What is Data Science? Coursera Quiz Answers

What is Data Science? Quiz Answers

Week 1 – Quiz 1: Data Science: The Sexiest Job in the 21st Century

Q1. Which of the following jobs was called by the Harvard Business Review the sexiest job of the 21st century?

Coal Mining
Data Science
Math and Statistics
Renewable Energy Engineering

Q2. According to the report by the McKinsey Global Institute, by 2018, it is projected that there will be a shortage of 140,000 – 190,000 people with deep analytical skills in the world.

True
False

Q3. Walmart addressed its analytical needs by approaching Keggle to host a competition for analyzing its proprietary data.

True
False

Q4. The New York Times reported that the average base salary of a data scientist is $85,000 + a competitive bonus.

True
False

Q5. According to professor Haider, the three important qualities to possess in order to succeed as a data scientist are curious, judgemental, and proficient in programming.

True
False

Extra Questions

Q6. Harvard Business Review called data science the sexiest job in the 21st century.

True
False

Q7. How is Walmart reported to have addressed its analytical needs?

Social media
Code sharing
Crowdsourcing
Outsourcing
None of the options is correct

Q8. What is the average base salary of a data scientist reported by the New York Times?

$100,000
$150,000
$112,000
$16 per hour
$85,000 + Bonus

Q9. According to professor Haider, the three important qualities to possess in order to succeed as a data scientist are:

Curious.
Judgemental.
Proficient in Programming.
Good at Math and Statistics.
Good Story Teller (Argumentative).

Quiz 2: What Makes Someone a Data Scientist?

Q1. Hal Varian, the chief economist at Google, declared that “the sexy job in the next ten years will be

Physicists
Statisticians
Engineers
Computer Scientists

Q2. The author defines a data scientist as someone who finds solutions to problems by analyzing data using appropriate tools and then tells stories to communicate their finding to the relevant stakeholders.

True
False

Q3. According to the reading, the author defines data science as the art of uncovering hidden secrets in data.

True
False

Q4. What is admirable about Dr. Patil’s definition of a data scientist is that it limits data science to activities involving machine learning.

True
False

Q5. According to the reading, the characteristics exhibited by the best data scientists are those who are curious, ask good questions, and are O.K. dealing with unstructured situations.

True
False

Extra Questions

Q1. Hal Varian, the chief economist at Google, declared that “the sexy job in the next ten years will be computer scientists”.

True
False

Q2. According to the reading, how does the author define data science?

Data science is what data scientists do.
Data science is some data and more science.
Data science is a physical science like physics or chemistry
Data science is the art of uncovering the hidden secrets in data.
Data science is a way of understanding things and understanding the world.

Q3. According to the reading, what is admirable about Dr. Patil’s definition of a data scientist?

His definition excludes statistics.
His definition limits data science to activities involving machine learning
His definition is about weaving strong narratives into analytics.
His definition is inclusive of individuals from various academic backgrounds and training.

Q4. According to the reading, the characteristics exhibited by the best data scientists are those who are curious, ask good questions, and have at least 10 years of experience.

True
False

Q5. According to the reading, what characteristics are said to be exhibited by the best data scientists?

Thinkers who are really curious and hold a Ph.D.
Really curious people who ask good questions.
Curious individuals who ask good questions and are O.K. dealing with unstructured situations
Really curious engineers and statisticians.
Really curious people who ask good questions and have at least 10 years of experience.

Week 2 – Quiz 3: Data Mining

Q1. According to the reading, the output of a data-mining exercise largely depends on:

The data scientist
The quality of the data
The scope of the project
The programming language used

Q2. Prior Variable Analysis and Principal Component Analysis are both examples of a data reduction algorithm.

True
False

Q3. After the data are appropriately processed, transformed, and stored, what is a good starting point for data mining?

Machine learning.
Data Visualization.
Non-parametric methods.
Creating a relational database.

Q4. When data are missing in a systematic way, you can simply extrapolate the data or impute the missing data by filling in the average of the values around the missing data.

True
False

Q5. “Formal evaluation could include testing the predictive capabilities of the models on observed data to see how effective and efficient the algorithms have been in reproducing data.” This is known as:

Prototyping.
Overfitting.
In-sample forecast.
Reverse engineering.

Q6. What is an example of a data reduction algorithm?

Cojoint Analysis.
A/B Testing.
Prior Variable Analysis.
Principal Component Analysis.

Q7. After the data are appropriately processed, transformed, and stored, machine learning and non-parametric methods are a good starting point for data mining.

True
False

Q8. In–sample forecast is the process of formally evaluating the predictive capabilities of the models developed using observed data to see how effective the algorithms are in reproducing data.

True
False

Quiz 4: Regression

Q1. The author discovered that all else being equal, houses located less than 5kms but more than 2.5 km to shopping centers sold for more than the rest.

True
False

Q2. The author discovered that houses located more than 2.5 km to shopping centers
sold for less than the rest.

True
False

Q3. Based on the reading, which of the following are questions that can be put to
regression analysis?

Do homes with brick exterior sell for less than homes with stone exterior?
Do homes with brick exterior sell in rural areas?
What is the impact of lot size on housing price?
What are typical land taxes in a house sale?

Q4. The real added value of the author’s research on residential real estate properties
is quantifying people’s preferences of different transport services.

