87 Commonly Asked Data Science Interview Questions

This can lead to wrong conclusions in numerous different means. A confounding variable here would be any other variable that affects both of these variables, such as the age of the subject. When you perform a hypothesis test in statistics, a p-value can help you determine the strength of your results. Based on the value it will denote the strength of the results. Point Estimation gives us a particular value as an estimate of a population parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters.

  • TensorFlow is considered a high priority when learning Data Science because it provides support for languages such as C++ and Python.
  • In other words, selection bias is a distortion of statistical analysis that results from the sample collecting method.
  • Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.
  • If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate.

Boosting is an iterative method which allows you to adjust the weight of an observation depends upon the last classification. Boosting decreases the bias error and helps you to build strong predictive models. AB testing used to conduct random experiments with two variables, A and B. The goal of this testing method is to find out changes to a web page to maximize or increase the outcome of a strategy.

Data Science Interview Questions [2022 Prep Guide]

You can describe notable moments in your data science career, like personal projects or awards, to make an impact on interviewers. A confusion matrix is used to determine the efficacy of a classification algorithm. It is used because a classification algorithm isn’t accurate when there are more than two classes of data, or when there isn’t an even number of classes. A time-series analysis is a form of data analysis that looks at data values collected in a particular sequence. It both studies the data collected over time, and factors in the different points in time in which data was collected. An outlier is a data value that lies at a great distance from the other values in a dataset.

A data set used for performance evaluation is called a test data set. There is no escaping the relationship between bias and variance in machine learning. List the differences between supervised and unsupervised learning. A perfect example of the Markov Chains is the system of word recommendation. In this system, the model recognizes and recommends the next word based on the immediately previous word and not anything before that. Deep Learning is one of the essential factors in Data Science, including statistics.


Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Tell me about a time when your project didn’t go according to plan and what you learned from it. The below diagram explains a step-by-step model of the Markov Chains whose output depends on their current state. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing, or stretching. Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas.

  • It is a neural network method based on convolutional neural networks .
  • This means the input layers, the data coming in, and the activation function is based upon all nodes and weights being added together, producing the output.
  • Rather, recruiters want to know whether you understand the foundations of the discipline and how it fits into a business context.
  • A/B Testing is a statistical hypothesis testing meant for a randomized experiment with two variables, A and B.
  • Underfitting would occur, for example, when fitting a linear model to non-linear data.
  • As such, there are plenty of opportunities for those interested in pursuing a data scientist career.
  • If it is a large dataset, then the quickest method would be to simply remove the rows containing the missing values.

Error represents how observed data differs from the actual population. While a residual represents the way observed data differs from the sample population data. The Support Vector Machine algorithm has high variance and low bias. In order to change the trade-off, we can increase the parameter C.

Article was published on: 10/7/22

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