When I wanted to become a backend developer my gorgeous boyfriend made some practical interview with me then created a repository and put the questions and answers there. Now I want to apply for a data scientist position in which unfortunately he has no experience, so I'm making this repository and want to put all my practices here.

1. you have buckets one 5 liters the other 3 liters, measure 4 liters:
   first I make the 3 liters bucket full and then pour it to 5 liters bucket then I make it full again and pour it to the 5 liters bucket till it gets full so the amount of water left in the 3 liters bucket is 1 liter then I make the 5 liters bucket empty and pour that 1 liter in it then make the 3 liters bucket full again and pour it to the 5 liters bucket which had 1 liter inside, so the result is 4 liters of water in the 5 liters bucket.

1. Differences between supervised and unsupervised:
   In supervised the data has label
   
   

2. How is logistic regression done?
   
   

3. Explain steps in decision tree
   
   

4. How do you build a random forest?
   
   

5. How can you avoid overfitting? lasso regression
   
   

6. What are feature selection methods? Wrapper methods: forward selection, backward selection. Filter methods: Chi-Square, ANOVA (learn f-distribution, t-test, p-value, null hypothesis and significance level)
   
   

7. How do you deal with more than 30% missing value?
   
   

8. Explain dimensionality reduction and its benefits
   
   

9. How will you calculate eigen values and eigen vectors of a 3 by 3 matrix
   
   

10. How to maintain deployed model?
    
    

11. What are recommender systems? collaborative, content-based
    
    

12. It rains on saturday with 0.6 probability and rains on sunday with 0.2 probability what is the probability that it rains this weekend?
    
    

13. How can you select k for k-means?
    
    

14. What is the significance of p-value?
    
    

15. How can outlier values be treated?
    
    

16. How can you say that a time series data is stationary?
    
    

17. How can you calculate accuracy using confusion matrix?
    
    

18. Write the equation and calculate precision and recall rate
    
    

19. If a drawer contains 12 red socks, 16 blue socks, and 20 white socks, how many must you pull out to be sure of having a matching pair?
    
    

20. People who bought this, also bought... recommendations seen on Amazon is a result of which algorithm?
    
    

21. Write a SQL query to list orders with customer information?
    
    

22. You are given a dataset on cancer detection. You've built a classification model and achieved an accuracy of 96%.Why shouldn't you be happy with your model performance? What can you do about it?
    
    

23. Which of the following ML algorithms can be used for imputing missing values of both categorical and continuous variables? K-means-clustering --> crash, linear regression --> crash, Decision tree --> crash, K-NN works
    
    

24. calculate entropy [0,0,0,1,1,1,1,1]
    
    

25. What is the standard of the mean?

I had to answer to a few questions in the first phase of interview for the data scientist position at cafebazaar and I was rejected because the questions needed deep understanding of linear algebra which I had forgotten, I can't remember what they exactly asked for, but I can give you a sense and another important point is that I now write the answer I think is true, but I'm not sure so don't rely on my answers:

1. We generated 5 random numbers using uniform distribution from [0, d] and the numbers are 5, 11, 13, 17, 23 find the minimum d with more than 95% probability.
   So we can write
   

2. Given uniform distributions X and Y and the mean 0 and standard deviation 1 for both, what’s the probability of 2X > Y? as you see this question is supeeeeer easyyyyyy but I couldn't answer :)))) cause I didn't expect these kinds of questions so shout out to you, they ask something like this expect it.

3. What can you say about the rank of the matrix when the number of samples is many more than the number of features? pay attention the first phase is a test so there is no one who you can talk to or there is no box in which you can explain what you know if I had the chance I could say that in order to find out a linear regression model I know gradient descent and normal equation and in normal equation we should calculate the inverse of X multiplied by X transpose and this term is not invertible if the number of samples in many more than features or if features have high correlation with each other, but I just could check one answer about the rank I mean they ask for pure linear algebra. Well, I couldn't even remember what the rank of a matrix was. The rank of a matrix is the number of linearly independent columns (or rows), you can find the rank of a matrix using echelon. To be honest I don't have an intuition but there is a theorem which says the column rank is equal to the row rank, so the first thing we can say is that the rank of this matrix at most will be the min(number of rows, number of columns). In this question the rank will be at most the number of samples.
   Full rank means the rank of the matrix is the largest possible number so if your features have high correlation with each other (and the number of features are less than the number of samples) your matrix won't be full rank.

