Data Science Interview Questions and Answers
Question - 21 : - How is Data modeling different from Database design?
Answer - 21 : -
Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying data modeling techniques.
Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. Strictly speaking, database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.
Question - 22 : - What is precision?
Answer - 22 : -
Precision: When we are implementing algorithms for the classification of data or the retrieval of information, precision helps us get a portion of positive class values that are positively predicted. Basically, it measures the accuracy of correct positive predictions. Below is the formula to calculate precision:
Question - 23 : - What is a recall?
Answer - 23 : -
Recall: It is the set of all positive predictions out of the total number of positive instances. Recall helps us identify the misclassified positive predictions. We use the below formula to calculate recall:
Question - 24 : - What is a p-value?
Answer - 24 : -
P-value is the measure of the statistical importance of an observation. It is the probability that shows the significance of output to the data. We compute the p-value to know the test statistics of a model. Typically, it helps us choose whether we can accept or reject the null hypothesis.
Question - 25 : - Why do we use p-value?
Answer - 25 : -
We use the p-value to understand whether the given data really describes the observed effect or not. We use the below formula to calculate the p-value for the effect ‘E’ and the null hypothesis ‘H0’ is true:
Question - 26 : - How can we select an appropriate value of k in k-means?
Answer - 26 : -
Selecting the correct value of k is an important aspect of k-means clustering. We can make use of the elbow method to pick the appropriate k value. To do this, we run the k-means algorithm on a range of values, e.g., 1 to 15. For each value of k, we compute an average score. This score is also called inertia or the inter-cluster variance.
This is calculated as the sum of squares of the distances of all values in a cluster. As k starts from a low value and goes up to a high value, we start seeing a sharp decrease in the inertia value. After a certain value of k, in the range, the drop in the inertia value becomes quite small. This is the value of k that we need to choose for the k-means clustering algorithm.
Question - 27 : - What is a kernel function in SVM?
Answer - 27 : -
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
Question - 28 : - What is RMSE?
Answer - 28 : -
RMSE stands for the root mean square error. It is a measure of accuracy in regression. RMSE allows us to calculate the magnitude of error produced by a regression model. The way RMSE is calculated is as follows:
First, we calculate the errors in the predictions made by the regression model. For this, we calculate the differences between the actual and the predicted values. Then, we square the errors.
After this step, we calculate the mean of the squared errors, and finally, we take the square root of the mean of these squared errors. This number is the RMSE, and a model with a lower value of RMSE is considered to produce lower errors, i.e., the model will be more accurate.
Question - 29 : - What is a bias-variance trade-off in Data Science?
Answer - 29 : -
When building a model using Data Science or Machine Learning, our goal is to build one that has low bias and variance. We know that bias and variance are both errors that occur due to either an overly simplistic model or an overly complicated model. Therefore, when we are building a model, the goal of getting high accuracy is only going to be accomplished if we are aware of the tradeoff between bias and variance.
Bias is an error that occurs when a model is too simple to capture the patterns in a dataset. To reduce bias, we need to make our model more complex. Although making the model more complex can lead to reducing bias, and if we make the model too complex, it may end up becoming too rigid, leading to high variance. So, the tradeoff between bias and variance is that if we increase the complexity, the bias reduces and the variance increases, and if we reduce complexity, the bias increases and the variance reduces. Our goal is to find a point at which our model is complex enough to give low bias but not so complex to end up having high variance.
Question - 30 : - What is the benefit of dimensionality reduction?
Answer - 30 : -
Dimensionality 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.
The reason why data with high dimensions is considered so difficult to deal with is that it leads to high time consumption while processing the data and training a model on it. Reducing dimensions speeds up this process, removes noise, and also leads to better model accuracy.