The average interview for a data scientist position at Amazon can takes about 2-3 weeks.

• Stage 1: Initial phone screen by HR.
  
• Stage 2: A phone screen with a product manager that is resume based and the
  
• Stage 3: A phone screen with a coding component (SQL, SAS, R and Python) and a Market entry case.
  
• Stage 4: An onsite interview composed of 6 one-on-one interviews, 5 formal and 1 informal interview over lunch. Each interview tests at least 1 leadership principle and also tests problem solving skills. The questions include SQL questions + statistics questions + machine learning questions, predictive modelling, exploratory analysis, etc.

• Q 1: I have table 1, with 1million records, with ID, AGE (column names) , Table 2 with 100 records with ID and Salary. How many records will the following query return SELECT A.ID,A.AGE,B.SALARY FROM TABLE 1 A LEFT JOIN TABLE 2 B ON A.ID = B.ID + WHERE B.SALARY > 50000 ( HE ASKED TO MODIFY THIS LINE OF QUERY) ?
• Q 2: Given a csv file with ID, and Quantity columns, 50million records and size of data is 2gig, write a program in a language of your choice to aggregate the QUANTITY column.
• Q 3: Write a Python function that displays the first n Fibonacci numbers.
• Q 4: If you have a table with a billion rows, how would you add a column inserting data from the original source without affecting the user experience?
• Q 5: Estimate the cumulative sum of top 10 most profitable products of the last 6 month for customers in Seattle.
• Q 6: Explain the concept of colinearity
• Q 7: Given a bar plot and imagine you are pouring water from the top, how to qualify how much water can be kept in the bar chart.
• Q 8: How to choose a model, and how to determine if a model is better than another.
• Q 9: What is linear regression?
• Q 10: Given a table with three columns, (id, category, value) and each id has 3 or less categories (price, size, color). How can I find the id's for the values with two or more category matches to one another? EG: ID1 (price 10, size M, color Red), ID2 (price 10, Size L, Color Red) , ID3 (price 15, size L, color Red) Then the output should be two rows: ID1 ID2 and ID2 ID3
• Q 11: What are hyperparameters, how do you tune them, how do you test them, how do you know if they worked for the particular problem.
• Q 12: Write Python code to return the count of words in a string
• Q 13: What is cross validation?
• Q 14: What is over-fitting? How do you avoid it?
• Q 15: What types of regularization exist? Which one is simpler to use?
• Q 16: Explain decision trees?
• Q 17: What are different metrics to classify a dataset?
• Q 18: What is bagging?
• Q 19: We have two models, one with 85% accuracy, one 82%. Which one do you pick? -
• Q 20: What is p-value and how can we use it?
• Q 21: How do you deal with unbalanced data where the ratio of positive and negative is huge?
• Q 22: Estimate the disease probability in one city given the probability is very low nation-wide. Randomly asked 1000 person in this city, all negative response responded negatively (NO disease). What is the probability of disease in this city?
• Q 23: How to do treat colinearity?
• Q 24: How do you inspect missing data and when are they important?
• Q 25: How will you apply machine learning to a business case, explain the algorithm and why you chose it?
• Q 26: Which machine learning algorithms/techniques are you familiar with?
• Q 27: What is k-mean?
• Q 28: How does speech synthesis work?
• Q 29: When you have a time series data by monthly, it has large data records, how will you find out a significant difference between this month and previous month
• Q 30: Navigate a binary tree
• Q 31: If you have a customer and want to decide whether they will buy today or not buy today and you know 1. where they live, 2. their income, 3. their gender, 4. their profession, how would you define a machine learning algorithm to figure this out?
• Q 32: How does a neural network with one layer and one input and output compare to a logistic regression?
• Q 33: Given a long sorted list and a short (4 element) sorted list, what algorithm would you use to search the long list for the 4 elements? How would the algorithm scale?
• Q 34: Given an unfair coin with the probability of heads not equal to .5, what algorithm could you use to create a list of random 1s and 0s?

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