False
True

Q5. Regression is a statistical technique developed by Sir Frances Galton.

False
True

Q6. According to the reading, the author discovered that an additional bedroom adds
more to the housing prices than an additional washroom.

False
True

Q7. What did the Author’s Research reveal about proximity to large shopping centres?

The author discovered that houses located more than 2.5 kms to shopping centres sold for less than the rest.
The author discovered that proximity to large shopping centres didn’t have any significant impact on the prices of housing units.
The author discovered that proximity to large shopping centres had a nonlinear impact on the housing prices.
The author discovered that houses located more than 5 kms to shopping centres sold for less than the rest.

Q8. ”How much does a finished basement contribute to the price of a housing unit?” is a question that can be put to regression analysis.

False
True

Week 3 – Quiz 5 The Final Deliverable

Q1. The report discussed in the reading successfully did the job of using data and analytics to generate the likely economic scenarios.

True
False

Q2. The ultimate purpose of analytics is to communicate findings to stakeholders to formulate policy or strategy.

True
False

Q3. The Untied States Economic Forecast is a publication by McKinsey University Press.

True
False

Q4. The report discussed in the reading successfully did the job of:

Calculating projections for the economy
Summarizing pages and pages of research
Convincing the leadership team to act on an initiative
Using data and analytics to generate the likely economic scenarios

Q5. According to the reading, it is recommended that a team waits until the results of analytics are out before they can decide on the final deliverable.

True
False

Extra Questions

Q6. According to the reading, what is the ultimate purpose of analytics?

To evangelize data science
To facilitate meetings between sales and marketing
To efficiently store big data with minimum storage requirements
To communicate findings to stakeholders to formulate policy or strategy

Q7. What role of a data scientist is discussed in the reading?

Using insights to build a narrative to communicate findings
Managing a team of analysts to create a predictive model
Developing a strategy to fix the problems in the findings
Using the data to put together a story that the CEO wants to tell

Q8. According to the reading, in order to produce a compelling narrative, initial planning and conceptualizing of the final deliverable is of extreme importance.

True
False

Quiz 6: The Report Structure

Q1. Regardless of the length of the final deliverable, which of the following does the author recommend that you include:

Code
Discussion Section
Appendices
A Cover Page
Table of Contents

Q2. An introductory section is always helpful in introducing the research methods and presenting the statistical calculations.

True
False

Q3. The results section is where you present:

The empirical findings.
OR Squared.
The methods used.
The conclusion.

Q4. The discussion section is where you introduce the research methods and data sources used for the analysis.

True
False

Q5. Adding a list of references and an acknowledgment section are examples of housekeeping, according to the author.

True
False

Extra Questions

Q6. The results section is where you craft your main arguments and present your conclusion.

True
False

Q7. The discussion section is where you:

Highlight how your findings provide the ultimate missing piece to the puzzle
Introduce the research methods and data sources used for the analysis
Refer the reader to the research question and the knowledge gaps you identified earlier
Rely on the power of narrative to enable numbers to communicate your important findings to the readers

Q8. Regardless of the length of the final deliverable, the author recommends that it includes a cover page, table of contents, executive summary, methodology section, and a discussion section.

True
False

Q9. An introductory section is always helpful in:

Presenting the statistical calculations
Introducing the research methods
Setting up the problem for the reader who might be new to the topic
Advertising the product

Q10. The results section is where you craft your main arguments and present your conclusion

True
False

Q11. The discussion section is where you:

Introduce the research methods and data sources used for the analysis.
Highlight how your findings provide the ultimate missing piece to the puzzle.
Refer the reader to the research question and the knowledge gaps you identified earlier.
Rely on the power of narrative to enable numbers to communicate your important findings to the readers.

Final Exam Quiz Answer

Q1. Based on the videos and the reading material, how would you define a data scientist and data science?

Data Science:

Data science is something that data scientist do.
Data science is a way of extracting insights from large volumes of disparate data.
Data science involves drawing patterns from seemingly random structured and unstructured type of data.

Data scientists:

Data scientists are curious and analytical thinkers who use a variety of math skills not limited to Mathematics, Statistics and Probability to solve a problem.
They apply different available methods and algorithms to draw insights and conclusions from various kinds of data.
After applying data science methodologies, they are effective communicators and story tellers who can present their findings often to present new findings or confirm what was initially suspected.

Q2. As discussed in the videos and the reading material, data science can be applied to problems across different industries. What industry are you passionate about and would like to pursue a data science career in?

I am passionate about pursuing a data science career in the field of Healthcare with the main focus is improving the quality of care provided and making healthcare affordable. I would like to create models to predict diseases very early on by looking at various parameters of a person not limited to genetics, family history, lifestyle, and diet.

Q3. Based on the videos and the reading material, what are the ten main components of a report that would be delivered at the end of a data science project?

The 10 main components of a data science project report would be:

Cover Page with Author’s name, contacts, affiliations if any and publication date
Table of Contents containing main headings, list of contents and figures
Abstract / Executive summary to explain gist of the report
Introduction to explain the topic to new readers
Literature Review including citations of authors and data sources
Methodology section to explain the research methods and data sources used for analysis
Detailed Explanations including Results and discussion sections
Conclusions which generalize findings and identify possible future outcomes.
References
Acknowledgement and Appendices (if Needed)