Data Scientist

Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

Yes, rotation (orthogonal) is necessary because it maximizes the difference between variance captured by the component. This makes the components easier to interpret. Not to forget, that’s the motive of doing PCA where, we aim to select fewer components (than features) which can explain the maximum variance in the data set. By doing rotation, the relative location of the components doesn’t change, it only changes the actual coordinates of the points.

If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more number of components to explain variance in the data set.

The purpose of this question is to determine the motivation behind the candidate's choice of applying and interviewing for the position. Their answer should reveal their inspiration for working for the company and their drive for being a data scientist. It should show the candidate is pursuing the position because they are passionate about data and believe in the company, two elements that can determine the candidate's performance. Answers to look for include:

☛ Interest in data mining

☛ Respect for the company's innovative practices

☛ Desire to apply analytical skills to solve real-world issues with data

The encoder CNN can basically be thought of as a feature extraction network, while the decoder uses that information to predict the image segments by “decoding” the features and upscaling to the original image size.

☛ Take the entire data set as input.

☛ Look for a split that maximizes the separation of the classes. A split is any test that divides the data into two sets.

☛ Apply the split to the input data (divide step).

☛ Re-apply steps 1 to 2 to the divided data.

☛ Stop when you meet some stopping criteria.

☛ This step is called pruning. Clean up the tree if you went too far doing splits.

The main thing that residual connections did was allow for direct feature access from previous layers. This makes information propagation throughout the network much easier. One very interesting paper about this shows how using local skip connections gives the network a type of ensemble multi-path structure, giving features multiple paths to propagate throughout the network.

It is a model validation technique for evaluating how the outcomes of a statistical analysis will generalize to an independent data set. It is mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice. The goal of cross-validation is to term a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting and gain insight on how the model will generalize to an independent data set.

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from reoccurring.

Some drawbacks of the linear model are:

☛ The assumption of linearity of the errors.

☛ It can’t be used for count outcomes or binary outcomes

☛ There are overfitting problems that it can’t solve

The underlying principle of this technique is that several weak learners combined to provide a strong learner. The steps involved are

☛ Build several decision trees on bootstrapped training samples of data

☛ On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors

☛ Rule of thumb: At each split m=p√m=p

☛ Predictions: At the majority rule

You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small / medium sized effect, use ridge regression.

Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.

They are not different but the terms are used in different contexts. Mean is generally referred when talking about a probability distribution or sample population whereas expected value is generally referred in a random variable context.

Data scientists must be able to go beyond classroom theoretical applications to real-world applications. Your candidate's answer to this question will show how they allocate their time to finding the best way to detect outliers. This information is important to know because it demonstrates the candidate's analytical skills. Look for answers that include:

☛ Raw data analysis

☛ Models

☛ Approaches

While one should always keep the “no free lunch theorem” in mind, there are some general guidelines.

An experimental design technique for determining the effect of a given sample size.

True Positive Rate = Recall. Yes, they are equal having the formula (TP/TP + FN).

Classification technique is used widely in mining for classifying data sets.

The reason for asking this question is to discover how well the candidate approaches solving conflicts in a team environment. Their answer shows the candidate's problem-solving and interpersonal skills in stressful situations. Understanding these skills is significant because group dynamics and business conditions change. Consider answers that:

☛ Encourage discussion

☛ Demonstrate leadership

☛ Acknowledges recognizing and respecting different opinions

As explained above, each convolution kernel acts as it’s own filter/feature detector. So let’s say you’re doing object detection, it doesn’t matter where in the image the object is since we’re going to apply the convolution in a sliding window fashion across the entire image anyways.