Here you will find the Data Analysis with Python Coursera course All Quiz Answers. Data science is relatively a new field that deals with the study of data. In data science, a large amount of data is studied to extract meaningful insights for business. Data science is a multidisciplinary approach that combines principles and practices from the fields of computer science, mathematics, statistics, and artificial intelligence.

This analysis of data helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the finding results.

## Data Analysis with Python Coursera Course Quiz Answers

**Week 01 Practice Quiz: Importing Datasets**

**Topic: Understanding the Data**

Q1. Each column contains a:

**attribute or feature**- different used car

Q2. How many columns does the dataset have?

**26**- 205

**Topic: 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.**

**Topic: 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’

df.to_csv(path)

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

**Topic: 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”)

**Week 01 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

**Week 02 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

**Topic: 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”})

**Topic: 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**

**Topic: Turning categorical variables into quantitative variables in Python**

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

**1**

**Week 02 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 03 Practice Quiz: Descriptive Statistics**

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

**positive linear relation**ship- negative linear relationship

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

**Topic: 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