Introduction to Big Data complete course is currently being offered by UC San Diego through Coursera platform.

Learning Outcomes for Introduction to Big Data Course!

At the end of this course, you will be able to:

* Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. 

* Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.

* Get value out of Big Data by using a 5-step process to structure your analysis. 

* Identify what are and what are not big data problems and be able to recast big data problems as data science questions.

* Provide an explanation of the architectural components and programming models used for scalable big data analysis.

* Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.

Instructors for Introduction to Big Data Course!

- Ilkay Altintas

- Amarnath Gupta

Skills You Will Gain

  • Big Data
  • Apache Hadoop
  • Mapreduce
  • Cloudera

Also Check: How to Apply for Coursera Financial Aid

Introduction to Big Data Coursera Week 1 Quiz Answers!

Why Big Data and Where Did it Come From!

Q1) Which of the following is an example of big data utilized in action today?
  • The Internet
  • Social Media
  • 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?
  • 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.
  • Access of larger storage becomes easier for everyone, which means client-facing services require very large data storage.

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

Q6) Which is the most compelling reason why mobile advertising is related to big data?
  • Mobile advertising in and of itself is always associated with 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.
  • 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, Map Data, and Social Media
  • Information Networks, Map Data, and People
  • Machine Data, Organizational Data, and People
  • Sensor Data, Organizational Data, and Social Media

Q8) What is an example of machine data?
  • Social Media
  • Weather station sensor output.
  • Sorted data from Amazon regarding customer info.

Q9) What is an example of organizational data?
  • Satellite Data
  • Social Media
  • Disease data from Center for Disease Control.

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

Q12) Where does the real value of big data often come from?
  • Size of the data.
  • Combining streams of data and analyzing them for new insights.
  • Using the three major data sources: Machines, People, and Organizations.
  • Having data-enabled decisions and actions from the insights of new data.

Q3) What does it mean for a device to be "smart"?
  • Must have a way to interact with the user.
  • Connect with other devices and have knowledge of the environment.
  • 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?
  • Accelerometers.
  • In the situation
  • The sensors used in airplanes to measure altitude.
  • Bringing the computation to the location of the data.

Q15) Which of the following are reasons mentioned for why data generated by people are hard to process?
  • Very unstructured data.
  • They cannot be modeled and stored.
  • The velocity of the data is very high.
  • Skilled people to analyze the data are hard to come by.

Q16) What is the purpose of retrieval and storage; pre-processing; and analysis in order to convert multiple data sources into valuable data?
  • To enable ETL methods.
  • Designed to work like the ETL process.
  • To allow scalable analytical solutions to big data.
  • Since the multi-layered process is built into the Neo4j database connection.

Q17) Which of the following are benefits for organization generated data?
  • Higher Sales
  • High Velocity
  • Improved Safety
  • Better Profit Margins
  • Customer Satisfaction

Q18) What are data silos and why are they bad?
  • Highly unstructured data. Bad because it does not provide meaningful results for organizations.
  • 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.

Q19) Which of the following are benefits of data integration?
  • Monitoring of data.
  • Adds value to big data.
  • Increase data availability.
  • Unify your data system.
  • Reduce data complexity.
  • Increase data collaboration.

Post a Comment

Previous Post Next Post