Get All Modules What is Data Science? Coursera Quiz Answers
Table of Contents
Module 01: Practice Quiz: Data Science: The Sexiest Job in the 21st Century
Q1. Why are companies looking for well-rounded individuals when hiring data scientists?
Answer: Because data science requires a combination of skills, including subject matter expertise, programming, and communication abilities
Data science is multifaceted, requiring technical, analytical, and communication skills to interpret data and share insights effectively with various stakeholders.
Q2. Why is there a growing demand for data scientists and analytics professionals in various industries?
Answer: Because of the digital revolution and the need to analyze big data for effective decision-making
The growing availability of big data and the need for informed decision-making across industries has fueled the demand for data scientists.
Q3. Due to the shortage of data scientists, employers are willing to pay top salaries for their talent, with an average base salary for data scientists reported as $112,000.
Answer: True
There is a high demand for data scientists, and their skills are in short supply, leading to competitive salaries in the field.
Module 01: Practice Quiz: Defining Data Science
Q1. Imagine you’re working for a retail company that wants to optimize its product offerings and marketing strategies. In this scenario, you would use Data Science for:
Answer: Analyzing customer purchase data to identify trends and tailor product recommendations.
Data science can help businesses understand customer behavior and preferences, enabling them to optimize product offerings and personalize marketing strategies.
Q2. What is the role of data analysis in Data Science and how does it contribute to decision-making?
Answer: Data analysis involves gathering insights from data and helps make informed decisions.
Data analysis is essential in extracting meaningful patterns and trends from data, providing the foundation for data-driven decision-making.
Q3. In a healthcare context with patient data, medical histories, and treatment outcomes, Data Science can be applied to:
Answer: Analyzing patient data for personalized treatment plans.
Data science can be used to analyze patient data and develop personalized treatments, leading to more effective healthcare outcomes.
Q4. Considering an individual with a marketing background transitioning to data science, how might their marketing experience contribute to their data science journey?
Answer: Their marketing background might assist in interpreting data to generate actionable insights.
A marketing background can help a data scientist contextualize and interpret data effectively, particularly when the data relates to customer behavior and business strategies.
Q5. You have just started your career as a data scientist. Which of the following skills should you develop to succeed as a data scientist? You should:
Answer: Cultivate curiosity, develop strong positions, and learn to communicate insights effectively through storytelling.
Success in data science requires not just technical skills but also the ability to communicate insights in a compelling way that drives decision-making and action.
Module 01: Graded Quiz: Defining Data Science
Q1. You are a data scientist about to start a new project. What would one of your key roles be?
Answer: Asking questions to clarify the business need.
One of the primary roles of a data scientist is to understand the business problem thoroughly and ask the right questions to ensure the data analysis aligns with the company’s objectives.
Q2. When did the term “data science” come into existence and who is credited with coining the term?
Answer: 2009-2011, DJ Patil or Andrew Gelman.
The term “data science” became more widely used in the early 2000s, and DJ Patil and Andrew Gelman are among those credited with popularizing the term in the 2009-2011 period.
Q3. As an aspiring data scientist, what primary qualities should you possess to succeed in the field?
Answer: Curiosity and storytelling skills.
To succeed as a data scientist, it’s important to have curiosity to explore and understand data, along with the ability to tell a compelling story with the insights derived from data.
Module 01: Practice Quiz: What makes Someone a Data Scientist?
Q1. You have the task of defining the role of a data scientist for a retail company that seeks to improve its product offerings and marketing strategies. In this context, a data scientist would primarily engage in which activity?
Answer: Analyzing customer purchase data to identify trends and tailor product recommendations.
A data scientist in this context would focus on analyzing customer data to find insights that can help optimize product offerings and marketing strategies.
Q2. What is a key characteristic that defines a data scientist?
Answer: A curious mind, fluency in analytics, and effective communication of findings characterize a data scientist.
Data scientists are defined not just by technical skills, but also by their curiosity, ability to analyze data effectively, and communicate their insights clearly.
Q3. Dr. Vincent Granville defines a data scientist as someone who relies solely on statistical models for data analysis.
Answer: False.
Dr. Vincent Granville suggests that data scientists should integrate a variety of methods, including machine learning and other approaches, not rely solely on statistical models.
Module 01: Graded Quiz: What Data Scientists Do
Q1. You are a new data scientist. You have been tasked with coming up with a solution for reducing traffic congestion and improving transportation efficiency. How would you go about it?
Answer: Gather and analyze streetcar operations data and identify congested routes.
A data scientist would gather relevant data, such as traffic patterns, to analyze which routes are most congested and suggest data-driven solutions for improving transportation efficiency.
Q2. Imagine you take a taxi ride where the initial fare is a fixed amount, and the fare increases based on both the distance traveled and the time spent in traffic. Which concept in data analysis does this scenario closely resemble?
Answer: Regression analysis.
The taxi fare calculation based on distance and time spent in traffic involves regression analysis, where you model the relationship between various factors (distance, time) and the fare.
Q3. You have to pick a file format which meets the following conditions: a) is self-descriptive for internet-based information sharing b) readable by both humans and machines c) Facilitates easy data sharing between different systems. Which file format would you pick?
Answer: JavaScript Object Notation (JSON).
JSON is self-descriptive, machine-readable, and commonly used for data exchange between systems, making it an ideal choice for internet-based information sharing.
Module 02: Practice Quiz: Data Mining
Q1. What is one of the key considerations when setting up goals for data mining?
Answer: The level of accuracy expected from the results.
When setting up goals for data mining, it is essential to determine the accuracy level expected from the results, as this will guide the approach, algorithms, and validation methods used in the project.
Q2. What is the purpose of data preprocessing in data mining?
Answer: To ensure the integrity of data, deal with missing data, and remove irrelevant attributes.
Data preprocessing is critical in data mining as it prepares the data for analysis by handling missing values, cleaning errors, and eliminating unnecessary features, ensuring high-quality input for modeling.
Q3. What is the purpose of evaluating data mining results?
Answer: To conduct an “in-sample forecast” to test the predictive capabilities of models.
Evaluating data mining results involves testing the predictive accuracy of models by comparing predictions to actual outcomes, ensuring that the model performs well and is ready for real-world application.
Module 02: Practice Quiz: Big Data and Data Mining
Q1. What was the key discovery made by the Houston Rockets NBA team through the analysis of video tracking data?
Answer: Two-point dunks from inside the two-point zone and three-point shots from outside the three-point line provided the best opportunities for high scores.
Through the analysis of video tracking data, the Houston Rockets discovered that focusing on efficient plays like two-point dunks and three-point shots yielded the best scoring opportunities.
Q2. What is one of the key advantages of using the Cloud for data scientists?
Answer: It allows collaboration among multiple teams on the same data.
The cloud enables data scientists and teams to easily collaborate and work on the same datasets, making it an efficient tool for distributed tasks and real-time analysis.
Q3. What is Hadoop primarily known for in the context of handling data?
Answer: Data storage.
Hadoop is primarily known for its ability to store and process large volumes of data across distributed systems, making it a key tool for big data handling.
Module 02: Graded Quiz: Big Data and Data Mining
Q1. What key benefit does cloud computing offer users, particularly in contrast to traditional software installations on their local computers?
Answer: Users can access the latest version of applications without purchasing retail copies.
Cloud computing allows users to always have access to the most up-to-date software, without needing to buy or install new versions locally.
Q2. What are the primary advantages of using cloud for data scientists?
Answer: The Cloud enables data scientists to work with large datasets and deploy advanced computing algorithms and tools available centrally.
Cloud computing offers scalable storage and computing power, making it easier for data scientists to analyze large datasets and run complex algorithms.
Q3. What are the common characteristics of Big Data, often called the “V’s of Big Data”?
Answer: Velocity, Volume, Variety, Veracity, and Value
These five “V’s” describe key attributes of Big Data: how fast data flows (Velocity), how much data there is (Volume), the diversity of data types (Variety), the accuracy and trustworthiness of data (Veracity), and the usefulness or potential insights derived from data (Value).
Q4. How has the interest in data science and business analytics changed over the last few years, and what is the impact on undergraduate courses in this field?
Answer: Interest in data science and business analytics has increased, leading to a growing number of students enrolling in related undergraduate courses.
With the growing importance of data-driven decision-making, more students are pursuing degrees in data science and analytics.
Q5. Which open-source technology provides distributed storage and processing of big data, allowing scalability and support for various data formats?
Answer: Apache Hadoop
Apache Hadoop is an open-source framework that supports the distributed storage and processing of large datasets, offering scalability and flexibility to handle various data formats.
Module 02: Practice Quiz: Regression
Q1. What are some examples of questions that can be addressed using regression (hedonic) models in the context of housing prices?
