DS 面试问题 Data Scientist Interview Questions
1 What is the Central Limit Theorem and why is it important?
2 What is sampling? How many sampling methods do you know?
3 What is the difference between Type I vs Type II error?
4 What is linear regression? What do the terms P-value, coefficient, R-Squared value mean? What is the significance of each of these components?
5 What are the assumptions required for linear regression?
There are four major assumptions:
1. There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data,
2. The errors or residuals of the data are normally distributed and independent from each other,
3. There is minimal multicollinearity between explanatory variables, and
4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable.
6 What is a statistical interaction?
7 What is selection bias?
8 What is an example of a dataset with a non-Gaussian distribution?
9 What is the Binomial Probability Formula?
1 With which programming languages and environments are you most comfortable working?
2 What are some pros and cons about your favorite statistical software?
3 Tell me about an original algorithm you’ve created.
4 Describe a data science project in which you worked with a substantial programming component. What did you learn from that experience?
5 Do you contribute to any open source projects?
6 How would you clean a dataset in (insert language here)?
7 Tell me about the coding you did during your last project?
1 What are the two main components of the Hadoop Framework?
2 Explain how MapReduce works as simply as possible.
3 How would you sort a large list of numbers?
4 Here is a big dataset. What is your plan for dealing with outliers? How about missing values? How about transformations?
1 What modules/libraries are you most familiar with? What do you like or dislike about them?
2 What are the supported data types in Python?
3 What is the difference between a tuple and a list in Python?
1 What are the different types of sorting algorithms available in R language?
There are insertion, bubble, and selection sorting algorithms.
2 What are the different data objects in R?
3 What packages are you most familiar with? What do you like or dislike about them?
4 How do you access the element in the 2nd column and 4th row of a matrix named M?
5 What is the command used to store R objects in a file?
6 What is the best way to use Hadoop and R together for analysis?
7 How do you split a continuous variable into different groups/ranks in R?
8 Write a function in R language to replace the missing value in a vector with the mean of that vector.
1 What is the purpose of the group functions in SQL? Give some examples of group functions.
2 Group functions are necessary to get summary statistics of a dataset. COUNT, MAX, MIN, AVG, SUM, and DISTINCT are all group functions.
3 Tell me the difference between an inner join, left join/right join, and union.
4 What does UNION do? What is the difference between UNION and UNION ALL?
5 What is the difference between SQL and MySQL or SQL Server?
6 If a table contains duplicate rows, does a query result display the duplicate values by default? How can you eliminate duplicate rows from a query result?
1 Tell me about how you designed the model you created for a past employer or client.
2 What are your favorite data visualization techniques?
3 How would you effectively represent data with 5 dimensions?
4 How is kNN different from k-means clustering?
kNN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data. Both accomplish different tasks.
5 How would you create a logistic regression model?
6 Have you used a time series model? Do you understand cross-correlations with time lags?
7 Explain the 80/20 rule, and tell me about its importance in model validation.
8 Explain what precision and recall are. How do they relate to the ROC curve?
Recall describes what percentage of true positives are described as positive by the model. Precision describes what percent of positive predictions were correct. The ROC curve shows the relationship between model recall and specificity – specificity being a measure of the percent of true negatives being described as negative by the model. Recall, precision, and the ROC are measures used to identify how useful a given classification model is.
9 Explain the difference between L1 and L2 regularization methods.
10 What is root cause analysis?
11 What are hash table collisions?
12 What is an exact test?
13 In your opinion, which is more important when designing a machine learning model: Model performance? Or model accuracy?
14 What is one way that you would handle an imbalanced dataset that’s being used for prediction? (i.e. vastly more negative classes than positive classes.)
15 How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
16 I have two models of comparable accuracy and computational performance. Which one should I choose for production and why?
17 How do you deal with sparsity?
18 Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy?
19 What are some situations where a general linear model fails?
20 Do you think 50 small decision trees are better than a large one? Why?
21 When modifying an algorithm, how do you know that your changes are an improvement over not doing anything?
22 Is it better to have too many false positives, or too many false negatives?
1 Tell me about a time when you took initiative.
2 Tell me about a time where you had to overcome a dilemma.
3 Tell me about a time where you resolved a conflict.
4 Tell me about a time you failed, and what you have learned from it.
5 Tell me about (a job on your resume). Why did you choose to do it and what do you like most about it?
6 Tell me about a challenge you have overcome while working on a group project.
7 When you encounter a tedious, boring task, how would you deal with it and motivate yourself to complete it?
8 What have you done in the past to make a client satisfied/happy?
9 What have you done in your previous job that you are really proud of?
10 What do you do when your personal life is running over into your work life?
面试官试图了解你是谁，以及你如何配合公司。 他们想知道你对数据科学和招聘公司的兴趣来自哪里。 看看这些例子，想想你最好的答案是什么，但要记住诚实地对待这些问题是很重要的。 这些问题没有正确的答案，但最好的答案是自信和微笑。
1 Which data scientists do you admire most? Which startups?
2 What do you think makes a good data scientist?
3 How did you become interested in data science?
4 Give a few examples of “best practices” in data science.
5 What/when is the latest data science book / article you read? What/when is the latest data mining conference / webinar / class / workshop / training you attended
6 What’s a project you would want to work on at our company?
7 What unique skills do you think you’d bring to the team?
8 What data would you love to acquire if there were no limitations?
9 Have you ever thought about creating a startup? Around which idea / concept?
10 What can your hobbies tell me that your resume can’t?
11 What are your top 5 predictions for the next 20 years?
12 What did you do today? Or what did you do this week / last week?
13 If you won a million dollars in the lottery, what would you do with the money?
14 What is one thing you believe that most people do not?
15 What personality traits do you butt heads with?
16 What are you passionate about?
1 How would you come up with a solution to identify plagiarism?
2 How many “useful” votes will a Yelp review receive?
3 How do you detect individual paid accounts shared by multiple users?
4 You are about to send one million emails. How do you optimize delivery? How do you optimize response?
5 You have a dataset containing 100K rows and 100 columns, with one of those columns being our dependent variable for a problem we’d like to solve. How can we quickly identify which columns will be helpful in predicting the dependent variable. Identify two techniques and explain them to me as though I were 5 years old.
6 How would you detect bogus reviews, or bogus Facebook accounts used for bad purposes?
7 How would you perform clustering on one million unique keywords, assuming you have 10 million data points – each one consisting of two keywords, and a metric measuring how similar these two keywords are? How would you create this 10 million data points table in the first place?
8 How would you optimize a web crawler to run much faster, extract better information, and better summarize data to produce cleaner databases?