" } }, { "@type": "Question", "name": "What is the difference between Data Analytics, Big Data, and Data Science?", "acceptedAnswer": { "@type": "Answer", "text": "

Big Data: Big Data deals with huge data volume in structured and semi structured form and require just basic knowledge of mathematics and statistics.

Data Analytics: Data Analytics provide the operational insights of complex scenarios of business.

Data Science: Data Science deals with slicing and dicing of data and require deep knowledge of mathematics and statistics.

" } }, { "@type": "Question", "name": "Which language R or Python is most suitable for text analytics?", "acceptedAnswer": { "@type": "Answer", "text": "

As Python consists of a rich library of Pandas, due to which the analysts can use high-level data analysis tools and data structures, this feature is absent in R, so Python is more suitable for text analytics.

" } }, { "@type": "Question", "name": "Explain Recommender System.", "acceptedAnswer": { "@type": "Answer", "text": "

The recommended system works on the basis of past behavior of the person and is widely deployed in a number of fields like music preferences, movie recommendations, research articles, social tags and search queries. With this system, the future model can also be prepared, which can predict the person’s future behavior and can be used to know the product the person would prefer buying or which movie he will view or which book he will read. It uses the discrete characteristics of the items to recommend any additional item.

" } }, { "@type": "Question", "name": "What are the benefits of R language?", "acceptedAnswer": { "@type": "Answer", "text": "

R programming uses a number of software suites for statistical computing, graphical representation, data calculation and manipulation. Following are a few characteristics of R programming:

It has an extensive tool collection

Tools have the operators to perform Matrix operations and calculations using arrays

Analysing techniques using graphical representation

It is a language with many effective features but is simple as well

It supports machine learning applications

It acts as a connecting link between a number of data sets, tools and software

It can be used to solve data oriented problem

" } }, { "@type": "Question", "name": "How is statistics used by Data Scientists?", "acceptedAnswer": { "@type": "Answer", "text": "

With the help of statistics, the Data Scientists can convert the huge amount of data to provide its insights. The data insights can provide a better idea of what the customers are expecting? With the help of statistics, the Data scientists can know the customer’s behavior, his engagements, interests and final conversion. They can make powerful predictions and certain inferences. It can also be converted into powerful propositions of business and the customers can also be offered suitable deals.

" } }, { "@type": "Question", "name": "What is the importance of data cleansing in data analysis?", "acceptedAnswer": { "@type": "Answer", "text": "

As the data come from various multiple sources, so it becomes important to extract useful and relevant data and therefore data cleansing become very important. Data cleansing is basically the process of correcting and detecting accurate and relevant data components and deletion of the irrelevant one. For data cleansing, the data is processed concurrently or in batches.

Data cleansing is one of the important and essential steps for data science, as the data can be prone to errors due to a number of reasons, including human negligence. It takes a lot of time and effort to cleanse the data, as it comes from various sources.

" } }, { "@type": "Question", "name": "In real world scenario, how the machine learning is deployed?", "acceptedAnswer": { "@type": "Answer", "text": "

The real world applications of machine learning include:

Finance: To evaluate risks, investment opportunities and in the detection of fraud

Robotics: To handle the non ordinary situations

Search Engine: To rank the pages as per the user’s personal preferences

Information Extraction: To frame the possible questions to extract the answers from database

E-commerce: To deploy targeted advertising, re-marketing and customer churn

" } }, { "@type": "Question", "name": "What is Linear Regression?", "acceptedAnswer": { "@type": "Answer", "text": "

Linear regression is basically used for predictive analysis. This method describes the relationship between dependent and independent variables. In linear regression, a single line is fitted within a scatter plot. It consists of the following three methods:

Analyzing and determining the direction and correlation of the data

Deployment of estimation model

To ensure the validity and usefulness of the model. It also helps to determine the outcomes of various events

" } }, { "@type": "Question", "name": "Explain K-means algorithm.", "acceptedAnswer": { "@type": "Answer", "text": "

K-Means is a basic an unsupervised learning algorithm and uses data clusters, known as K-clusters to classify the data. The data similarity is identified by grouping the data. The K centers are defined in each K cluster. Using K clusters the K groups are formed and K is performed. The objects are assigned to their nearest cluster center. All objects of the same cluster are related to each other and different from the objects of other clusters. This algorithm is the best for large sets of data.

