#Harvard sas interview questions software
#Harvard sas interview questions plus

Explain to a business analyst the trade-off between the predictive power and the interpretability of a model вЂ“ and why this matters.

How would you design a chart or graph for a color-blind audience?.What is your favorite data visualization book or blog? And why?.Tell me a compelling story about data that you have analyzed.What were the factors that made it a success? Tell me about a project you worked on that succeeded in part because of the way results were communicated.How do you identify and overcome obstacles (during projects, with customers, with decision makers, etc.).Explain to the leaders of this company what model lift is and why they should care.With these questions, you are seeking to evaluate the candidateвЂ™s ability to communicate clearly and persuasively. Data scientists need to understand their data and explain its significance to the problem at hand. Even the smartest statistician in the room will fail if she cannot explain the relevance of her results.

#Harvard sas interview questions software

Explain the benefits of test-driven software development or explain the benefits of unit testing.Д¬ommunication-focused data scientist interview questions.

Describe a situation where you had to decide between two different types of analyses вЂ“ and why you chose the one you did.

What subjects would you include in a one-day data science crash course? And why?.

Give examples of data cleaning techniques you have used in the past.

How were the findings used by the business? (This can be from a student research project or thesis if the candidate is a recent graduate.)

Describe an analysis you have recently completed, including strategies and findings.

Describe a recent use of logistic regression.

вЂњIвЂ™ve heard from hiring managers who say their best hires have been people outside their industries who can look at problems from a fresh perspective.вЂќД®valuate practical skills by asking some of these questions: вЂњRemember that the candidateвЂ™s practical experience does not need to be within your industry,вЂќ says Udo Sglavo, Senior Director of Advanced Analytics R&D at SAS. They also should be able to articulate what aspects of their technical training have been important in their day-to-day data scientist tasks, and how they can apply their skills to your business. Your data science candidates should be able to describe projects they have worked on, and how they turned out. Technical skills are important, but they must be applied to solve problems. Practical experience data scientist interview questions

Explain the вЂњbias-variance trade-offвЂќ and why it is fundamental to machine learning.

Why is finite precision a problem in machine learning?

WhatвЂ™s more important: predictive power or interpretability of a model?.

How do you determine вЂњkвЂќ for k-means clustering? Or, how do you determineВ the number of clusters in a dataВ set?.

Explain the difference between a compiled computer language and an interpreted computer language.

Why is a comma a bad record separator/delimiter?.

What is the curse of dimensionality and how should one deal with it when building machine-learning models?.

Some of the questions are also designed to bring out the art and science of data science.

#Harvard sas interview questions plus

These questions help measure knowledge, plus the ability to explain complex topics. Statistics and machine learning are important technical skills for data scientists. Technical data scientist interview questions They are designed to evaluate the three sets of skills above, but youвЂ™ll want to add your own questions to make sure the candidate is a good fit with your organizationвЂ™s culture and requirements. Taking a hint from Thompson, weвЂ™ve broken our list into three categories, with an equal emphasis on each:Д¬onsider our questions to be a starting point for your data scientist interviews. Their domain experience and communications skills are just as important as their technical skills.вЂќ Logical reasoning agility, along with storyboarding skills are critical because these scientists must be able to solve problems iteratively in collaboration with business analysts and decision makers.

вЂњYouвЂ™re looking for more than technical skills. Remember that data scientists arenвЂ™t just ninja quantitative analysts, says Wayne Thompson, Chief Data Scientist at SAS. Data scientists have a unique blend of skills not only do they solve problems by squeezing information out of data, they also communicate results and persuade others to apply that information to their decisions. If youвЂ™re hiring a data scientist, we recommend focusing on more than technical skills, and your interview questions should reflect that. You can find lists and lists of questions to ask data scientist recruits in an interview, but most of the questions focus on the technical and quantitative aspects of the job without considering the softer skills. ThereвЂ™s no shortage of data scientist interview questions available online.