Lead Data Scientist Interview Preparation Guide

Setwd(“dir_path”)

Different syntax can be asked in R Data science interview questions.

At last, we have to iterate the list of files in the current working directory. Also, we need to put them together to form a data frame. Moreover, when the script encounters the first file in the file_list, then it creates the main data frame to merge everything into. This is done using the !exists conditional:

If their dataset exists, then a temp_dataset called temporary data frame will be created and added to the dataset. Moreover, we have to delete temporary data frame. That is been removed when we’re done with it using the rm(temp_dataset) command.
If dataset doesn’t exist (!exists is true), then we have to create it.

It's likely that you'll be interviewed by an HR rep, an end business user, and an IT pro. Each person will probably ask you to explain what big data is, and how the data analysis discipline works with big data to produce insights.

You can start your answer with something fundamental, such as "big data analysis involves the collection and organization of data, and the ability to discover correlations between the data that provide revelations or insights that are actionable." You must be able to explain this in terms that resonate with each interviewer; the best way to do this is to illustrate the definition with an example.

Bias:
“Bias is error introduced in your model due to over simplification of machine learning algorithm.” It can lead to underfitting. When you train your model at that time model makes simplified assumptions to make the target function easier to understand.

Low bias machine learning algorithms - Decision Trees, k-NN and SVM
Hight bias machine learning algorithms - Liear Regression, Logistic Regression
Variance:
“Variance is error introduced in your model due to complex machine learning algorithm, your model learns noise also from the training dataset and performs bad on test dataset.” It can lead high sensitivity and overfitting.

Random forest is a versatile machine learning method capable of performing both regression and classification tasks. It is also used for dimentionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

In Random Forest, we grow multiple trees as opposed to a single tree. To classify a new object based on attributes, each tree gives a classification. The forest chooses the classification having the most votes(Over all the trees in the forest) and in case of regression, it takes the average of outputs by different trees.

Regularization is the process of adding tunning parameter to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector. This constant is often the L1(Lasso) or L2(ridge). The model predictions should then minimize the loss function calculated on the regularized training set.

A feature vector is an n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics, called features, of an object in a mathematical, easily analyzable way.

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.

These are extraneous variables in a statistical model that correlate directly or inversely with both the dependent and the independent variable. The estimate fails to account for the confounding factor.

Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample.