Introduction to Data Science in Python Coursera Quiz Answers – Networking Funda
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Table of contents, introduction to data science in python week 01 quiz answers, introduction to the course.
Q1. “What will be the output of the following code?
- ‘bat, bet, bit, bot’
- [‘bat’, ‘bot’]
- [‘bat’, ‘bet’, ‘bit’, ‘bot’]
Assume a and b are two (20, 20) numpy arrays. The L2-distance (defined above) between two equal dimension arrays can be calculated in python as follows:
Which of the following expressions using this function will produce a different result from the rest?
- l2_dist(np.reshape(a, (20 * 20)), np.reshape(b, (20 * 20)))
- l2_dist(a, b)
- l2_dist(np.reshape(a, (20 * 20)), np.reshape(b, (20 * 20, 1)))
- l2_dist(a.T, b.T)
Q3. Consider the following variables in Python:
Which of the following statements regarding these variables is correct?
- a1.shape == a2.shape
- a3.shape == a4.shape
- a5.shape == a1.shape
- a4.ndim() == 1
Q4. Which of the following is the correct output for the code given below?
- [[1 1 0][1 1 0]]
- [[0 1 1][0 1 1]]
- [[1 1 1][1 1 1]]
- [[0 0 1][1 1 1]]
Q5. Given the 6×6 NumPy array r shown below, which of the following options would slice the shaded elements?
- r[[2,4],[2,4]]
- r[[2,3],[2,3]]
For the given string, which of the following regular expressions can be used to check if the string starts with ‘AC’?
- re.findall(‘^AC’, s)
- re.findall(‘^[AC]’, s)
- re.findall(‘[^A]C’, s)
- re.findall(‘AC’, s)
Q7. What will be the output of the variable L after the following code is executed?
Q8. Which of the following is the correct regular expression to extract all the phone numbers from the following chunk of text:
- [(]\d{3}[)]\d{3}[-]\d{4}
- \d{3}\s\d{3}[-]\d{4}
- [(]\d{3}[)]\s\d{3}[-]\d{4}
- \d{3}[-]\d{3}[-]\d{4}
Q9. Which of the following regular expressions can be used to get the domain names (e.g. google.com, www.baidu.com) from the following sentence?
- (?<=https:\/\/)([.]*)
- (?<=[https]:\/\/)([A-Za-z0-9.]*)
- (?<=https:\/\/)([A-Za-z0-9]*)
- (?<=https:\/\/)([A-Za-z0-9.]*)
Q10. The text from the Canadian Charter of Rights and Freedoms section 2 lists the fundamental freedoms afforded to everyone. Of the four choices provided to replace X in the code below, which would accurately count the number of fundamental freedoms that Canadians have?
Introduction to Data Science in Python Week 02 Quiz Answers
Introduction to pandas and series data.
Q1. For the following code, which of the following statements will not return True?
In the above python code, the keys of the dictionary d represent student ranks and the value for each key is a student name. Which of the following can be used to extract rows with student ranks that are lower than or equal to 3?
- S.iloc[0:2]
- S.iloc[0:3]
Q3. Suppose we have a DataFrame named df . We want to change the original DataFrame df in a way that all the column names are cast to upper case. Which of the following expressions is incorrect to perform the same?
- df.rename(mapper = lambda x: x.upper(), axis = 1)
- df = df.rename(mapper = lambda x: x.upper(), axis = 1)
- df = df.rename(mapper = lambda x: x.upper(), axis = ‘column’)
- df.rename(mapper = lambda x: x.upper(), axis = 1, inplace = True)
For the given DataFrame df we want to keep only the records with a toefl score greater than 105. Which of the following will not work?
- df[df[‘toefl score’] > 105]
- df.where(df[‘toefl score’] > 105)
- All of these will work
- df.where(df[‘toefl score’] > 105).dropna()
Q5. Which of the following can be used to create a DataFrame in Pandas?
- Pandas Series object
- All of these work
- Python dict
Q6. Which of the following is an incorrect way to drop entries from the Pandas DataFrame named df shown below?
- df.drop(‘Ohio’)
- df.drop([‘Utah’, ‘Colorado’])
- df.drop(‘two’)
- df.drop(‘one’, axis = 1)
Q7. For the Series s1 and s2 defined below, which of the following statements will give an error?
Q8. Which of the following statements is incorrect ?
- loc and iloc are two useful and commonly used Pandas methods.
- We can use s.iteritems() on a pd.Series object s to iterate on it.
- If s and s1 are two pd.Series objects, we cannot use s.append(s1) to directly append s1 to the existing series s
- If s is a pd.Series object, then we can use s.loc[label] to get all data where the index is equal to label.
For the given DataFrame df shown above, we want to get all records with a toefl score greater than 105 but smaller than 115. Which of the following expressions is incorrect to perform the same?
