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What is admirable about Dr patils definition of a data scientist is that it limits data science to activities involving machine learning?

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Question 1

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

Statisticians.

Engineers.

Computer Scientists.

Physicists.

Question 2

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

True.

Question 3

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

False.

The author defines data science as what data scientists do.

Question 4

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

False.

What is admirable about his definition is that it does not limit data science to activities involving machine learning.

Question 5

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

Really curious people who ask good questions.

Really curious engineers and statisticians.

Curious individuals who ask good questions and are O.K. dealing with unstructured situations.

Really curious people who ask good questions and have at least 10 years of experience.

Thinkers who are really curious and hold a Ph.D.

🗂️ Page Index for this GitHub Wiki

Course Name: – Introduction to Data Science

Module 1: Defining data science

Question 1. In the report by the McKinsey Global Institute, by 2018, it is projected that there will be a shortage of people with deep analytical skills in the United States. What is the size of this shortage?

140 000 – 190 000 people
120 000
20 000 – 50 000 people
800 000 – 900 000 people
3 – 6 million people

Question 2. How is Walmart reported to have addressed its analytical needs?

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

Question 3. In the reading, the New York Times reported the base salary for data scientists as:

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

Module 2: What do data science people do?

Question 1. In the reading, what was the real added value of the research?

Quantifying the magnitude of relationships
Analyzing consumer behavior
Proximity to transport and infrastructure resulted in higher housing prices
Shopping centers had a nonlinear impact on housing prices
‘all else being equal’ is a powerful assumption

Question 2. In the reading, what is an example of a question that can be put to a regression analysis?

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?
How much does a finished basement cost?
How much should a house near a park cost?

Question 3. Who developed the statistical technique known as Regression?

Andrew Gelman
Sir Frances Galton
Anindya Ghose
Saeed Aghabozorgi
Dhanurjay “DJ” Patil

Module 3: Data science in Business

Question 1. In the reading, what is the ultimate purpose of analytics:

To evangelize data science
To facilitate meetings between sales and marketing
To communicate findings to the concerned
To build models
To generate reports

Question 2. In the reading, the report successfully did the job of:

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

Question 3. In this reading, what is the role of the data scientist?

Email the stakeholders about the analysis
Manage a team of analysts to create a model
Develop the strategy to fix the problems in the findings
Use the insights to build the narrative to communicate the findings
Use the data to tell the story the CEO wants to tell

Module 4: Use cases for data science

Question 1. An introductory section is always helpful in:

Setting up the problem for the reader
Presenting the statistical calculations
Summarizing the text
Introducing the research methods
Advertising the product

Question 2. The results section is where you present:

The empirical findings
R Squared
The conclusion
The contributors
The methods used

Question 3. In the reading, what is an example of housekeeping?

Adding slide numbers
Including a list of references
Adding headings to charts
Adding pictures to graphs
Saving the report as a PDF

Module 5: Data Science People

Question 1. In the reading, how does the author define ‘data science’?

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

Question 2. In the reading, what is admirable about Dr. Patil’s definition of a ‘data scientist’?

His definition limits data science to activities involving machine learning
His definition is only for people who program in Python
His definition excludes statistics
His definition is about weaving strong narratives into analytics
His definition is inclusive of individuals from various academic backgrounds and training

Question 3. In the reading, what characteristics are said to be exhibited by “The best” data scientists?

Ask good questions, really curious people, engineers
Really curious, ask good questions, at least 10 years of experience
Thinkers, ask good questions, O.K. dealing with unstructured situations
Thinkers, really curious, PHDs
Really curious people, engineers, statisticians

Introduction to Data Science Cognitive Class Final Exam Answers

Question 1. In the reading, the output of a data mining exercise largely depends on:

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

Question 2 In the reading, what are some of the steps down the data mine?

Establish goals, store data, mine data, present data
Establish goals, select data, pre-process data, transform data
Establish goals, team meeting, select data, transform data
Establish goals, mine data, evaluate data mining results, create database
Establish goals, select data, pre-process data, present data

Question 3. What should you do when data are missing in a systematic way?

Extrapolate data
Use Python to generate values
Determine the average of the values around the missing data
Determine the impact of missing data on the results
Determine who was managing the database

Question 4. What is an example of a data reduction algorithm?

Conjoint Analysis
Programmatization
A/B Testing
Principal Component Analysis
Prior Variable Analysis

Question 5. What should be a prime concern for storing data?

Data safety and privacy
Hiring the right database manager
The size of the files
The physical location of the servers
Hadoop clusters

Question 6. What is a good starting point for data mining?

Data Visualization
Writing a data dictionary
Non-parametric methods
Creating a relational database
Machine learning

Question 7. When evaluating mining results, data mining and evaluating becomes:

A transformative process
An intuitive process
A data driven process
A strategic process
An iterative process

Question 8. When establishing data mining goals, the accuracy expected from the results also influences the:

The timelines for the project
The scope of the project
The costs
The presentation
Data scientist

Question 9. When processing data, what factor can lead to errors in data?

Synchronizing the database
Changing services providers
Renaming variables
Human error
Overfitting

Question 10. “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
False positive
Reverse engineering

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