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?

• 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:

headers_list=['A','B','C']

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”)
• df.info

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?

avg=df['horsepower'].mean(axis=0)
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