Data Analysis with Python Coursera Quiz Answers – 100% Correct Answers

All Weeks Data Analysis with Python Coursera Quiz Answers

Week 01: Importing Datasets

Practice Quiz: Understanding the Data

Q1. Each column contains a:

  • attribute or feature
  • different used car

Q2. How many columns does the dataset have?

  • 26
  • 205

Practice Quiz: Python Packages for Data Science

Q1. What description best describes the library Pandas?

  • Includes functions for some advanced math problems as listed in the slide as well as data visualization.
  • Uses arrays as their inputs and outputs. It can be extended to objects for matrices, and with a little change of coding, developers perform fast array processing.
  • Offers data structure and tools for effective data manipulation and analysis. It provides fast access to structured data. The primary instrument of Pandas is a two-dimensional table consisting of columns and rows labels which are called a DataFrame. It is designed to provide an easy indexing function.

Q2. What is a Python library?

  • A file that contains data.
  • A collection of functions and methods that allows you to perform lots of actions without writing your code.

Practice Quiz: Importing and Exporting Data in Python

Q1. What does the following method do to the data frame? df : df.head(12)1 point

  • Show the first 12 rows of dataframe.
  • Shows the bottom 12 rows of dataframe.

Q2. What task does the following lines of code perform?

path=’C:\Windows\…\ automobile.csv’


  • Exports your Pandas dataframe to a new csv file, in the location specified by the variable path.
  • Loads a csv file.

Practice Quiz: Getting Started Analyzing Data in Python

Q1. To enable a summary of all the columns, what must the parameter include be set to for the method described?

  • df.describe(include=“all”) 
  • df.describe(include=“None”) 

Graded Quiz: Importing Datasets

Q1. What do we want to predict from the dataset?

  • price
  • colour
  • make

Q2. What library is primarily used for machine learning

  • scikit-learn
  • Python
  • matplotlib

Q3. We have the list headers_list:


We also have the data frame df that contains three columns, what is the correct syntax to replace the headers of the data frame df with values in the list headers_list?

  • df.columns = headers_list
  • df.head()
  • df.tail()

Q4. What attribute or method will give you the data type of each column?

  • describe()
  • columns
  • dtypes

Q5. How would you generate descriptive statistics for all the columns for the data frame df?

  • df.describe()
  • df.describe(include = “all”)

Practice Quiz: Dealing with Missing Values in Python

Q1. How would you access the column ”body-style” from the data frame df?

  • df[ “body-style”] 
  • df==”bodystyle”

Q2. What is the correct symbol for missing data?

  • nan
  • no-data

Practice Quiz: Data Formatting in Python

Q1. How would you rename the column “city_mpg” to “city-L/100km”?

  • df.rename(columns={”city_mpg”: “city-L/100km”}, inplace=True)
  • df.rename(columns={”city_mpg”: “city-L/100km”})

Practice Quiz: Data Normalization in Python

Q1. Which of the following is the correct formula for z -score or data standardization?

Q2. What is the maximum value for feature scaling?

  • 1

Practice Quiz: Turning categorical variables into quantitative variables in Python

Q1. Consider the column ‘diesel’; what should the value for Car B be?

  • 1

Graded Quiz: Data Wrangling

Q1. What task do the following lines of code perform?

df['horsepower'].replace(np.nan, avg)
  • calculate the mean value for the ‘horsepower’ column and replace all the NaN values of that column by the mean value
  • nothing; because the parameter inplace is not set to true
  • replace all the NaN values with the mean

Q2. Consider the dataframe df; convert the column df[“city-mpg”] to df[“city-L/100km’] by dividing 235 by each element in the column ‘city-mpg’.

Q3. What data type is the following set of numbers? 666, 1.1,232,23.12

Q4. Consider the two columns ‘horsepower’, and ‘horsepower-binned’; from the data frame df; how many categories are there in the ‘horsepower-binned’ column?

  • 3

Week 3 – Practice Quiz: Descriptive Statistics

Q1. Consider the following scatter plot; what kind of relationship do the two variables have?

  • positive linear relationship
  • negative linear relationship

Q2. Which of the following tables representing a number of drive wheels, body style, and the price is a Pivot Table?

Graded Quiz: Exploratory Data Analysis

Q1. Consider the dataframe df; what method provides the summary statistics?

  • describe()
  • head()
  • tail()

Q2. If we have 10 columns and 100 samples, how large is the output of df.corr()?

  • 10 x 100
  • 10×10
  • 100×100

Q3. If the p-value of the Pearson Correlation is 1, then …

  • The variables are correlated
  • The variables are not correlated
  • None of the above

Q4. Consider the following dataframe:

1df_test = df[['body-style', 'price']]

The following operation is applied:

1df_grp = df_test.groupby(['body-style'], as_index=False).mean()

What are the resulting values of: df_grp[‘price’]?

  • The average price for each body style
  • The average price
  • The average body style

Q5. What is the Pearson Correlation between variables X and Y, if X=-Y?

  • -1
  • 1
  • 0
All Quiz Answers of multiple Specializations or Professional Certificates programs:

Course 1: What is Data Science?

Course 2: Tools for Data Science

Course 3: Data Science Methodology

Course 4: Python for Data Science, AI & Development

Course 5: Python Project for Data Science

Course 6: Databases and SQL for Data Science with Python

Course 7: Data Analysis with Python

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