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Apr" } }, { "@type": "Question", "name": "What is the difference between Data Analytics, Big Data, and Data Science?", "acceptedAnswer": { "@type": "Answer", "text": "
" } }, { "@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:
" } }, { "@type": "Question", "name": "How is statistics used by Data Scientists?", "acceptedAnswer": { "@type": "Answer", "text": "
Read: How To Write A Resume Of An Entry Level Data Scientist?
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:
" } }, { "@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:
" } }, { "@type": "Question", "name": "Explain K-means algorithm.", "acceptedAnswer": { "@type": "Answer", "text": "
Read: Top 5 Python Automation Testing Frameworks to Practice in 2020
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 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.
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.
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.
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:
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.
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.
Read: What Exactly Does a Data Scientist Do?
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.
The real world applications of machine learning include:
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:
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.
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