Are you looking for Introduction to Data Science Exam Answers by Cognitive Class? If yes, this article will help you find all the questions and answers asked in the Cognitive Class Introduction to Data Science Quiz. I have followed this article to solve all the questions for this exam.
This course helps you find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science. This course was formerly named Data Science 101.
Cognitive Class – Introduction to Data Science Answers
Module 1 – Defining Data Science
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
20 000 – 50 000 people
800 000 – 900 000 people
3 – 6 million people
2. How is Walmart reported to have addressed its analytical needs?
None of the options is correct
3. In the reading, the New York Times reported the base salary for data scientists as:
$85 000 + Bonus
$16 per hour
Module 2 – What do data science people do?
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
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?
3. Who developed the statistical technique known as Regression?
Sir Frances Galton
Dhanurjay “DJ” Patil
Module 3 – Data Science in Business
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
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
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
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
2. The results section is where you present:
The empirical findings
The methods used
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
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
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
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 Final Exam Answers
1. In the reading, the output of a data mining exercise largely depends on:
The programming language used
The quality of the data
The scope of the project
The data scientist
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
3. What should you do when data are missing in a systematic way?
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
4. What is an example of a data reduction algorithm?
Principal Component Analysis
Prior Variable Analysis
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
6. What is a good starting point for data mining?
Writing a data dictionary
Creating a relational database
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
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
9. When processing data, what factor can lead to errors in data?
Synchronizing the database
Changing services providers
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:
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