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The Best Way to Prepare for a Technical Interview

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What are the best ways for someone to prepare for a technical interview? Technical interviews are where you get to meet potential teammates and collaborate with other developers to solve problems. Interviewers may approach the tech portion differently but the objective is always the same: assess whether you are the right person to join their team. That means you'll be presented with coding challenges to tackle, and be expected to be able to work through those problems, but, but also that, even when the interview is over, you still have an opportunity to show the interviewers why you're awesome. There are a number of different formats for technical interviewing.

How Two Googlers Use Engineering Techniques to Solve Marital Disputes


I approached finding a spouse the way I do even the most emotional decisions: with logic. A few years ago, before I started dating, I wrote a list of requirements and some optional good-to-have features, similar to how one might decide on a smart phone. A friend who was helping me complained that I eliminated 90 percent of the population with my requirements, but this was intentional: I wanted to narrow the pool so I didn't waste time on incompatible matches.

109 Commonly Asked Data Science Interview Questions


Preparing for an interview is not easy – naturally there is a large amount of uncertainty regarding the data science interview questions you will be asked. No matter how much work experience or technical skill you have, an interviewer can throw you off with a set of questions that you didn't expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge and communication skills from the part of the interviewee. Your statistics, programming, and data modeling skills will be put to the test through a variety of questions and question styles – intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when in pursuit of a career in data science.



For someone working or trying to work in data science, statistics is probably the biggest and most intimidating area of knowledge you need to develop. The goal of this post is to reduce what you need to know to a finite number of concrete ideas, techniques, and equations. Of course, that's an ambitious goal -- if you plan to be in data science for the long term I'd still expect to continue learning statistical concepts and techniques throughout your career. But what I'm aiming for is to provide you with a baseline to get you through your interviews and into practicing data science with as short and painless a process a possible. I'll end each section with key terms and resources for further reading.

How a coding credit score for developers could impact technical interviews


CodeSignal, formerly known as CodeFights, announced plans on Tuesday for a "Coding Score" rating system for developers. According to a press release, a Coding Score will be a credit score equivalent for the technical recruiting market, and will range between 300 and 850. The release noted that a Coding Score is meant to be a measurement of a developer's overall implementation and problem solving skills. To get an initial coding score, users must solve at least three challenges on CodeSignal. Data will be collected from 30-60 minutes of coding.