This part is not a question asked in any of the interviews, but I had to write it here cause it made me think of my whole life decisions. This is the story happened to me which may happen to you too. Once I had to solve a coding challenge, it was an imbalanced dataset, and I had to train a classifier. As you can guess precision is not a good metric in this problem, cause even always predicting false gives you a high prediction. Then I found out that recall is as important as precision, and the first thing popped to my mind was F1-score but then in my searches I heard about another metric called ROC_AUC which was claimed to work well for an imbalanced dataset. That's wrong, in ROC_AUC we are calculating the area under the receiver operating characteristic curve. In this curve the y-axis is the sensitivity or true positive rate:
True Positive Rate = True Positives / (True Positives + False Negatives)
It measures the proportion of the positive data that we classify correctly. The x-axis is the false positive rate:
False Positive Rate = False Positives / (False Positives + True Negatives)
it measures the proportion of the negative data that we classify as positive, so if your dataset is highly imbalanced, and most of the data is labeled negative then the number of True Negative samples can affect the whole curve, if the model classifies all samples as negative then the number of True Negative will be so large and False Positive Rate will be so small, and it assumes the model is so good so this metric is not good at all for imbalanced dataset classification. To solve it, we should use precision instead of false positive rate:
Precision = True Positives / (True Positives + False Positives)
it measures the proportion of the data the model classifies as positive is really positive, and the areas under this curve can be used as a good metric.

These optimizations were not asked in interview, but it's highly important to fully understand them:

Stochastic Gradient Descent: In regular gradient descent we should bring whole the training dataset to our calculations which will be so much if our dataset is so large and our model has so many parameters. In this situation we can use SGD, this algorithm stochastically chooses a mini batch of the dataset in each step and does the calculation.

Momentum: I always think about this optimization with a physics intuition. The problem is that we want to make gradient descent faster and avoid oscillation. We change the calculations this way:
"V" of derivative of "W" (parameters of the model) is equal to "B" (hyper parameter) multiplied by "V" of derivative of "W" plus derivative of "W".
Then "W" in each step is updated this way:
"W" will be equal to "W" minus learning rate multiplied by "V" of derivative of "W"
In here I see "V" as velocity and derivative of "W" as acceleration. As the equations show if faster move in one dimension is needed then the acceleration makes velocity bigger and bigger each step, and the parameters of the model will be updated faster and faster and if one dimension oscillate in regular gradient descent in this algorithm acceleration will be positive one step and negative the other step and doesn't let velocity to get big so parameters will not change a lot.

Adam: I had an intuition for SGD that we use a mini-batch instead of the whole dataset cause our dataset is so large or the model has so many parameters, and I even had a physics intuition for Momentum optimization that to avoid oscillating and make the algorithm faster we use what I called "velocity" and "acceleration" to move faster in a dimension needed and avoid oscillating by changing the parameters by "V" of derivative of "W" but to be honest I have no intuition for ADAM optimization, I've seen the algorithm and this is something I can't understand but is working

I can guarantee they will ask you this question. It has some simple answers but make sure you review them before the interview:

1. Drop the Nulls.
2. Replace them with mean or median

1. Use the mean or median but not of the whole column, only of a few samples that are close to it.
2. Pick an algorithm or change them, so they don't crash because of Null, they just don't put the Null into calculation.
3. Predict the Nulls. You can choose the column which has Nulls as your target column and then the known values can be used for training, then we can predict the unknown values by a simple regression.

1. Mice (Multivariate Imputation By Chained Equations)algorithm: If you've gotten the idea of predicting the Null values by regression (part 3 of Medium) this algorithm will be super easy. Most probably more than just one column in your dataset contains Null values:
   

   1. Replace the Null values by the mean of their column.
   2. Go through the columns and for each of them predict the Null values by regression (And other columns don't contain Nulls).
   3. Calculate the difference between the new matrix and the previous one.
   4. If the matrix is not close to zero then do the step two and three again.

2. Matrix Factorization: I'll go through an example, suppose you wanna build a recommender system for Netflix when a person creates an account you ask them to score how much they like specific categories and knowing this can help you a lot, but most of the people just skip the part now you have a matrix R of size nm where n is the number of users and m is the number of categories and it contains so many Nulls. Consider a positive integer k <= min(n,m), the main objective is to find matrix factors W with size nk and H is size k*m in which R is close to WH. As these matrices are completed, the predicted rating of user u to item i (r at index u and i) can be estimated by the inner product of the corresponding user-item feature vector pairs. Estimating the parameters of W and H can be achieved by searching around the global (or local) minimum of the quadratic cost function F and once it's done, the other missing values can be estimated by the inner product too.

This is another most repeated questions, how will you transform categorical columns to numerical columns?

1. Ordinal encoder and Label encoder
2. One hot encoder and Dummy encoder

1. Hashing: By hashing we can transform the categories into a fixed size number
2. Base N encoding: We can first use ordinal encoding and then transform the numbers by base N, look at the example below:

ordinal encoding

base 4

Let’s explore a simple 24-hour time dataset, we want to convey its cyclical nature to our model.

we want our machine learning model to see that 23:55 and 00:05 are 10 minutes apart, but as it stands, those times will appear to be 23 hours and 50 minutes apart!

Here's the trick: we will create two new features, deriving a sine transform and cosine transform of the seconds-past-midnight feature. We can forget the raw “seconds” column from now on.

seconds_in_day = 24*60*60

df['sin_time'] = np.sin(2*np.pi*df.seconds/seconds_in_day)
df['cos_time'] = np.cos(2*np.pi*df.seconds/seconds_in_day)

df.drop('seconds', axis=1, inplace=True)

df.head()

Notice that now, 5 minutes before midnight and 5 minutes after is 10 minutes apart