Data Scientist Interview Preparation Guide

You could find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value.
In Pandas, there are two very useful methods:
isnull() and dropna() that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value (for example, 0), you could use the fillna() method.

Again as you would expect this is for a role in Computer Vision. Max-pooling in a CNN allows you to reduce computation since your feature maps are smaller after the pooling. You don’t lose too much semantic information since you’re taking the maximum activation. There’s also a theory that max-pooling contributes a bit to giving CNNs more translation in-variance.

This question will determine how the candidate approaches solving real-world issues they will face in their role as a data scientist. It will also determine how they approach problem-solving from an analytical standpoint. This information is vital to understand because data scientists must have strong analytical and problem-solving skills. Look for answers that reveal:

Examples of problem-solving methods
Steps to take to identify the barriers to performance
Benchmarks for assessing performance
"My approach to determining performance bottlenecks is to conduct a performance test. I then evaluate the performance based on criteria set by the lead data scientist or company and discuss my findings with my team lead and group."

Data scientists frequently have to combine large amounts of information from various devices in several formats, such as data from a smartwatch or cellphone. Answers to this question will demonstrate how your candidate's methods for organizing large data. This information is important to know because data scientists need clean data to analyze information accurately to offer recommendations that solve business problems. Possible answers may include:

☛ Automation tools
☛ Value correction methods
☛ Comprehension of data sets

No, they do not because in some cases they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.

Resampling is done in any of these cases:

☛ Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points
☛ Substituting labels on data points when performing significance tests
☛ Validating models by using random subsets (bootstrapping, cross validation)

Don’t get mislead by ‘k’ in their names. You should know that the fundamental difference between both these algorithms is, kmeans is unsupervised in nature and kNN is supervised in nature. kmeans is a clustering algorithm. kNN is a classification (or regression) algorithm.

kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other. The algorithm tries to maintain enough separability between these clusters. Due to unsupervised nature, the clusters have no labels.

These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.

If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.

Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for large number of outliers the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values.The most common ways to treat outlier values –

☛ 1) To change the value and bring in within a range
☛ 2) To just remove the value.

I felt this one would be important to highlight. Data normalization is very important preprocessing step, used to rescale values to fit in a specific range to assure better convergence during backpropagation. In general, it boils down to subtracting the mean of each data point and dividing by its standard deviation. If we don’t do this then some of the features (those with high magnitude) will be weighted more in the cost function (if a higher-magnitude feature changes by 1%, then that change is pretty big, but for smaller features it’s quite insignificant). The data normalization makes all features weighted equally.