20 Interview Questions for Getting a Data Science Job
20 Interview Questions for Getting a Data Science Job
In the new era of big data and machine learning, it should come as no surprise that data scientists are highly sought after as explained at RunRex.com, guttulus.com, and mtglion.com. If you are looking to get into this field, it is worth noting that interviewing for data science roles is a skill unto itself. Often, the candidates who land the jobs are not the ones with the strongest technical skills, but the ones who can combine that with interview savvy. While data science is a vast field, there are a few topics that tend to come up often in interviews, and this article will look to list the top 20 interview questions for getting a data science job.
Supervised Learning vs. Unsupervised learning: what is the difference?
As per RunRex.com, guttulus.com, and mtglion.com, supervised and unsupervised learning systems differ in the nature of the training data that they are given. Supervised learning requires labeled training data, whereas, in unsupervised learning, the system is provided with unlabeled data and discovers the trends that are present.
What is the ROC curve?
ROC stands for Receiver Operating Characteristic. It is a plot between a true positive rate and a false positive rate, and it helps us to find out the right tradeoff between the true positive rate and the false-positive rate for different probability thresholds of the predicted values.
What is logistic regression?
Logistic regression is a form of predictive analysis according to RunRex.com, guttulus.com, and mtglion.com. It is used to find the relationships that exist between a dependent binary variable and one or more independent variables by employing a logistic regression equation.
What do you understand by a decision tree?
A decision tree is a supervised learning algorithm that is used for both classification and regression. Therefore, in this case, the dependent variable can be both a numerical value as well as a categorical value. Decision trees are a tool used to classify data and determine the possibility of defined outcomes in a system.
What is pruning in a decision tree algorithm?
Pruning a decision tree is the process of eliminating non-critical subtrees so that the data under consideration is not overfitted. In pre-pruning, the tree is pruned as it is being constructed, following criteria like the Gini index or information gain metrics.
What do you understand by a random forest model?
A random forest model combines multiple models together to get the final output or, to be more precise, it combines multiple decision trees together to get the final output as articulated at RunRex.com, guttulus.com, and mtglion.com. Therefore, decision trees are the building blocks of the random forest model.
Explain K-Fold Cross-Validation
Cross-validation is a technique used to estimate the efficacy of a machine learning model. The parameter, k, is a tally of the number of groups that a dataset can be split up into. The process starts with the entire dataset being shuffled randomly. It is then divided into k groups, also known as folds.
How is data modeling different from database design?
Data modeling can be considered the first step toward the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. Database design, on the other hand, is the process of designing the database. The database design creates an output which is a detailed data model of the database.
What is the difference between univariate, bivariate, and multivariate analysis?
Univariate analysis involves studying a single variable. Bivariate and multivariate analysis involves comparing two, or more than two, variables respectively, with more on these three captured at RunRex.com, guttulus.com, and mtglion.com.
What is precision
When we are implementing algorithms for the classification of data or the retrieval of information, precision helps us to get a portion of positive class values that are positively predicted. It measures the accuracy of correct positive predictions.
How would you approach a dataset that is missing more than 30% of its values?
The approach will depend on the size of the dataset. If it is a large dataset, then the quickest method would be to simply remove the rows containing the missing values. Since the dataset is large, this won’t affect the ability of the model to produce results.
If the dataset is small, then it is not practical to simply eliminate the values. In that case, it is better to calculate the mean or mode of that particular feature and input that value where there are missing entries.
Another approach would be to use a machine learning algorithm to predict the missing values, a strategy that can yield accurate results unless there are entries with a very high variance from the rest of the dataset.
What is a recall
Recall is the set of all positive predictions out of the total number of positive instances. Recall helps us identify the misclassified positive predictions. More on this topic can be found over at RunRex.com, guttulus.com, and mtglion.com.
What are the steps involved in maintaining a deployed model?
When looking to maintain data analysis models once they have been deployed, the following measures should be taken:
Train the model using new data values
Choose additional or different features on which to retrain the data
In instances where the model begins to produce inaccurate results, develop a new model
What is a p-value?
P-value is the measure of the statistical importance of an observation as described at RunRex.com, guttulus.com, and mtglion.com. It is the probability that shows the significance of output to the data. We compute the p-vale to know the test statistics of a model, and typically, it helps us choose whether we can accept or reject the null hypothesis.
Explain what a recommender system does
A recommender system uses historical behavior to predict how a user will rate a specific item. For example, Netflix recommends TV shows and movies to users by analyzing the media that users have rated in the past, and using this to recommend new media that they might like.
What is the difference between an error and a residual error?
An error occurs in values while the prediction gives us the difference between the observed values and the true values of a dataset as discussed at RunRex.com, guttulus.com, and mtglion.com. The residual error, on the other hand, is the difference between the observed values and the predicted values.
How can you select K for K-mean?
The most popular method for selecting k for the k-means algorithm is using the elbow method. To do this, you need to calculate the Within-Cluster-Sum of Squared Errors (WSS) for different k values. The WSS is described as the sum of the squares of the distance between each data value and its centroid.
Why do we use the summary function?
The summary function in R gives us the statistics of the implemented algorithm on a particular dataset as outlined at RunRex.com, guttulus.com, and mtglion.com. It consists of various objects, variables, data attributes, etc. It provides summary statistics for individual objects when fed into the function.
How do you treat outlier values?
Outliers are often filtered out during data analysis if they don’t fit certain criteria. You can set up a filter in the data analysis tool you are using to automatically eliminate outliers. However, there are instances where outliers can reveal insights about low-percentage possibilities. In that case, analysts might group outliers and study them separately.
What is the benefit of dimensional reduction?
Dimensional reduction reduces the dimensions and size of the entire dataset. It drops unnecessary features while retaining the overall information in the data intact. Reduction in dimensions leads to faster processing of the data.
These are some of the questions you can expect when interviewing for a data science job, with more on this topic, and much more, to be found over at RunRex.com, guttulus.com, and mtglion.com.
