Hello Learners, Today, we are going to share Data Analysis with Python Cognitive Class Course Exam Answer launched by IBM. This certification course is totally free of cost✅✅✅ for you and available on Cognitive Class platform.
Here, you will find Data Analysis with Python Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct✅ answers of all modules and final exam answers of Data Analysis with Python from Cognitive Class Certification Course.
Course Name | Data Analysis with Python |
Organization | IBM |
Skill | Online Education |
Level | Beginner |
Language | English |
Price | Free |
Certificate | Yes |
For participating in quiz/exam, first you will need to enroll yourself in the given link mention below and learn Data Analysis with Python launched by IBM. Interested students must enroll for this courses and grab this golden opportunity which will definitely enhance their technical skills and you will learn more things in brief.
Link for Course Enrollment: Enroll Now
Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.
Data Analysis with Python Cognitive Class Course Exam Answer
Module 1 – Introduction
Question 1: What does CSV stand for ?
- Comma Separated Values
- Car Sold values
- Car State values
- None of the above
Question 2: In the data set what represents an attribute or feature?
- Row
- Column
- Each element in the data set
Question 3: What is another name for the variable that we want to predict?
- Target
- Feature
- Dataframe
Question 4: What is the command to display the first five rows of a dataframe df?
- df.head()
- df.tail()
Question 5: what command do you use to get the data type of each row of the dataframe df?
- df.dtypes
- df.head()
- df.tail()
Question 6: How do you get a statistical summary of a dataframe df?
- df.describe()
- df.head()
- df,tails()
Question 7: If you use the method describe() without changing any of the arguments you will get a statistical summary of all the columns of type object?
- False
- True
Module 2 – Data Wrangling
Question 1: Consider the dataframe “df” what is the result of the following operation df[‘symbolling’] = df[‘symbolling’] + 1?:
- Every element in the column “symbolling” will increase by one
- Every element in the row “symbolling” will increase by one
- Every element in the dataframe will increase by one
Question 2: Consider the dataframe “df”, what does the command df.rename(columns={‘a’:’b’}) change about the dataframe “df”
- rename column “a” of the dataframe to “b”
- rename the row “a” to “b”
- nothing as you must set the parameter “inplace =True “
Question 3: Consider the dataframe “df” , what is the result of the following operation df[‘price’] = df[‘price’].astype(int) ?
- convert or cast the row ‘price’ to an integer value
- convert or cast the column ‘price’ to an integer value
- convert or cast the entire dataframe to an integer value
Question 4: Consider the column of the dataframe df[‘a’]. The colunm has been standardized. What is the standard deviation of the values, i.e the result of applying the following operation df[‘a’].std() :
- 1
- 0
- 3
Question 5: Consider the column of the dataframe df[‘Fuel’], with two values ‘gas’ and’ diesel’. What will be the name of the new colunms pd.get_dummies(df[‘Fuel’]) ?
- 1 and 0
- Just diesel
- Just gas
- Gas and diesel
Question 6: What are the values of the new columns from part 5 a)
- 1 and 0
- Just diesel
- Just gas
- Gas and diesel
Module 3 – Exploratory Data Analysis
Question 1: Consider the dataframe “df”. Which method provides the summary statistics?
- df.describe()
- df.head()
- df.tail()
- df.summary()
Question 2: Consider the following dataframe:
df_test = df[‘body-style’, ‘price’]
The following operations is applied:
df_grp = df_test.groupby([‘body-style’], as_index=False).mean()
What are resulting values of df_grp[‘price’]:
- The average price for each body style
- The average price
- The average body style
Question 3: Correlation implies causation :
- False
- True
Question 4: What is the minimum possible value of Pearson’s Correlation :
- 1
- -100
- -1
Question 5: What is the Pearson correlation between variables X and Y, if X=Y:
- -1
- 1
- 0
- X
- Y
Module 4 – Model Development
Question 1: Let X be a dataframe with 100 rows and 5 columns, let y be the target with 100 samples,assuming all the relevant libraries and data have been imported, the following line of code has been executed:
LR = LinearRegression()
LR.fit(X, y)
yhat = LR.predict(X)
How many samples does yhat contain :
- 5
- 500
- 100
- 0
Question 2: What value of R^2 (coefficient of determination) indicates your model performs best ?
- -100
- -1
- 0
- 1
Question 3: What statement is true about Polynomial linear regression
- Polynomial linear regression is not linear in any way
- Although the predictor variables of Polynomial linear regression are not linear the relationship between the parameters or coefficients is linear.
