Data Science, Data Analyst, Data Engineering

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DATA SCIENCE INTERVIEW QUESTIONS Q. What is a bias-variance trade-off? A. If the algorithm is too basic (hypothesis with linear eq. ), it may be prone to errors due to strong bias and low variance. If the algorithms are too sophisticated (hypothesis with a high degree eq. ), the variance and bias may be considerable. The new entries will not fare well in the latter scenario. Trade-off, also known as Bias Variance Trade-off, is something that exists between these two situations. There is a tradeoff between bias and variance because of this tradeoff in complexity. It's impossible for an algorithm to be both more complex and less complex at the same time. Q. What are the support vectors in SVM? A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM. Q. Describe different regularization methods, such as L1 and L2 regularization? A. L1 regularization, also known as L1 norm or Lasso (in regression problems), combats overfitting by shrinking the parameters towards 0. This makes some features obsolete. L2 regularization, or the L2 norm, or Ridge (in regression problems), combats overfitting by forcing weights to be small, but not making them exactly 0. Q. What is correlation and covariance in statistics? A. Both covariance and correlation measure the relationship and the dependency between two variables. Covariance indicates the direction of the linear relationship between variables. Correlation measures both the strength and direction of the linear relationship between two variables. Correlation values are standardized. Covariance values are not standardized.

How Uber works With a huge database of drivers, as soon as a user requests for car, their algorithms match a user with the most suitable driver within a 15 second window to the nearest driver. Uber stores and analyses data on every single trip the users take which is leveraged to predict the demand for cars, set the fares and allocate sufficient resources. Data science team at Uber also performs in-depth analysis of the public transport networks across different cities so that they can focus on cities that have poor transportation and make the best use of the data to enhance customer service experience.

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