Data Scientist

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.

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.

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.

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)

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.

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

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.

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.

It is a statistical hypothesis testing for randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. An example for this could be identifying the click through rate for a banner ad.

naive Bayes is so ‘naive’ because it assumes that all of the features in a data set are equally important and independent. As we know, these assumption are rarely true in real world scenario.

You will want to update an algorithm when:

☛ You want the model to evolve as data streams through infrastructure

☛ The underlying data source is changing

☛ There is a case of non-stationarity

Logistic Regression is also known as the logit model. It is a technique to forecast the binary outcome from a linear combination of predictor variables.

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because - as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

This one was pretty interesting since it’s not something companies usually ask. As you would expect, I got this question from a company focused on Computer Vision. This answer has 2 parts to it. Firstly, convolutions preserve, encode, and actually use the spatial information from the image. If we used only FC layers we would have no relative spatial information. Secondly, Convolutional Neural Networks (CNNs) have a partially built-in translation in-variance, since each convolution kernel acts as it’s own filter/feature detector.

This question helps determine the candidate's experience from a holistic perspective and reveals experience in demonstrating interpersonal, communication and technical skills. It is important to understand this because data scientists must be able to communicate their findings, work in a team environment and have the skills to perform the task. Here are some possible answers to look for:

☛ Project management skills

☛ Examples of working in a team environment

☛ Ability to identify errors

It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.