- Polynomial linear regression uses wavelets
Question 4: The larger the mean square error, the better your model has performed
- False
- True
Question 5: Assume all the libraries are imported, y is the target and X is the features or dependent variables, consider the following lines of code:
Input = [(‘scale’, StandardScaler()), (‘model’, LinearRegression())]
pipe = Pipeline(Input)
pipe.fit(X,y)
ypipe = pipe.predict(X)
What have we just done in the above code?
- Polynomial transform, Standardize the data, then perform a prediction using a linear regression model
- Standardize the data, then perform prediction using a linear regression model
- Polynomial transform then Standardize the data
Module 5 – Model Evaluation:
Question 1: In the following plot, the vertical access shows the mean square error andthe horizontal axis represents the order of the polynomial. The red line represents the training error the blue line is the test error. What is the best order of the polynomial given the possible choices in the horizontal axis?
- 2
- 8
- 16
Question 2: What is the use of the “train_test_split” function such that 40% of the data samples will be utilized for testing, the parameter “random_state” is set to zero, and the input variables for the features and targets are_data, y_data respectively.
- train_test_split(x_data, y_data, test_size=0, random_state=0.4)
- train_test_split(x_data, y_data, test_size=0.4, random_state=0)
- train_test_split(x_data, y_data)
Question 3: What is the output of cross_val_score(lre, x_data, y_data, cv=2)?
- The predicted values of the test data using cross validation.
- The average R^2 on the test data for each of the two folds
- This function finds the free parameter alpha
Question 4: What is the code to create a ridge regression object “RR” with an alpha term equal 10
- RR=LinearRegression(alpha=10)
- RR=Ridge(alpha=10)
- RR=Ridge(alpha=1)
Question 5: What dictionary value would we use to perform a grid search for the following values of alpha: 1,10, 100. No other parameter values should be tested
- alpha=[1,10,100]
- [{‘alpha’: [1,10,100]}]
- [{‘alpha’: [0.001,0.1,1, 10, 100, 1000,10000,100000,100000],’normalize’:[True,False]} ]
Data Analysis with Python Final Exam Answers
Question 1: Question 1: What does the following command do:
df.dropna(subset=[“price”], axis=0)
- Drop the “not a number” from the column price
- Drop the row price
- Rename the data frame price
Question 2: How would you provide many of the summery statistics for all the columns in the dataframe “df”:
- df.describe(include = “all”)
- df.head()
- type(df)
- df.shape
Question 3: How would you find the shape of the dataframe df
- df.describe()
- df.head()
- type(df)
- df.shape
Question 4: What task does the following command to df.to_csv(“A.csv”) perform
- change the name of the column to “A.csv”
- load the data from a csv file called “A” into a dataframe
- Save the dataframe df to a csv file called “A.csv”
Question 5: What task does the following line of code perform:
df[‘peak-rpm’].replace(np.nan, 5,inplace=True)
- replace the not a number values with 5 in the column ‘peak-rpm’
- rename the column ‘peak-rpm’ to 5
- add 5 to the data frame
Question 6: What task does the following line of code perform:
df[‘peak-rpm’].replace(np.nan, 5,inplace=True)
- replace the not a number values with 5 in the column ‘peak-rpm’
- rename the column ‘peak-rpm’ to 5
- add 5 to the data frame
Question 7: How do you “one hot encode” the column ‘fuel-type’ in the dataframe df
- pd.get_dummies(df[“fuel-type”])
- df.mean([“fuel-type”])
- df[df[“fuel-type”])==1 ]=1
Question 8: What does the vertical axis in a scatter plot represent
- independent variable
- dependent variable
Question 9: What does the horizontal axis in a scatter plot represent
- independent variable
- dependent variable
Question 10: If we have 10 columns and 100 samples how large is the output of df.corr()
- 10 x 100
- 10 x 10
- 100×100
- 100×100
Question 11: what is the largest possible element resulting in the following operation “df.corr()”
- 100
- 1000
- 1
Question 12: if the Pearson Correlation of two variables is zero:
- the two variable have zero mean
- the two variables are not correlated
Question 13: if the p value of the Pearson Correlation is 1:
- the variables are correlated
- the variables are not correlated
- none of the above
Question 14: What does the following line of code do: lm = LinearRegression()
- fit a regression object lm
- create a linear regression object
- predict a value
Question 15: If the predicted function is:
Yhat = a + b1 X1 + b2 X2 + b3 X3 + b4 X4
The method is
- Polynomial Regression
- Multiple Linear Regression
Question 16: What steps do the following lines of code perform:
Input=[(‘scale’,StandardScaler()),(‘model’,LinearRegression())]
pipe=Pipeline(Input)
pipe.fit(Z,y)
ypipe=pipe.predict(Z)
