Introduction to Big Data Week 3 Quiz Answers

[ad_1]

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

Data Science 101

Q1) Which of the following are parts of the 5 P’s of data science and what is the additional P introduced in the slides?

Q2) Which of the following are part of the four main categories to acquire, access, and retrieve data?

Q3) What are the steps required for data analysis?

Investigate, Build Model, Evaluate

Classification, Regression, Analysis

Regression, Evaluate, Classification

Select Technique, Build Model, Evaluate

Q4) Of the following, which is a technique mentioned in the videos for building a model?

Q5) What is the first step in finding a right problem to tackle in data science?

Q6) What is the first step in determining a big data strategy?

Q7) According to Ilkay, why is exploring data crucial to better modeling?

enables a description of data which allows visualization.

enables understanding of general trends, correlations, and outliers.

leads to data understanding which allows an informed analysis of the data.

enables histograms and others graphs as data visualization.

Q8) Why is data science mainly about teamwork?

Analytic solutions are required.

Engineering solutions are preferred.

Exhibition of curiosity is required.

Data science requires a variety of expertise in different fields.

Q9) What are the ways to address data quality issues?

Remove data with missing values.

Generate best estimates for invalid values.

Q10) What is done to the data in the preparation stage?

Select Analytical Techniques

Identify Data Sets and Query Data

Understanding Nature of Data and Preliminary Analysis