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Python is best apt at handling colossal data while R has memory constraints and is slower in response to large data. For handling unstructured data, R provides a vast variety of support packages.

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Python is best suited for enterprise level and for increasing software productivity. R provides extensive text analytics libraries but its data mining libraries are still in a nascent stage. Q.9 Python or R – Which one would you prefer for text analytics?īoth Python and R provide robust functionalities for working with text data. To solve this, we will use the to_datetime() function. Q.8 How can you convert date-strings to timeseries in a series?

#Basic data science interview questions series

Yes, we can stack the two series horizontally using concat() function and setting axis = 1. Q.7 Can you stack two series horizontally? If so, how? Read our latest article on K-means clustering and learn everything about it. On the contrary, the K in K-means specify the number of centroids. The K in KNN is the number of nearest data points. K-means is an unsupervised learning algorithm that looks for patterns that are intrinsic to the data. In order to train this algorithm, we require labeled data. Q.6 How are KNN and K-means clustering different?įirstly, KNN is a supervised learning algorithm. First, we will create a list of 10 numbers – s1 = pd.Series()

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Q.5 How to find the positions of numbers that are multiples of 4 from a series?įor finding the multples of 4, we will use the argwhere() function.

For this, we create two series s1 and s2 – s1 = pd.Series() Q.4 How will you verify if the items present in list A are present in series B?

Finding appropriate value with clustering.

Replacing value with the mean, median and mode of the observation.

Deleting the observation (not always recommended).

There are several ways to handle missing values in the given data. Q.3 How will you handle missing values in data? Based on this linear relationship, we establish a model that predicts the future outcomes based on an increase in one variable. By linear relationship, we mean that an increase in a variable would lead to increase in the other variable and a decrease in one variable would lead to attenuation in the second variable as well. Linear Regression is a statistical technique of measuring the linear relationship between the two variables.

Q.2 How will you explain linear regression to a non-tech person? it shows that the data is closer to the mean and the frequency of occurrences in data are far from the mean. It is a type of probability distribution that is symmetric about the mean. Normal Distribution is also known as Gaussian Distribution. Q.1 What do you understand by the term Normal Distribution? Not only this, all the below data science interview questions cover the important concepts of data science, machine learning, statistics, and probability.

And finally open-ended and behavior-based data science interview questions.

Non-technical data science interview questions based on your problem-solving ability, analytical thinking, and skills.Technical data science interview questions related to different programming languages like R, SQL, Python.Project-based data science interview questions based on the projects you worked on.Scenario-based data science interview questions to help build critical thinking and improve performance under pressure.

This blog consists of the following types of questions – This will surely help you to get your desired data science job. We bring to you a variety of challenging, insightful data science interview questions curated by top data scientists, industry experts, and experienced professionals widely asked in the industry. So, let’s start with the first part – top Data Science Interview Questions for Freshers. Data Science Interview Questions for Experienced.Data Science Interview Questions for Intermediate Level.Data Science Interview Questions for Freshers.Bookmark the links now and thank us later –

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