Big Data Specialization: Introduction to Big Data Quiz 1 | Coursera

Introduction to Big Data Quiz 1 Answers

 

Big Data Scope 

Big Data is an important field that encompasses a range of job titles and positions. Here are some of the most common positions related to Big Data and their probable earnings:

• Data Analyst: Data analysts are responsible for collecting, analyzing, and interpreting large sets of data to help organizations make better decisions. The average salary for a data analyst is around $65,000 to $85,000 per year.
• Data Scientist: Data scientists are responsible for designing and implementing advanced algorithms and statistical models to analyze and interpret data. The average salary for a data scientist is around $100,000 to $130,000 per year.
• Big Data Engineer: Big Data engineers are responsible for designing, building, and maintaining the infrastructure that supports the storage and analysis of large sets of data. The average salary for a Big Data engineer is around $110,000 to $140,000 per year.
• Business Intelligence Analyst: Business intelligence analysts are responsible for analyzing and interpreting data to help organizations make better business decisions. The average salary for a business intelligence analyst is around $80,000 to $100,000 per year.
• Data Architect: Data architects are responsible for designing and implementing the systems and processes that support the storage and analysis of large sets of data. The average salary for a data architect is around $120,000 to $150,000 per year.
• Chief Data Officer (CDO): CDOs are responsible for overseeing an organization's overall data strategy and ensuring that data is effectively collected, stored, and analyzed to support the organization's goals. The average salary for a CDO is around $200,000 to $300,000 per year.

It's important to note that these salaries are estimates and can vary based on factors such as industry, company size, location, and level of experience. Additionally, the demand for Big Data professionals is expected to continue to grow as organizations increasingly rely on data to drive their decision-making processes.

About the Course:

Would you want to learn more about the Big Data environment? This course is intended for people who are new to data science and are curious about the origins of the Big Data Era. It is intended for people who wish to become familiar with the terminology and core concepts of big data problems, applications, and systems. It is intended for anyone who wish to begin considering how Big Data could be advantageous for their business or profession. It gives an overview of one of the most popular frameworks, Hadoop, which has made big data analysis simpler and more accessible, increasing the probability that data will transform the world!

After completing this course, you'll be able to:

• Explain the three main sources of big data: people, organisations, and sensors, as well as instances of real-world big data difficulties.
• Describe the impact of the "Big Data V's" (volume, velocity, variety, veracity, valence, and value) on data collection, monitoring, storage, analysis, and reporting.
• Structure your analysis using a 5-step approach to maximise the usefulness of big data.
• Recognize which issues are big data challenges and which are not, and be able to rephrase large data issues as data science inquiries.
• Describe the architectural elements and programming paradigms employed for scalable big data analysis.
• Outline the attributes and features of the key elements of the Hadoop stack, such as the YARN resource and task management system, the HDFS file system, and the MapReduce programming paradigm.
• Install and run a Hadoop program!

Newcomers to data science should take this course. Although no prior programming knowledge is required, it is necessary to install software and use a virtual machine in order to complete the practical tasks.

Why Big Data and Where Did it Come From

Q1) Which of the following is an example of big data utilized in action today?

• Social Media
• The Internet
• Wi-Fi Networks
• Individual, Unconnected Hospital Databases

  

Q2) What reasoning was given for the following: why is the "data storage to price ratio" relevant to big data?

• Access of larger storage becomes easier for everyone, which means client-facing services require very large data storage.
• It isn't, it was just an arbitrary example on big data usage.
• Larger storage means easier accessibility to big data for every user because it allows users to download in bulk.
• Companies can't afford to own, maintain, and spend the energy to support large data storage unless the cost is sufficiently low.

  

Q3) What is the best description of personalized marketing enabled by big data?

• Being able to use the data from each customer for marketing needs.
• Marketing to each customer on an individual level and suiting to their needs.
• Being able to obtain and use customer information for specific groups and utilize them for marketing needs.