Answer: How much does the size of a lot influence housing prices?
Hedonic regression models can help understand the impact of various factors, such as lot size, on housing prices.
Q2. What is the primary purpose of regression hedonic models in the context of housing analysis?
Answer: To analyze the relationships between various factors and housing prices.
Hedonic models are used to quantify how different variables (e.g., lot size, location, amenities) influence the price of a house.
Q3. You are ready to buy a house. However, you wonder, “Do houses located near high-voltage power lines sell for more or less than the rest?”
This question can be addressed using regression analysis.
Answer: True
Regression analysis can help determine how proximity to high-voltage power lines impacts housing prices.
Module 02: Practice Quiz: Deep Learning and Machine Learning
Q1. Imagine you’re working on an AI project that involves creating new content such as images, music, and language. Which artificial intelligence technology would you be primarily focused on?
Answer: Generative AI
Generative AI focuses on creating new content such as images, music, and language, which is what you’re working on.
Q2. What sets deep learning apart from traditional neural networks?
Answer: Multiple layers of neural networks
Deep learning uses multiple layers of neural networks to model complex data patterns, distinguishing it from traditional neural networks, which may have fewer layers.
Q3. In the realm of machine learning, what significant application involves the task of predicting items of interest for users based on their past interactions or behaviors?
Answer: Recommender systems for personalized content suggestions
Recommender systems analyze past behaviors and interactions to suggest content that aligns with users’ interests.
Module 02: Graded Quiz: Deep Learning and Machine Learning
Q1. What is the concept that refers to data sets of massive scale, rapid generation, and diverse types that challenge traditional analysis methods like those used in relational databases?
Answer: Big data
Big data refers to large, fast, and diverse datasets that require advanced techniques and tools beyond traditional relational databases.
Q2. How does Generative AI contribute to addressing the challenges faced by data scientists, researchers, and analysts when exploring significant data patterns and insights?
Answer: By enabling the derivation and evaluation of hypotheses from diverse data sources
Generative AI helps in exploring and generating hypotheses by working with large and diverse datasets to model complex data patterns and produce valuable insights.
Q3. Imagine you’re working with generative AI to create new instances of data that resemble your original dataset’s patterns. Which model would you choose as the foundational deep learning approach for this task?
Answer: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
GANs and VAEs are commonly used for generating new data instances based on patterns learned from original datasets.
Q4. Which technology is characterized by its ability to learn patterns on its own, such as distinguishing between objects like cats and dogs, and even generating speech that sounds like a learning baby?
Answer: Deep Learning
Deep learning models are known for their ability to autonomously learn complex patterns, such as recognizing images or generating speech.
Q5. In the context of fintech, what is a common application of machine learning that resembles the recommendation system used by Netflix?
Answer: Predictive Analytics
In fintech, predictive analytics is used to forecast future trends or behaviors, similar to how Netflix predicts user preferences based on past behavior.
Module 03: Practice Quiz: The Final Deliverable
Q1. What is the ultimate purpose of analytics in the context of delivering insights and findings?
Answer: To summarize findings in tables and plots for communication
The goal of analytics is to distill insights into clear and communicable findings that can guide decision-making.
Q2. What is the primary advantage of utilizing big data clusters?
Answer: They allow for easy distribution and parallel data processing
Big data clusters are designed to distribute data across multiple nodes, enabling parallel processing, which makes it easier to handle and analyze large datasets efficiently.
Q3. What is the key message in the Deloitte report, “United States Economic Forecast”?
Answer: The report presents a positive view of the robustness of the U.S. economy
Deloitte’s U.S. Economic Forecast typically highlights strengths in the economy, indicating positive outlooks on growth, employment, and other economic indicators.
Module 03: Practice Quiz: Data Science Application Domains
Q1. How does Data Science improve patient care in healthcare?
Answer: By recommending appropriate tests, trials, and treatments for patients using predictive analytics
Data science utilizes predictive analytics to identify trends in patient data, which can help in suggesting more personalized and effective treatments.
Q2. Imagine you’re an e-commerce company looking to enhance customer experiences and boost sales. How could data science help you achieve this goal?
Answer: By using algorithms to suggest products based on individual customer preferences
Data science leverages algorithms to personalize product recommendations, improving customer experience and boosting sales.
