Interview Questions For Data Science You Must Know

1. Top Interview Questions For Data Science

In our previous blogs, we have discussed so many Interview Questions for Data Science. Moreover, in this blog, we will discuss Must Known Interview Questions for Data Science. Also, we will discuss Top Known Interview Questions for Data Science which contains almost all important questions. Furthermore, Popular known Interview Questions for Data Science will help you in the preparation of the interview.

So, let’s explore interview Questions for Data Science.

Interview Questions for Data Science

2. Best Data Science Interview Questions

Following are the best Interview Questions for Data Science, let’s have a look

Q.1. Explain how to merge data frames in R?
merge() function is used to merge two data frames. The data frames must have same column names on which the merging happens.
Adding Columns
To merge two data frames (datasets) horizontally, use the merge function. Mostly, we use to join two data frames by one or more common key variables (i.e., an inner join).
# merge two data frames by ID
total <- merge(data frameA,data frameB,by=”ID”)
# merge two data frames by ID and Country
total <- merge(data frameA,data frameB,by=c(“ID”,”Country”)) .

Read more about data frames in detail

Q.2. What are Random Forests?
Ensemble technique called Bagging is like Random Forests. The idea behind this technique is to decorrelate the several trees. It generates on the different bootstrapped samples from training Data.And then we reduce the Variance in the Trees by averaging them. Hence, in this approach, it creates a large number of decision trees.
We use R package “randomForest” to create random forests.

Read more about Random Forests in detail

Q.3. What do you understand by decision tree?

We can consider R as very simple and easy. Along with this, it is accountable as well as having understandable Modelling techniques. Yet, a major drawback in them is that they have a poor predictive performance and poor Generalization on Test Set.

Read more about Decision tree in detail

Q.4. What is Ensemble Learning?

It is a type of Supervised Learning Technique. The basic idea behind it is to generate many Models on a training dataset and then combining their Output Rules.
We will use to generate lots of Models by training on Training Set and at the end combine them. Hence, we can use it to improve the predictive performance of Decision Trees by reducing the variance in the Trees by averaging them called Random forest technique.

Q.5. What are Ensemble Models in R?

• It is a type of model which combine results from different models and usually better than the result from one of the individual models.Some of the features of R Random Forests are as follows:
• It is the type of model which runs on large databases.
• Random Forests allows handling of thousands of input variables without variable deletion.
• It gives very good estimates of which variables are important in the classification.

Q.6. What is meant by Random Forest Classifier?

At training time, we can classify ensemble learning method of a Random forest and thus we can operate it by constructing a multitude of decision trees.
Adele Cutler and Leo Breiman developed it. Here a combination of two different methods is done – Leo’s bagging idea and the random selection of features introduced by Tin Kan Ho. He also proposed Random Decision forest in the year 1995.

Q.7. What are functions of Random forest in R?

If the number of cases in the training set is N, and the sample N case is at random, each tree will grow. Thus, this sample will be the training set for growing the tree. If there are M input variables, we specify a number m<<M such that at each node, m variables are selected at random out of the M. The value of m is constant during the forest growing and hence, each tree grows to the largest extent possible.

Q.8. What do you mean by data visualization in R?

In R, the most appealing things are its ability to create data visualizations with just a couple of lines of code. Also, it is an art of how to turn numbers into useful knowledge.
First of all, let us see the history of data visualization in R along with motivation for the same. Consequently, we will learn why R data visualization and types of it.

Read more about data visualization in detail

Q.9. What is a motivation behind data visualization in R?

• As we know humans are outstanding at detecting patterns and structures with their eyes.
• Its methods try to explore these capabilities.
• Its methods also have several problems, particularly with large datasets.

Q.10. What do you understand by scientific data visualization in R?

• We use visualization to communicate Data and analysis.
• The leading software for statistical analyses is the statistical programming language R.
• The leading R extension of data visualization is ggplot2.

Interview Questions For Data Science, Freshers- Q. 1,2,3,4,5,8,10

Interview Questions For Data Science, Experience- Q. 6,7,9

Q.11. Why we need data visualization in R?

Because it provides a clear understanding of patterns in data. Also, it has an ability to detect hidden structures in data.

Q.12. What do you mean by graphics devices in data visualization?

