Big Data Application & Analysis Link Analysis 1. Compute the PageRank of each page in the figure below, assuming no taxation answer only u r sure
Q: When it comes to big data, what types of difficulties do clients often encounter
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Q: Five V’s plays important role in big data management”. Justify statement with suitable examples.
A: Overview : In today's age, there are constant streams of high-volume real-time data flowing from…
Q: While there are three key components that must be in place for an organization to get real value…
A: While there are three key components that must be in place for an organization to get real value…
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A: Here are the ways that show a company's data differ from other businesses- Product differentiation-…
Q: FIGURE 6.12 CONTEMPORARY BUSINESS INTELLIGENCE INFRASTRUCTURE Operational Data Data Mart Extract,…
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A: Big data management Big data management is a board concept that will encompasses the policies,…
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A: Data mining method includes business understanding, information Understanding, information…
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A: Data mining The process of evaluating data from various angles and distilling it into valuable…
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A: The financial companies' stock market rates keep modifying or changing every second. For this, the…
Q: Use examples to compare and contrast unstructured and structured data. Which type is more prevalent…
A: Unstructured data It is the information or data that do not have any meaning or logic. This results…
Q: The difference between EDA and hypothesis testing, and why analysts may prefer EDA when doing data…
A: The, answer has given below:
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A: This question explains about the users of data warehouse and basics of categorization that which is…
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A: Structured Data Unstructured Data It can be easily understood by machines compared to machines it…
Q: Write a paper on the following topic. "Big data modeling and frameworks for smart cities"
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Q: Use examples to compare and contrast unstructured and structured data. Which type is more prevalent…
A: Unstructured Data: Unstructured data is the information or data that doesn’t have any meaning or…
Q: Use 4 examples drawn from the case study above to illustrate each of the 4 Vs of Big Data.
A: Big data is the collective name for a large amount of registered digital data and the equal growth…
Q: The primary characteristics of big data are speed and diversity. In practise, how do each of these…
A: Introduction: Big data has two characteristics: velocity and diversity. In practise, how do each of…
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A: Defining the terms: Simple key A simple key refers to a "single (or only one) attribute" that is…
Q: Consider the three major Vs as Volume, Velocity and Variety along with other Big Data contexts.…
A: Actually, big data is also a data but huge size.
Q: What is the core component of data management; it defines the roles, responsibilities, and processes…
A: b. Data governance
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Q: Data mining analysts favor EDA over hypotheses because of the mismatch between the two
A: Data mining analysts favor EDA over hypotheses because of the mismatch between the two.
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A: We're looking at the healthcare industry because it generates a lot of data and is primarily driven…
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A: Introduction: A hypothesis analysis would include the specifics of the investigation that is…
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A: Veracity: It refers to the trustworthiness and accuracy of the information. and, if the available…
Q: Discussion Questions Question 1: Identify three companies that have been using big data. Explain how…
A: Introduction: Big data is a collection of data that is huge in volume, yet growing exponentially…
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Q: 9. Which of the following statement is a valid criticism of the big data approach to investment…
A: Explanation: Big-data is indeed a discipline that deals with methods for analyzing, methodically…
Q: hen it comes to data mining, why may analysts choose EDA over hypothesis testing?
A: EDA: It is the process of analyzing a dataset to detect patterns and outliers and produce hypotheses…
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A: Big data can be defined in such a way that it is a combination of systematic, informal, and informal…
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A: Given To know about the speed and variety properties of big data.
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A: Data Type Images, audio, video , etc. Unstructured data Emails Unstructured data Tables in…
Q: There are two properties of big data: velocity and diversity. What does each of these attributes…
A: Introduction: Big data is defined as data that is so massive, quick, or complicated that processing…
Q: Make use of examples to compare and contrast unstructured and structured data. Which kind is more…
A: The Answer
Q: 2. Explain 6 steps in data life cycle
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Q: Example of a venue in a big data characteristics.
A: Example of a venue in a big data characteristics. In below step.
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A: Introduction: Mercy is a health-care organization with 46 acute-care and specialty hospitals, as…
Q: Identify and then discuss two different data mining methodologies
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Q: If you want to compare structured and unstructured data, utilise examples to back up your…
A: Information that is stored in a fixed field inside of a document or record is referred to as…
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Big Data Application & Analysis
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1. Compute the PageRank of each page in the figure below, assuming no taxation
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- Db&__Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions:1. Retrieve all rows in Airport table for all the airports in London city.2. Retrieve all British and German passengers.3. Retrieve all names of all the passengers...Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…
- Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column. Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them to your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification ModelsSupport vector machine classifier with…Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…_&plase helo with Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions:1. Retrieve all rows in Airport table for all the airports in London city.2. Retrieve all British and German passengers.3. Retrieve all names of all the passengers..
- Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions: Transaction SQL Statement Retrieve the flight number, departure and arrival airports of all British Airways. Retrieve the name of every passenger together with their flight number and the associated company. Retrieve all flights departed from all airports in London. Retrieve the ticket numbers…Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions:1. Retrieve all rows in Airport table for all the airports in London city.2. Retrieve all British and German passengers.3. Retrieve all names of all the passengers.4. Retrieve the flight number, departure and arrival airports of all British Airways.5. Retrieve the name of every passenger together with their…Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions: Transaction SQL Statement Retrieve all rows in Airport table for all the airports in London city. Retrieve all British and German passengers. Retrieve all names of all the passengers. Retrieve the flight number, departure and arrival airports of all British Airways. Retrieve the name of…
- Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions: please if you could give all the sql statements needed i will appreciate that, and i promise to put thump up Transaction SQL Statement Retrieve all rows in Airport table for all the airports in London city. Retrieve all British and German passengers. Retrieve all names of all the passengers.…Unique Answer only Based on the dataset information given above, please answer the following questions. You are required to perform the following tasks using R Studio. Please provide the script and screenshot of the output. Q. If you want to see the 1st Quartile, Median, and 3rd Quartile of DiabetesPedigreeFunction, which plot is suitable to visualize these three pieces of information? Visualize the plot with an appropriate header and label.As per our guidelines, we are supposed to answer only 1st three parts. Kindly repost the remaining questions separately. 4.1 use gym_29876543 4.2 db.createCollection("userProfiles") db.userProfiles.insertMany([ {"Name" : "Dominic","Surname":"Badeaux","Date_of_Birth":1982-09-04,"FitnessLevel":1}, {"Name" : "John","Surname":"Dlamini","Date_of_Birth":1974-05-18,"FitnessLevel":2} ]) 4.3 db.userProfiles.find() arrow_forward Step 2 Complete code: use gym_29876543 db.createCollection("userProfiles") db.userProfiles.insertMany([ {"Name" : "Dominic","Surname":"Badeaux","Date_of_Birth":1982-09-04,"FitnessLevel":1}, {"Name" : "John","Surname":"Dlamini","Date_of_Birth":1974-05-18,"FitnessLevel":2} ]) db.userProfiles.find() Q.4.4 Query all the user profiles where the fitness level of the user is equal to 2. Q.4.5 Query all the user profiles where the date of birth of the user is from 1980‐01‐01 to1982‐12‐31 (including both dates)
