2020 Data Analysis Interview Answering Tips: Questions and Answers

Introduction As we all know, with the development of society, data analysts have become a hot and popular profession. On the one hand, they have high salaries and on the other hand, they have broad development prospects in the future. Under normal circumstances, employers will give quizzes and hands-on questions to test the true strength of applicants. It can be said that the written interview test is a very important link. It can directly test your mastery of specific theories of data analysis and hands-on ability. For this reason, the editor will use this as an example to talk to you about the 2020 data analysis interview answering skills: question and answer questions. I hope it will be helpful to you.

Questions

1. Use a programming language to implement 1+2+3+4+5+…+100.

This question tests the basics of language. You can complete this question in a language you are familiar with, such as Python, Java, PHP, C++, etc. Here I use Python as an example:

sum = 0

for number in range(1,101):

sum = sum + number

print(sum)

2. How to understand overfitting?

Overfitting and underfitting are the same basic concepts of data mining. Overfitting means that the data is trained too well, which may cause errors in the actual test environment, so appropriate pruning is also very important for data mining algorithms.

Underfitting means that the machine has not learned enough and there are too few data samples to allow the machine to form self-awareness.

3. Why is Naive Bayes "naive"?

Naive Bayes is a simple but extremely powerful predictive modeling algorithm. It is called Naive Bayes because it assumes that each input variable is independent. This is a strong assumption that may not be true in practice, but this technique is still very effective for most complex problems.

4. What is the most important idea of ??SVM?

The calculation process of SVM is to help us find the hyperplane. It has a core concept called: classification interval. The goal of SVM

is to find the hyperplane corresponding to the largest value among all classification intervals. Mathematically, this is a convex optimization problem. Similarly, we divide SVM into hard margin SVM, soft margin SVM and nonlinear SVM according to whether the data is linearly separable.

5. What is the difference between K-Means and KNN algorithms?

First of all, these two algorithms solve two types of problems in data mining. K-Means is a clustering algorithm and KNN is a classification algorithm. Secondly, these two algorithms are two different learning methods. K-Means is unsupervised learning, that is, there is no need to give classification labels in advance, while KNN is supervised learning, which requires us to give classification labels for training data. Finally, the K value has different meanings. The K value in K-Means represents K categories. The K value in KNN represents the K closest neighbors.

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