  

Q4) Of the following, which are some examples of personalized marketing related to big data?

• Facebook revealing posts that cater towards similar interests.
• A survey that asks your age and markets to you a specific brand.
• News outlets gathering information from the internet in order to report them to the public.

  

Q5) What is the workflow for working with big data?

• Big Data -> Better Models -> Higher Precision
• Extrapolation -> Understanding -> Reproducing
• Theory -> Models -> Precise Advice

  

Q6) Which is the most compelling reason why mobile advertising is related to big data?

• Mobile advertising benefits from data integration with location which requires big data.
• Mobile advertising allows massive cellular/mobile texting to a wide audience, thus providing large amounts of data.
• Mobile advertising in and of itself is always associated with big data.
• Since almost everyone owns a cell/mobile phone, the mobile advertising market is large and thus requires big data to contain all the information.

  

Q7) What are the three types of diverse data sources?

• Machine Data, Organizational Data, and People
• Sensor Data, Organizational Data, and Social Media
• Machine Data, Map Data, and Social Media
• Information Networks, Map Data, and People

  

Q8) What is an example of machine data?

• Weather station sensor output.
• Social Media
• Sorted data from Amazon regarding customer info.

  

Q9) What is an example of organizational data?

• Disease data from Center for Disease Control.
• Satellite Data
• Social Media

  

Q10) Of the three data sources, which is the hardest to implement and streamline into a model?

• People
• Machine Data
• Organizational Data

  

Q11) Which of the following summarizes the process of using data streams?

• Integration -> Personalization -> Precision
• Big Data -> Better Models -> Higher Precision
• Theory -> Models -> Precise Advice
• Extrapolation -> Understanding -> Reproducing

  

Q12) Where does the real value of big data often come from?

• Combining streams of data and analyzing them for new insights.
• Using the three major data sources: Machines, People, and Organizations.
• Size of the data.
• Having data-enabled decisions and actions from the insights of new data.

  

Q3) What does it mean for a device to be "smart"?

• Connect with other devices and have knowledge of the environment.
• Must have a way to interact with the user.
• Having a specific processing speed in order to keep up with the demands of data processing.

  

Q14) What does the term "in situ" mean in the context of big data?

• Bringing the computation to the location of the data.
• Accelerometers.
• In the situation
• The sensors used in airplanes to measure altitude.

  

Q15) Which of the following are reasons mentioned for why data generated by people are hard to process?

• Very unstructured data.
• The velocity of the data is very high.
• Skilled people to analyze the data are hard to come by.
• They cannot be modeled and stored.

  

Q16) What is the purpose of retrieval and storage; pre-processing; and analysis in order to convert multiple data sources into valuable data?

• To allow scalable analytical solutions to big data.
• Since the multi-layered process is built into the Neo4j database connection.
• To enable ETL methods.
• Designed to work like the ETL process.

  

Q17) Which of the following are benefits for organization generated data?

• Higher Sales
• Improved Safety
• Better Profit Margins
• Customer Satisfaction
• High Velocity

  

Q18) What are data silos and why are they bad?

• Data produced from an organization that is spread out. Bad because it creates unsynchronized and invisible data.
• A giant centralized database to house all the data production within an organization. Bad because it hinders opportunity for data generation.
• A giant centralized database to house all the data produces within an organization. Bad because it is hard to maintain as highly structured data.
• Highly unstructured data. Bad because it does not provide meaningful results for organizations.

 

Q19) Which of the following are benefits of data integration?

• Adds value to big data.
• Increase data availability.
• Unify your data system.
• Reduce data complexity.
• Increase data collaboration.
• Monitoring of data.

 

Conclusion

 

With any luck, this post will help you quickly and easily uncover assessment answers for Coursera's Introduction to Big Data Quiz. If this article has been helpful to you in any way, please let your friends and family know on social media about this wonderful training. Be patient with us as we release a tonne more free courses along with the exam/quiz solutions, and keep checking our QueHelp Blog for updates.