• Its functions produce output. That totally depends on the active graphics device.
• A screen is the default and more frequently device.
• R graphical devices, like the pdf device, the jpeg device, etc. The user just needs to open the graphics output device she/he wants. Hence, R takes care of producing the type of output required by the device.
• This means that to produce a certain plot on the screen or as a GIF R graphics file the R code is exactly the same. You only need to open the target output device before!
• Several devices may be open at the same time, but only one is the active device.

Q.13. What are the key elements of the statistical graphics?
Key elements of a statistical graphic:

• data
• aesthetic mappings
• geometric objects
• statistical transformations
• scales
• coordinates system
• Faceting

Read more about graphics applications in detail
Q.14. What do you understand by data visualization in R using ggplot2?

“As data visualization has different plotting system but ggplot2 is the plot which is mostly used.”
What type of data visualization in R to use for what sort of problem?
I will tell you things which helps you choose the right type of chart for your specific objectives. Also, helps how to implement it in R using ggplot2. This is primarily geared towards those who have some basic knowledge of the R programming language. And also who want to make complex and nice looking charts with R ggplot2:

• Introduction to ggplot2
• Customizing the Look and Feel

Q.15. What are Important things to remember for ggplot?

• It was developed by Hadley Wickham as an implementation of the grammar of graphics.
• Basically, ggplot is relatively complete and powerful graphics package.
• It Can do many things but not 3D.

Q.16. How to install the ggplot2 package?

• Ggplot2 can be easily installed by typing.
• install.packages(“ggplot2”)
• Although, make sure that you are using the latest version of R to get the most recent version of ggplot2.

Q.17. What are applications of ggplot2?

Aesthetics: It refers to visual attributes that affect how data are displayed in a graphic, e.g., color, point size, or line type.
Geometric objects: We use it for a visual representation of observations such as points, lines, polygons, etc.
Faceting: Generally, it is applied to the same type of graph.
Annotation: Moreover, it allows us to add text and/or external graphics to a ggplot.
Positional adjustments: it helps to reduce overplotting of points.

Q.18. Why we need ggplot?

• Generally, it is used Professionally.
• It’s very Pretty.
• Also, it is easy to manipulate.
• Although, it has great support online.
• Also, it has knowledge transfers to other packages/languages.
• It has a steep learning curve.
• Besides, it has lots of syntaxes.
• Also, it can be slow.
• Basically, it has defaulted to weird colors.

Q.19. What data Visualizations in R you should learn?
There are four basic presentation types:

• Comparison
• Composition
• Distribution
• Relationship

In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.

• Scatter Plot
• Histogram
• Bar & Stack Bar Chart
• Box Plot
• Area Chart
• Heat Map
• Correlogram

Q.20. Explain each data visualization in detail?

a. Scatter Plot
When to use:
To see the relationship between two continuous variables.
b. Histogram
When to use:
A histogram is used to plot a continuous variable. Also, It helps to break the data into bins and shows the frequency distribution of these bins. Thus, we can always change the bin size and see the effect it has on visualization.

c. Bar & Stack Bar Chart
When to use:
We use Bar charts to plot a categorical variable.
d. Box Plot
When to use:
Box Plots are used to plot a combination of categorical and continuous variables. Also, used for visualizing the spread of the data and detect outliers. Moreover, it shows five statistically significant numbers; the minimum; the 25th percentile; the median; the 75th percentile and the maximum.
e. Area Chart
When to use:
We use it to show the continuity across a variable or data set. Almost it is same as a line chart. Also, we can use it for time series plots. Alternatively, also we can use it to plot continuous variables and analyze the underlying trends.
f. Heat Map
When to use:
We use it for an intensity of colors. it is also used to display a relationship between two or three or many variables in a two-dimensional image. Thus, it allows us to explore two dimensions of the axis and the third dimension by an intensity of color.
g. Correlogram
When to use:
We use it to test the level of correlation and also among the variable available in the dataset. Thus, the cells of the matrix can be shaded or colored to show the co-relation value.

Interview Questions For Data Science, Freshers- Q. 13,14,16,17,18,19

Interview Questions For Data Science, Experience- Q. 11,12,15,20

Q.21. Explain advantages of R data Visualization?
a. Understanding
To look into the business may be more appealing. And it’s easy to understand through graphics and charts when compared to a written document comprising text and numbers. Thus can attract a wider audience. Logically, it means a far reached. Also, widespread utilization of those business insights to arrive at better decisions.
b. Efficiency
Its app allows us to display a lot of information in a small space. While the process of decision making in business is inherently complex and multifaceted, displaying evaluation findings in a graphic can allow the companies to organize lots of interrelated information in useful ways.
c. Location
Its app that uses features like geographical maps and GIS can be especially relevant for extensive businesses when a location is so often a very relevant factor. We use maps to show business insights from different places, giving an idea of the severity of issues, the reasons behind them and also the workarounds to address them.