- df[df[‘toefl score’].gt(105) & df[‘toefl score’].lt(115)]
- df[(df[‘toefl score’] > 105) & (df[‘toefl score’] < 115)]
- df[(df[‘toefl score’].isin(range(106, 115)))]
- (df[‘toefl score’] > 105) & (df[‘toefl score’] < 115)
Q10. Which of the following is the correct way to extract all information related to the student named Alice from the DataFrame df given below:
- df.T[‘Mathematics’]
- df.iloc[‘Mathematics’]
- df[‘Mathematics’]
- df[‘Alice’]
Introduction to Data Science in Python Week 03 Quiz Answers
More data processing with pandas.
Q1. Consider the two DataFrames shown below, both of which have Name as the index. Which of the following expressions can be used to get the data of all students (from student_df) including their roles as staff, where nan denotes no role?
- pd.merge(student_df, staff_df, how=’left’, left_index=True, right_index=True)
- pd.merge(student_df, staff_df, how=’right’, left_index=True, right_index=True)
- pd.merge(staff_df, student_df, how=’right’, left_index=False, right_index=True)
- pd.merge(staff_df, student_df, how=’left’, left_index=True, right_index=True)
Q2. Consider a DataFrame named df with columns named P2010, P2011, P2012, P2013, 2014 and P2015 containing float values. We want to use the apply method to get a new DataFrame named result_df with a new column AVG. The AVG column should average the float values across P2010 to P2015. The apply method should also remove the 6 original columns (P2010 to P2015). For that, what should be the value of x and y in the given code?
- x = 0 , y = 1
- x = 0 , y = 0
- x = 1 , y = 0
- x = 1 , y = 1
Q3. Consider the Dataframe df below, instantiated with a list of grades, ordered from best grade to worst. Which of the following options can be used to substitute X in the code given below, if we want to get all the grades between ‘A’ and ‘B’ where ‘A’ is better than ‘B’?
- my_categories = pd.CategoricalDtype(categories=[‘D’, ‘D+’, ‘C-‘, ‘C’, ‘C+’, ‘B-‘, ‘B’, ‘B+’, ‘A-‘, ‘A’, ‘A+’], ordered=True)
- my_categories = pd.CategoricalDtype(categories=[‘D’, ‘D+’, ‘C-‘, ‘C’, ‘C+’, ‘B-‘, ‘B’, ‘B+’, ‘A-‘, ‘A’, ‘A+’])
- (my_categories=[‘A+’, ‘A’, ‘A-‘, ‘B+’, ‘B’, ‘B-‘, ‘C+’, ‘C’, ‘C-‘, ‘D+’, ‘D’], ordered=True)
- my_categories = pd.CategoricalDtype(categories=[‘A+’, ‘A’, ‘A-‘, ‘B+’, ‘B’, ‘B-‘, ‘C+’, ‘C’, ‘C-‘, ‘D+’, ‘D’])
Q4. Consider the DataFrame df shown in the image below. Which of the following can return the head of the pivot table as shown in the image below df?
- df.pivot_table(values=’score’, index=’country’, columns=’Rank_Level’, aggfunc=[np.median], margins=True)
- df.pivot_table(values=’score’, index=’country’, columns=’Rank_Level’, aggfunc=[np.median])
- df.pivot_table(values=’score’, index=’Rank_Level’, columns=’country’, aggfunc=[np.median])
- df.pivot_table(values=’score’, index=’Rank_Level’, columns=’country’, aggfunc=[np.median], margins=True)
Q5. Assume that the date ’11/29/2019′ in MM/DD/YYYY format is the 4th day of the week, what will be the result of the following?
Q6. Consider a DataFrame df. We want to create groups based on the column group_key in the DataFrame and fill the nan values with group means using:
Which of the following is correct for performing this task?
- df.groupby(group_key).transform(filling_mean)
- df.groupby(group_key).filling_mean()
- df.groupby(group_key).aggregate(filling_mean)
- df.groupby(group_key).apply(filling_mean)
Consider the DataFrames above, both of which have a standard integer based index. Which of the following can be used to get the data of all students (from student_df) and merge it with their staff roles where nan denotes no role?
- result_df = pd.merge(student_df, staff_df, how=’inner’, on=[‘First Name’, ‘Last Name’])
- result_df = pd.merge(staff_df, student_df, how=’outer’, on=[‘First Name’, ‘Last Name’])
- result_df = pd.merge(staff_df, student_df, how=’right’, on=[‘First Name’, ‘Last Name’])
- result_df = pd.merge(student_df, staff_df, how=’right’, on=[‘First Name’, ‘Last Name’])
Q8. Consider a DataFrame df with columns name, reviews_per_month, and review_scores_value. This DataFrame also consists of several missing values. Which of the following can be used to:
- calculate the number of entries in the name column, and
- calculate the mean and standard deviation of the reviews_per_month, grouping by different review_scores_value?
- df.groupby(‘review_scores_value’).agg({‘name’: len, ‘reviews_per_month’: (np.mean, np.std)})
- df.agg({‘name’: len, ‘reviews_per_month’: (np.mean, np.std)}
- df.agg({‘name’: len, ‘reviews_per_month’: (np.nanmean, np.nanstd)}
- df.groupby(‘review_scores_value’).agg({‘name’: len, ‘reviews_per_month’: (np.nanmean, np.nanstd)})
Q9. What will be the result of the following code?:
- Period(‘2019-12-01’, ‘D’)
- Period(‘2019-12-06’, ‘D’)
- Period(‘2019-06’, ‘M’)
- Period(‘2019-12’, ‘M’)
Q10. Which of the following is not a valid expression to create a Pandas GroupBy object from the DataFrame shown below?
