The Data Scientist’s Toolbox Coursera Answer 2020

The Data Scientist’s Toolbox Coursera Answer 2020

The Data Scientist’s Toolbox – Coursera

⭐4.6 Stars (28,873 ratings)  

Instructor: Jeff Leek

Enroll Now

In this The Data Scientist’s Toolbox course you will get an introduction  to the fundamental devices and thoughts in the information researcher’s tool stash. The course gives a review of the information, questions, and tools that data analyst  and data scientist  work with. There are two segments to this course. The first is a calculated introduction  to the thoughts behind transforming information into noteworthy information. The second is a pragmatic introduction  to the apparatuses that will be utilized in the program like adaptation control, markdown, git, GitHub, R, and RStudio.
Here you will find all the questions and quiz answers relater to “The Data Scientist’s Toolbox By Coursera” 

N.B. We endeavored our best to keep this site invigorated for our customers in vain. You can similarly contribute by reviving new requests or existing request answer(s). There are various requests on our site, it is hard for us to check them reliably. It will be exceptional if you can help us with refreshing the site. Simply let us know whether you locate any new inquiries through mail or remark . We will endeavor to invigorate the request/answer ASAP.


The Data Scientist’s Toolbox Coursera Quiz Answer


Week-1 (Data Science  Fundamentals)

Practice Quiz-1


1.Which of the following is an example of structured data?

  • A database of individual’s addresses, phone numbers, and post codes
  • YouTube video transcript
  • Satellite imagery of weather patterns

1.Which of the following is an example of structured data?

  • Lung x-ray images of smokers
  • A database of individual’s addresses, phone numbers, and post codes
  • A compilation of cat videos


7 V’s Of Big Data

1

Velocity 

2

Variety 

3

Variability 

4

Veracity 

5

Volume

6

Visualisation 

7

Value

2. Which is NOT one of the three V’s of Big Data?

  • volume, vibrant, vital
  • vast, versatile, vital
  • visible, variety, vast

2. Which is NOT one of the three V’s of Big Data?

  • vexing, visible, vast
  • variety, velocity, vexing
  • velocity, variety, volume

3.Which of these is NOT one of the main skills embodied by data scientists?

  • Machine learning
  • Hacking skills
  • Math and stats

3. Which of these is NOT one of the main skills embodied by data scientists?

  • Hacking skills
  • Artificial intelligence
  • Math and stats


Practice Quiz-2

1.Which of these is an example of a quantitative variable?

  • clothing brand, genre, birthplace
  • occupation, height, gender
  • age, weight, height

1. Which of these is an example of a quantitative variable?

  • latitude, age, weight
  • height, birthplace, gender
  • genre, occupation, age


2. Quantitative variables are measured on ordered, continuous scales.

  • True
  • False


3. What is the most important thing in Data Science?


  • Knowing Hadoop and Pig
  • The question you are trying to answer
  • Machine learning and prediction

3. What is the most important thing in Data Science?

  • The data
  • Working with large data sets
  • The question you are trying to answer

Practice Quiz-3

 1. Which of these might be a good title for a forum post?

  • How do I get rnorm() to work?
  • STAT101 quiz
  • Removing rows with NAs in data.frame using subset(), R 3.4.3

2. Which is a characteristic of a good question when posting to message boards?

  • Assumes that you’ve discovered a bug in R
  • Includes follow-ups if you answer your own question
  • Asks for a code fix without explanation


3. Which is NOT a good strategy for finding the answer to a problem in this course?

  • Explaining your problem to a friend/coworker
  • Searching the course forum
  • Googling the error message

3. Which is NOT a good strategy for finding the answer to a problem in this course?
  • Searching the course forum
  • Emailing the professor
  • Explaining your problem to a friend/coworker

Practice Quiz-4


Will be added later


       Quiz-1      

Module One Summative Quiz

1. Which of these is NOT one of the main skills embodied by data scientists?

  • Access to large data sets
  • Hacking skills
  • Substantive expertise

2. What is the most important thing in Data Science?

  • The question you are trying to answer
  • Statistical inference
  • Working with large data sets


3. Which of these might be a good title for a forum post?

  • URGENT! R isn’t working!
  • Removing rows with NAs in data.frame using subset(), R 3.4.3
  • How do I get rnorm() to work?


4. What’s the first step in the data science process?

  • Communicate your findings
  • Exploring the data
  • Generating the question


5.Which of these is an example of a quantitative variable?

  • Latitude
  • Occupation
  • Educational level


Week- 2 (R and RStudio)

Practice Quiz-1

Will be added later

Practice Quiz-2

Will be added later

Practice Quiz-3

Will be added later

Practice Quiz-4

Will be added later

       Quiz-2      

Module Two Summative Quiz

1. What does base R focus on?
  • Mapping
  • Statistical analysis
  • Artificial intelligence

2.What is RStudio?
  • A graphical user interface for R
  • Version control software
  • A programming language
3.What is the name of the quadrant in the bottom left corner of RStudio, in the default layout?
  • History
  • Plots
  • Console

4.What command lists your R version, operating system, and loaded packages?
  • versions()
  • Sessioninfo()
  • sessionInfo()

5. What file extension do Projects in R use?
  • .Rproj
  • .R
  • .RPROJECT

Week-3 (Version Control and GitHub)

Practice Quiz-1

Will be added later

Practice Quiz-2

Will be added later

Practice Quiz-3

Will be added later

Practice Quiz-4

Will be added later

       Quiz-3      

1.What is a good example of a message to accompany a commit?
  • Modified linear model of height to include new covariate, genotype
  • Fixed problem with linear model
  • Updated thing
2. On each repository page in GitHub, in the top right hand corner there are three options. They are:
  • Watch, star, fork
  • Pull, clone, fork
  • Commit, contributors, issues

3. Which of the following will initiate a git repository locally?
  • git init
  • git remote add
  • git boom
4. What is the order of commands to send a file to GitHub from within RStudio?
  • Commit > Push
  • Stage > Commit message > Commit > Push
  • Pull > Push > Commit

5. How do you add all of the contents of directory to version control?
  • git add .
  • cd ~/dir/name/of/path/to/file
  • git commit -m “Message”

Week-4 (R Markdown, Scientific Thinking, and Big Data)

Practice Quiz-1

Will be added later

Practice Quiz-2

Will be added later

Practice Quiz-3

Will be added later

Practice Quiz-4

Will be added later

       Quiz-4      


1.What is the format for including a link that appears as blue text in your markdown document?


2. Which of the following describes a predictive analysis?

  • Using data collected in the past to predict values in the future
  • Finding if one variable is related to another one
  • Showing the effect on a variable of changing the values of another variable

3. We collect data on all the songs in the Spotify catalogue and want to summarize how many are country western, hip-hop, classic rock, or other. What type of analysis is this?

  • Exploratory
  • Descriptive
  • Predictive


4. What might a confounder be in an experiment looking at the relationship between the prevalence of white hair in a population and wrinkles?

  • Age
  • Socioeconomic status
  • Sex


5. Which one of the following is an example of structured data?

  • The text from a series of books
  • Lung x-ray images
  • A table of names and student grades

The above questions are from “The Data Scientist’s Toolbox “. You can discover all the refreshed questions and answers related to this on the “The Data Scientist’s Toolbox By Coursera” page. If you find the updated questions or answers, do comment on this page and let us know. We will update the answers as soon as possible.






Leave a Comment