Uber – Data Science Interview Questions
Here is the list Data Science Interview Questions which are recently asked in Uber company. These questions are included for both Freshers and Experienced professionals. Our Data Science Training has Answered all the below Questions.
1. Will Uber cause city congestion?
Yes. Recent studies show that Uber account for just 1-3 percent of vehicle miles travelled (VMT) in the broader metropolitan areas of each city. But the numbers spike when zooming in on the core county of each city.
2. What are the metrics you will use to track if Uber’s paid advertising strategies to acquire customers work? How will you figure out the acceptable cost of customer acquisition?
Tracking the three metrics—CAC, CLTV, and Churn Rate—provides insight into key aspects of the business, including the success of the sales team, pricing strategy, and customer service.
3. Explain principal components analysis with equations.
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
4. Explain about the various time series forecasting technqiues.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
5. Which machine learning algorithm will you use to solve a Uber driver accepting request?
Many uber cars on the road send GPS locations every 4 seconds, so to predict traffic the driver's app's GPS location data is used. We can represent the entire road network on a graph to calculate the ETAs. We can use AI simulated algorithms or simple Dijkstra's algorithm to find out the best route in the graph.
6. How will you compare the results of various machine learning algorithms?
- If you have a new dataset, it is a good idea to visualize the data using different techniques in order to look at the data from different perspectives.
- The same idea applies to model selection. Use a number of different ways of looking at the estimated accuracy of the machine learning algorithms in order to choose the one or two to finalize.
- A way to do this is to use different visualization methods to show the average accuracy, variance and other properties of the distribution of model accuracies.
7. What is Data Science?
Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. Statistics, Visualization, Deep Learning, Machine Learning, are important Data Science concepts.
8. Can you name the type of biases that occur in machine learning?
- Sample Bias.
- Prejudice Bias.
- Confirmation Bias.
- Group attribution Bias.
9. What is boosting?
Boosting is an ensemble modeling technique which attempts to build a strong classifier from the number of weak classifiers. It is done building a model by using weak models in series. Firstly, a model is built from the training data.
10. How would you create a taxonomy to identify key customer trends in unstructured data?
A model does not hold any value if it cannot produce actionable results, an experienced data analyst will have a varying strategy based on the type of data being analysed. Also, any sensitive data of the customer needs to be protected. It is also advisable to consult with the stakeholder to ensure that you are following all the compliance regulations of the organization and disclosure laws, if any.
11. What are the basic assumptions to be made for linear regression?
- Linear relationship.
- Multivariate normality.
- No or little multicollinearity.
- No auto-correlation.
12. How to solve multi-collinearity?
Remove some of the highly correlated independent variables. Linearly combine the independent variables, such as adding them together. Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
13. How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?
It can solve it in two steps. Use k-means to group previous journeys in similar area (relatively small geographical size). Then use the metric which analyses how long it took for a driver to find the client once they arrived to the pick-up location. The locations with smaller ‘search for a customer’ times will be the most appropriate.Additionally the model should be using maps data, which should identify whether it is possible to pick up people at those points or not.
14. If we added one rider to the current SF market, how would that affect the existing riders and drivers?
Yes, it affects the existing riders and drivers causing traffic congestion, pricing, allocation that affect the performance.
15. What are the different performance metrics for evaluating Uber services?
Average revenue per user, number of rides per user per month by Product, number of cancelled rides, Average Length of the ride, Number of returning Users
16. How will you decide which version (Version 1 or Version 2) of the Surge Pricing Algorithms is working better for Uber ?
Surge pricing automatically goes into effect when there are more riders in a given area than available drivers. This encourages more drivers to serve the busy area over time and shifts rider demand, to maintain reliability and restore balance.
17. How will you explain JOIN function in SQL to a 10 year old ?
SQL JOIN. A JOIN clause is used to combine rows from two or more tables, based on a related column between them. Notice that the "CustomerID" column in the "Orders" table refers to the "CustomerID" in the "Customers" table.
18. How can you iterate over a list and also retrieve element indices at the same time?
There is an optional start argument to the enumerate function, which I find very helpful when I need to count from 1 or any other number instead of 0.for index, value in enumerate(numbers, start=1):print 'The value at position', index, 'is', value.
19. How can you assess a good logistic model?
It examines whether the observed proportions of events are similar to the predicted probabilities of occurence in subgroups of the data set using a pearson chi square test. Small values with large p-values indicate a good fit to the data while large values with p-values below 0.05 indicate a poor fit.
20. Are expected value and mean value different?
Mean is defined as the sum of a collection of numbers divided by the number of numbers in the collection. The calculation would be "for i in 1 to n, (sum of x sub i) divided by n." Expected value (EV) is the long-run average value of repetitions of the experiment it represents.
21. What is Interpolation and Extrapolation?
When we predict values that fall within the range of data points taken it is called interpolation. When we predict values for points outside the range of data taken it is called extrapolation.
22. Describe the structure of Artificial Neural Networks?
ANN is made of three layers namely input layer, output layer, and hidden layer/s. There must be a connection from the nodes in the input layer with the nodes in the hidden layer and from each hidden layer node with the nodes of the output layer. The input layer takes the data from the network.
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