- df.groupby(‘vegetable’)
- df.groupby(‘class’, axis = 0)
- grouped = df.groupby([‘class’, ‘avg calories per unit’])
- df.groupby(‘class’)
Introduction to Data Science in Python Week 04 Quiz Answers
Beyond data manipulation.
Q1. Consider the given NumPy arrays a and b. What will be the value of c after the following code is executed?
Q2. Given the string s as shown below, which of the following expressions will be True?
Q3. Consider a string s. We want to find all characters (other than A) which are followed by triple A, i.e., have AAA to the right. We don’t want to include the triple A in the output and just want the character immediately preceding AAA . Complete the code given below that would output the required result.
Consider the following 4 expressions regarding the above pandas Series df. All of them have the same value except one expression. Can you identify which one it is?
Consider the two pandas Series objects shown above, representing the no. of items of different yogurt flavors that were sold in a day from two different stores, s1 and s2. Which of the following statements is True regarding the Series s3 defined below?
Q6. In the following list of statements regarding a DataFrame df, one or more statements are correct. Can you identify all the correct statements?
- Every time we call df.set_index(), the old index will be discarded.
- Every time we call df.set_index(), the old index will be set as a new column.
- Every time we call df.reset_index(), the old index will be discarded.
- Every time we call df.reset_index(), the old index will be set as a new column.
Q7. Consider the Series object S defined below. Which of the following is an incorrect way to slice S such that we obtain all data points corresponding to the indices ‘b’, ‘c’, and ‘d’?
Consider the DataFrame df shown above with indexes ‘R1’, ‘R2’, ‘R3’, and ‘R4’. In the following code, a new DataFrame df_new is created using df. What will be the value of df_new[1] after the below code is executed?
Consider the DataFrame named new_df shown above. Which of the following expressions will output the result (showing the head of a DataFrame) below?
- new_df.stack()
- new_df.unstack()
- new_df.stack().stack()
- new_df.unstack().unstack()
Consider the DataFrame df shown above. What will be the output (rounded to the nearest integer) when the following code related to df is executed:
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Introduction to Data Science with Python
Learn python for data analysis.
Join Harvard University Instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
What You'll Learn
Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).
Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career. Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python , and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.
The course will be delivered via edX and connect learners around the world. By the end of the course, participants will learn:
- Gain hands-on experience and practice using Python to solve real data science challenges
- Practice Python coding for modeling, statistics, and storytelling
- Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
- Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
- Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study
Your Instructor
Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science(IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences. He has had a long and distinguished career as a scientist and data science educator, and currently teaches the CS109 course series for basic and advanced data science at Harvard University, as well as the capstone course (industry-sponsored data science projects) for the IACS master’s program at Harvard. Pavlos has a Ph.D in theoretical physics from the University of Pennsylvania and has focused recently on the use of machine learning and AI in astronomy, and computer science. He was Deputy Director of the National Expandable Clusters Program (NSCP) at the University of Pennsylvania, and was instrumental in creating the Initiative in Innovative Computing (IIC) at Harvard. Pavlos has taught multiple courses on machine learning and computational science at Harvard, and at summer schools, and at programs internationally.
Course Overview
- Linear Regression
- Multiple and Polynomial Regression
- Model Selection and Cross-Validation
- Bias, Variance, and Hyperparameters
- Classification and Logistic Regression
- Multi-logstic Regression and Missingness
- Bootstrap, Confidence Intervals, and Hypothesis Testing
- Capstone Project
Ways to take this course
When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.
A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate.
Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.
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Join Harvard University Instructor Pavlos Protopapas to learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.
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This repository includes course assignments of Introduction to Data Science in Python on coursera by university of michigan - tchagau/Introduction-to-Data-Science-in-Python
These may include the latest answers to Introduction to Data Science in Python's quizs and assignments. You can see the link in my blog or CSDN. Blog link: Coursera | Introduction to Data Science in Python(University of Michigan)| Quiz答案. Coursera | Introduction to Data Science in Python(University of Michigan)| Assignment1
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Coursera Course: Introduction to Programming 👩💻 with MATLAB ~by Vanderbilt University 🎓 ... python coursera python3 coursera-assignment coursera-python python-data-structures coursera-solutions Updated ... This repo consists of the lecture PDFs and quiz solutions of all the courses under the IBM Data Science Professional Certificate ...
Introduction to Data Science in Python WEEK 1 Quiz Answers Coursera | by University of MichiganThis course will introduce the learner to the basics of the py...
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order.
This repository contains the answers for coursera 's "Databases and SQL for Data Science with Python " course by ibm with honors (week 1 - week 6) coursera ibm coursera-machine-learning coursera-data-science coursera-course coursera-assignment coursera-python coursera-specialization cognitive-class cognitive-class-course course-answers coursera ...
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