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每日一练 | Data Scientist & Business Analyst & Leetcode 面试题 362

大数据应用  · 公众号  · 大数据  · 2018-05-24 09:04

正文

点击上方 蓝字 会变美




MAY

23

Data Application Lab 自2017年6月15日起,每天和你分享讨论一道数据科学(DS)和商业分析(BA) 领域常见的面试问题。

自2017年10月4日起,每天再为大家分享一道Leetcode 算法题。


希望积极寻求相关领域工作的你每天关注我们的问题并且与我们一起思考,我们将会在第二天给出答案。

Day

262

DS Interview Question

Describe what is the Artificial Neural Network?

BA Interview Question

R programming: How do you evaluate the logistic regression? Name the principle of the likelihood ratio test.

LeetCode Question

Same Tree


Description:

Given two binary trees, write a function to check if they are equal or not.

Two binary trees are considered equal if they are structurally identical and the nodes have the same value.


Input: 两颗树相同

Output: true


Day

261

答案揭晓

DS Interview Question & Answer

Tell me the process you will use to perform an A/B Testing.

Answer:


Collect Data: Look for pages with low conversion rates or high drop-off rates that can be improved.


Identify Goals: Your conversion goals are the metrics that you are using to determine whether or not the variation is more successful than the original version.


Generate Hypothesis: Make the hypothesis for the plan you think it will improve the webpage


Create Variations: Make the desired changes to an element of your website or mobile app experience.


Run Experiment: Kick off your experiment and wait for visitors to participate! At this point, visitors to your site or app will be randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted, and compared to determine how each performs.


Analyze Results


BA Interview Question & Answer

R Programming: How do you assess the results of a logistic regression analysis?

Answer:


You can use different methods to assess how good a logistic model is


Concordance - this tells you about the ability of the model to discriminate between the event happening and not happening.


Lift - it helps you assess how much better the model is compared to random selection.


Classification matrix - helps you look at the false positives and true negatives.







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