
- Applied Data Science with Python Specialization by the University of Michigan on Coursera
- HackerRank: More entry-level friendly
- LeetCode: work on questions from easy or medium level
- Cracking the Coding Interview: 189 Programming Questions and Solutions (Written in Java)
- Datacamp
- Tip: I improved Python data manipulation tremendously by working on companies' take-home challenges. Practice is the best way to learn.
- Sorry, I don't use much R. Usually, in interviews you can use either R or Python.
- Mode Analytics SQL Tutorial: I'm fairly familiar with SQL but I still go through this before every SQL interview, especially the advanced section, just in case.
- Lynda Raynier's Youtube Channel: Really helpful for general interview questions. You can also search for other videos to learn about how to answer a specific interview question.
"Don't ever let someone tell you that you can't do something. You got a dream, you gotta protect it. People can't do something themselves, they wanna tell you that you can't do it. You want something, go get it. Period." - The Pursuit of Happyness