Data Science is one of the hottest jobs today. According to The Economic Times, the job postings for the Data Science profiles have grown over 400 times over the past year. So, if you want to start your career as a Data Scientist, here are some top Data Science interview questions and answers which will help you crack your interview.

### What do you understand about the true-positive rate and false-positive rate?

True positive rate: In Machine Learning, true-positive rates, which are also referred to as sensitivity or recall, are used to measure the percentage of actual positives which are correctly identified. Formula: True Positive Rate = True Positives/Positives False positive rate: False positive rate is basically the probability of falsely rejecting the null hypothesis for a particular test. The false-positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positive) upon the total number of actual events. Formula: False-Positive Rate = False-Positives/Negatives.

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### 4 What is ensemble learning?

When we are building models using Data Science and Machine Learning, our goal is to get a model that can understand the underlying trends in the training data and can make predictions or classifications with a high level of accuracy.

However, sometimes some datasets are very complex, and it is difficult for one model to be able to grasp the underlying trends in these datasets. In such situations, we combine several individual models together to improve performance. This is what is called ensemble learning.

### 4 How can we select an appropriate value of k in k-means?

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.

## Advanced Data Science Interview Questions

First, we will load the **ggplot2** package:

Next, we will use the **dplyr** package:

To extract those particular records, use the below command:

### 3 How are Data Science and Machine Learning related to each other?

Data Science and Machine Learning are two terms that are closely related but are often misunderstood. Both of them deal with data. However, there are some fundamental distinctions that show us how they are different from each other.

Data Science is a broad field that deals with large volumes of data and allows us to draw insights out of this voluminous data. The entire process of Data Science takes care of multiple steps that are involved in drawing insights out of the available data. This process includes crucial steps such as data gathering, data analysis, data manipulation, data visualization, etc.

Machine Learning, on the other hand, can be thought of as a sub-field of Data Science. It also deals with data, but here, we are solely focused on learning how to convert the processed data into a functional model, which can be used to map inputs to outputs, e.g., a model that can expect an as an input and tell us if that contains a flower as an output.

In short, Data Science deals with gathering data, processing it, and finally, drawing insights from it. The field of Data Science that deals with building models using algorithms is called Machine Learning. Therefore, Machine Learning is an integral part of Data Science.