" } } ] }

Data Science Interview Questions

What is Data Science?

What is the difference between Data Analytics, Big Data, and Data Science?

Which language R or Python is most suitable for text analytics?

Explain Recommender System.

What are the benefits of R language?

How is statistics used by Data Scientists?

What is the importance of data cleansing in data analysis?

In real world scenario, how the machine learning is deployed?

What is Linear Regression?

Explain K-means algorithm.

Data Science Interview Questions & Answers

Q1). What is Data Science?

Data Science is a combination or mix of mathematical and technical skill, which may require business vision as well. These skills are used to predict the future trend and analyzing the data.

Q2). What is the difference between Data Analytics, Big Data, and Data Science?

Big Data: Big Data deals with huge data volume in structured and semi structured form and require just basic knowledge of mathematics and statistics. Data Analytics: Data Analytics provide the operational insights of complex scenarios of business Data Science: Data Science deals with slicing and dicing of data and require deep knowledge of mathematics and statistics

Q3). Which language R or Python is most suitable for text analytics?

As Python consists of a rich library of Pandas, due to which the analysts can use high-level data analysis tools and data structures, this feature is absent in R, so Python is more suitable for text analytics.

Q4). Explain Recommender System.

The recommended system works on the basis of past behavior of the person and is widely deployed in a number of fields like music preferences, movie recommendations, research articles, social tags and search queries. With this system, the future model can also be prepared, which can predict the person’s future behavior and can be used to know the product the person would prefer buying or which movie he will view or which book he will read. It uses the discrete characteristics of the items to recommend any additional item.

Q5). What are the benefits of R language?

R programming uses a number of software suites for statistical computing, graphical representation, data calculation and manipulation. Following are a few characteristics of R programming:

It has an extensive tool collection

Tools have the operators to perform Matrix operations and calculations using arrays

Analysing techniques using graphical representation

It is a language with many effective features but is simple as well

It supports machine learning applications

It acts as a connecting link between a number of data sets, tools and software

It can be used to solve data oriented problem

Q6). How is statistics used by Data Scientists?

With the help of statistics, the Data Scientists can convert the huge amount of data to provide its insights. The data insights can provide a better idea of what the customers are expecting? With the help of statistics, the Data scientists can know the customer’s behavior, his engagements, interests and final conversion. They can make powerful predictions and certain inferences. It can also be converted into powerful propositions of business and the customers can also be offered suitable deals.

Q7). What is the importance of data cleansing in data analysis?

As the data come from various multiple sources, so it becomes important to extract useful and relevant data and therefore data cleansing become very important. Data cleansing is basically the process of correcting and detecting accurate and relevant data components and deletion of the irrelevant one. For data cleansing, the data is processed concurrently or in batches.

Data cleansing is one of the important and essential steps for data science, as the data can be prone to errors due to a number of reasons, including human negligence. It takes a lot of time and effort to cleanse the data, as it comes from various sources.

Q8). In real world scenario, how the machine learning is deployed?

The real world applications of machine learning include:

Finance: To evaluate risks, investment opportunities and in the detection of fraud

To evaluate risks, investment opportunities and in the detection of fraud Robotics : To handle the non ordinary situations

: To handle the non ordinary situations Search Engine : To rank the pages as per the user’s personal preferences

: To rank the pages as per the user’s personal preferences Information Extraction : To frame the possible questions to extract the answers from database

: To frame the possible questions to extract the answers from database E-commerce: To deploy targeted advertising, re-marketing and customer churn

Q9). What is Linear Regression?

Linear regression is basically used for predictive analysis. This method describes the relationship between dependent and independent variables. In linear regression, a single line is fitted within a scatter plot. It consists of the following three methods:

Analyzing and determining the direction and correlation of the data

Deployment of estimation model

To ensure the validity and usefulness of the model. It also helps to determine the outcomes of various events

Q10). Explain K-means algorithm.

K-Means is a basic an unsupervised learning algorithm and uses data clusters, known as K-clusters to classify the data. The data similarity is identified by grouping the data. The K centers are defined in each K cluster. Using K clusters the K groups are formed and K is performed. The objects are assigned to their nearest cluster center. All objects of the same cluster are related to each other and different from the objects of other clusters. This algorithm is the best for large sets of data.



