## Data Analysis With R Interview Questions

Checkout Vskills Interview questions with answers in Data Analysis with R to prepare for your next job role. The questions are submitted by professionals to help you to prepare for the Interview.

Q.1 Is R good for data analysis?
R is an open source programming language that's optimized for statistical analysis and data visualization. Developed in 1992, R has a rich ecosystem with complex data models and elegant tools for data reporting.
Q.2 How R is used in data analysis?
As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.
Q.3 What mean by data analysis?
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase.
Q.4 What are the methods of data analysis?
Some common methods of data analysis are: Cluster analysis, Cohort analysis, Regression analysis, Factor analysis, Neural Networks, Data Mining and Text analysis.
Q.5 How R can be used for predictive analysis?
Predictive analysis in R Language is a branch of analysis which uses statistics operations to analyze historical facts to make predict future events. Methods like time series analysis, non-linear least square, etc. are used in predictive analysis.
Q.6 What do you understand by Data Cleansing?
Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
Q.7 Differentiate between data profiling and data mining?
Data mining mines actionable information while making use of sophisticated mathematical algorithms, whereas data profiling derives information about data quality to discover anomalies in the dataset.
Q.8 What is KNN imputation method?
The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
Q.9 What to do with missing or suspected data?
The most common approach to the missing data is to simply omit those cases with the missing data and analyze the remaining data. This approach is known as the complete case (or available case) analysis or listwise deletion.
Q.10 What do you understand by Outlier?
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. An outlier is a data point that differs significantly from other observations.
Q.11 What is “Clustering?”
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering divides the population into a number of groups with similar traits and assign them into clusters.
Q.12 What is K-mean Algorithm?
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. In K-means clustering algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.
Q.13 What do you understand by Collaborative Filtering?
Collaborative filtering (CF) is a technique used by recommender systems and is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating).
Q.14 What is a hash table collision?
A collision occurs when two keys are hashed to the same index in a hash table. Collisions are a problem because every slot in a hash table is supposed to store a single element. All key-value pairs mapping to the same index will be stored in the linked list of that index.
Q.15 What do you understand by Time Series Analysis?
Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a set period of time rather than just recording the data points intermittently or randomly.
Q.16 What are the characteristics of a good data model?
The criteria of a good data model are: Data can be easily consumed, Large data changes are scalable, provides predictable performance and adapts to changes in requirements.
Q.17 Differentiate between variance and covariance.
Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables.
Q.18 What do you understand by Normal Distribution?
Normal distribution or the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
Q.19 What do you understand by univariate, bivariate, and multivariate analysis?
Univariate analysis looks at one variable, Bivariate analysis looks at two variables and their relationship. Multivariate analysis looks at more than two variables and their relationship.
Q.20 Differentiate between R-Squared and Adjusted R-Squared.
The difference between R Squared and Adjusted R Squared is that R Squared is the type of measurement that represent the dependent variable variations in statistics, where Adjusted R Squared is a new version of the R Squared that adjust the variable predictors in regression models.
Q.21 What are the different data types in R?
R's basic data types are character, numeric, integer, complex, and logical.
Q.22 What does class () do in R?
The function class prints the vector of names of classes an object inherits from. Correspondingly, class<- sets the classes an object inherits from. Assigning NULL removes the class attribute. unclass returns (a copy of) its argument with its class attribute removed.
Q.23 What is the list in R?
A list is an object in R Language which consists of heterogeneous elements. A list can even contain matrices, data frames, or functions as its elements. The list can be created using list() function in R. Named list is also created with the same function by specifying the names of the elements to access them.
Q.24 What are data frames in R?
Data Frames in R Language are generic data objects of R which are used to store the tabular data. Data frames can also be interpreted as matrices where each column of a matrix can be of the different data types. DataFrame is made up of three principal components, the data, rows, and columns.
Q.25 What is the difference between list and vector in R?
A list holds different data such as Numeric, Character, logical, etc. Vector stores elements of the same type or converts implicitly. Lists are recursive, whereas vector is not. The vector is one-dimensional, whereas the list is a multidimensional object.
Q.26 What is the difference between factor and character in R?
Factors are used to represent categorical data. Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.
Q.27 What is Dimnames R?
The dimnames() is a built-in R function that can set or get the row and column names of R Objects. The dimnames() function accepts an R object like matrix, array, or data frame. The dimnames() function operates on both rows and columns at once.
Q.28 What is head and tail in R?
The head() and tail() function in R are often used to read the first and last n rows of a dataset.
Q.29 What does N () do in R?
The function n() returns the number of observations in a current group. A closed function to n() is n_distinct(), which count the number of unique values.
Q.30 Why do you want the Data Analysis with R professional job?
I want the Data Analysis with R professional job as I am passionate about data analysis and R programming language and applying both to make companies more efficient by using them and leverage the present technology portfolio to maximize their utility.