Q.22. Explain Disadvantages of R data visualization?

a. Cost:
Its applications cost a decent sum of money, and it may not be possible for especially small companies to spend that many resources upon purchasing them. Further, dedicated professionals need to be hired for generating reports and charts from them, which may inflate the costs involved even further. Small enterprises are often working in resource-limited settings, and also getting evaluation results in a timely manner can often be of high importance.

b. Distraction:
Although at times, the Data Visualization apps create reports and charts laden with highly complicated and fancy graphics, which may be tempting for the users to focus more on form than on function. The overall value of the graphic will be minimal if we first add visual appeal. In a resource-setting, it is also important to think carefully about how resources can best be used. And also not get caught up in the graphics trend without a clear purpose.

Q.23. What do you understand by R cluster analysis?
We can consider R clustering as the most important unsupervised learning problem. Therefore, for every other problem of this kind, it has to deal with finding a structure in a collection of unlabeled data.

“It is the process of organizing objects into groups whose members are similar in some way”.

R clustering is a collection of objects. Which are “similar” to them? Also, “dissimilar” to the objects belonging to other clusters.

Q.24. What is the goal of the clustering?

To determine the intrinsic grouping in a set of unlabeled data. Although, problem is that how to decide what forms a good clustering? Moreover, It is being shown that there is no absolute “best” criterion. So it would be independent of the final aim of the clustering.
Read more about clustering in detail

Q.25. Explain the type of clustering in R?

Hard Clustering:

In this, each data point either belongs to a cluster completely or not.

Soft Clustering:

In this, we assign a probability of the data point. Although, instead of putting each data point into a separate cluster.

Q.26. What are requirements for clustering?

The main requirements that a clustering algorithm should meet are:

• Scalability;
• It must deal with different types of attributes;
• Clustering discover clusters with arbitrary shape;
• It has the ability to deal with noise and outliers;
• High dimensionality; Interpretability and usability.

Q.27. What are R applications of R clustering?

We can apply it in many fields:
Marketing: It helps in finding the groups of customers with similar behavior. Thus, provides a large database of customer data. Also, it contains the properties and past buying records.
Biology: Clustering helps in classification of plants and animals given their features.
Libraries: Helps in book order.
Insurance: clustering needs in identifying groups of motor insurance policyholders. Which is having a high average claim cost? identifying frauds.
City-planning: It helps in identifying groups of houses. Although, according to their house type, value and geographical location.
Earthquake studies: It observed earthquake epicenters to identify dangerous zones.

Q.28. What are problems with R clustering?

• There are some problems with clustering. We will discuss some problem among them:
• We face problem in addressing the requirements because of Current clustering techniques.
• Time complexity is the main reason that makes the problem.
• We can interpret the result of the clustering algorithm in different ways.

Q.29. Why is DBSCAN required?
They work well only for compact and well-separated clusters. Moreover, it is being in a notice that presence of noise and outliers affects DBSCAN.

Q.30. Name the packages that are based on density based on the algorithm?

DBSCAN: Density-based spatial clustering of applications with noise.
OPTICS/OPTICSXi: Ordering points to identify the clustering structure clustering algorithms.
HDBSCAN: Hierarchical DBSCAN with simplified hierarchy extraction.
LOF: Local outlier factor algorithm.
GLOSH: Global-Local Outlier Score from Hierarchies algorithm
Interview Questions For Data Science, Freshers- Q. 21,22,23,24,25,27
Interview Questions For Data Science, Experience- Q. 28,29,30

So, this was all in Interview Questions for Data Science. Hope you like our explanation.

3. Conclusion – Interview Questions for Data Science

As a result, in this blog, we have discussed Interview Questions For Data Science. Also, these must known Interview Questions For Data Science contains almost all important topics of data science. Moreover, this Interview Questions For Data Science will help you to crack the interview. Furthermore, if you feel any query, feel free to ask in a comment section.

Reference for Data Science