- Standardize the data, then perform a polynomial transform on the features Z
- find the correlation between Z and y
- Standardize the data, then perform a prediction using a linear regression model using the features Z and targets y
Question 17: What is the maximum value of R^2 that can be obtained
- 10
- 1
- 0
Question 18: We create a polynomial feature as follows “PolynomialFeatures(degree=2)”, what is the order of the polynomial
- 0
- 1
- 2
Question 19: You have a linear model the average R^2 value on your training data is 0.5, you perform a 100th order polynomial transform on your data then use these values to train another model, your average R^2 is 0.99 which comment is correct
- 100-th order polynomial will work better on unseen data
- You should always use the simplest model
- the results on your training data is not the best indicator of how your model performs, you should use your test data to get a beter idea
Question 20: You train a ridge regression model, you get a R^2 of 1 on your training data and you get a R^2 of 0 on your validation data, what should you do:
- Nothing your model performs flawlessly on your test data
- your model is under fitting perform a polynomial transform
- your model is overfitting, increase the parameter alpha
Conclusion
Hopefully, this article will be useful for you to find all the Modules and Final Quiz Answers of Data Analysis with Python of Cognitive Class and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also and follow our Techno-RJ Blog for more updates.
Hi, i think that i saw you visited my weblog thus i came to “return the favor”.I’m trying to find things to improve my site!I suppose its ok to use a few of your ideas!!
I love your blog.. very nice colors & theme. Did you make this website yourself or did you hire someone to do it for you? Plz answer back as I’m looking to design my own blog and would like to find out where u got this from. thanks
Helpful information. Fortunate me I found your website accidentally, and I’m surprised why this twist of fate did not happened in advance! I bookmarked it.
I have recently started a site, the information you offer on this website has helped me greatly. Thanks for all of your time & work.
I’m very happy to read this. This is the type of manual that needs to be given and not the random misinformation that’s at the other blogs. Appreciate your sharing this greatest doc.
Hello! Someone in my Facebook group shared this site with us so I came to take a look. I’m definitely loving the information. I’m bookmarking and will be tweeting this to my followers! Wonderful blog and fantastic style and design.
I am not very superb with English but I line up this rattling easy to read .
Does your site have a contact page? I’m having a tough time locating it but, I’d like to shoot you an email. I’ve got some recommendations for your blog you might be interested in hearing. Either way, great site and I look forward to seeing it expand over time.
Pretty! This was a really wonderful post. Thank you for your provided information.
I got good info from your blog
cialis 20mg generic order tadalafil 10mg online buy ed meds online
I just could not go away your site before suggesting that I actually loved the standard information a person provide to your visitors? Is going to be again frequently to inspect new posts
buy duricef 500mg pill buy epivir oral finasteride
buy diflucan generic order generic fluconazole 100mg purchase cipro sale
order metronidazole generic cost metronidazole order keflex 500mg pills
avanafil 200mg uk tadalafil canada order diclofenac 100mg pills
brand cleocin sildenafil online order fildena without prescription
indomethacin 75mg tablet oral cefixime 200mg order suprax pills
nolvadex 20mg for sale order tamoxifen 20mg ceftin canada
buy trimox 500mg sale anastrozole without prescription cheap biaxin
order bimatoprost without prescription trazodone price desyrel 50mg price
purchase clonidine online purchase catapres generic purchase tiotropium bromide generic
cost suhagra buy suhagra generic sildenafil overnight
minocin 100mg pills buy actos 15mg generic pioglitazone 30mg price
order arava 10mg generic order sulfasalazine for sale azulfidine for sale
isotretinoin 20mg cheap order isotretinoin 40mg buy azithromycin 500mg
cheap tadalafil pills buy cialis 10mg generic buy cialis 20mg without prescription
buy generic azipro online prednisolone 40mg us buy gabapentin
stromectol drug mens ed pills generic prednisone 5mg
lasix 40mg price buy lasix diuretic order ventolin inhalator without prescription
buy levitra 10mg pills brand hydroxychloroquine buy plaquenil 200mg pills
buy asacol paypal avapro canada avapro pills
benicar without prescription divalproex order online depakote over the counter
brand temovate buy amiodarone 200mg pills brand amiodarone
buy diamox 250mg generic order imdur 40mg buy generic azathioprine for sale