Q3. As a manager of an online fashion store, you’re concerned about customers adding items to their cart but not completing purchases. How can data science assist you in solving this problem and boosting conversion rates?
Answer: Analyzing customer behavior to find reasons for cart abandonment and applying strategies to address them
Data science can identify patterns and reasons behind cart abandonment, enabling the company to implement strategies to recover sales, such as targeted reminders or discounts.
Module 03: Graded Quiz: Data Science Application Domains
Q1. You’ve recently started a small manufacturing business. You’ve been focused on production but realize the importance of data for better decision-making. What should be your initial step to harness the power of data science to improve your operations?
Answer: Document your existing data collection and archiving practices
The first step is to assess the current data collection and storage processes to ensure the business is collecting reliable, useful data. This foundation is crucial before diving into more advanced data science applications.
Q2. Imagine you are a business executive looking to harness the power of data science to gain a competitive advantage for your company. After hearing about the impact of data science and big data on businesses, what key takeaway can you gather from the example of Netflix’s success through data analysis?
Answer: Analyzing customer preferences and behaviors can lead to a competitive advantage
Netflix’s success demonstrates how understanding customer behaviors and preferences through data analysis can help create personalized experiences, leading to a significant competitive advantage.
Q3. In the realm of healthcare, how do data science and predictive analytics contribute to improving patient outcomes and assisting physicians?
Answer: Data science systems ensure that all physicians have access to the latest information about diseases and treatments
Data science tools help physicians by providing real-time, evidence-based insights that support timely and informed decisions, improving patient outcomes.
Module 03: Practice Quiz: The Report Structure
Q1. When deciding on the structure of a report, what factors should be considered?
Answer: The length of the document and its purpose
The report’s structure should reflect its intended purpose (e.g., to inform, analyze, or persuade) and the length, ensuring the content is organized appropriately for the audience.
Q2. What does ETL stand for in the context of data processing?
Answer: Extract, Transform, Load
ETL is a process used in data integration where data is extracted from various sources, transformed into a suitable format, and loaded into a destination database.
Q3. What is the purpose of including a table of contents (ToC) in a report, even if it’s relatively short (five or fewer pages)?
Answer: To offer a glimpse of the document’s structure
The ToC provides an overview of the report’s organization, helping readers quickly locate specific sections or topics within the document.
Module 03: Practice Quiz: Careers and Recruiting in Data Science
Q1. What is one key foundational skill required for someone entering a data science team?
Answer: Programming skills, algebra, geometry, calculus, basic probability, basic statistics, and knowledge of relational databases.
A well-rounded data scientist needs a combination of programming skills and foundational mathematics/statistics to work with data effectively.
Q2. Per the expert Dr. Murtaza Haider, what crucial attribute should be prioritized in candidates when forming a data science team?
Answer: Passion and curiosity, particularly about the specific business domain.
Dr. Haider emphasizes that passion and curiosity for the domain are critical, as they drive the motivation for solving real-world problems through data science.
Q3. Based on the Careers in Data Science video, which career has been ranked number one among the most promising jobs since 2016?
Answer: Data science.
Data science has consistently been ranked as one of the top career choices due to its growth and impact across various industries.
Module 03: Graded Quiz: Careers and Recruiting in Data Science
Q1. What are some fundamental skills and knowledge areas that individuals should possess when aspiring to become data scientists?
Answer: Proficiency in programming, algebra, geometry, calculus, probability, statistics, and database concepts.
Data scientists need a broad set of skills, including programming, mathematical knowledge, and an understanding of data management.
Q2. You are responsible for hiring a data scientist for your e-commerce company. What is your primary consideration when assessing potential candidates?
Answer: You evaluate their problem-solving abilities and analytical thinking.
While technical skills are important, a data scientist’s ability to approach and solve complex problems creatively is essential.
Q3. Which of these qualities would make you a successful data scientist?
Answer: Programming skills, math knowledge, curiosity, and experimentation.
These qualities are crucial for exploring data, deriving insights, and applying them in practical contexts.
Module 03: Quiz Based on Case Study
Q1. Why did Lila focus on communication and storytelling skills?
Answer: To communicate her findings effectively as a data scientist.
Lila recognized that being able to communicate insights clearly is essential for conveying the results of her data analysis to non-technical stakeholders.
Q2. What is the importance of domain knowledge in data science?
Answer: It allows you to apply data science skills effectively in a specific field.
Domain knowledge helps a data scientist understand the context and challenges of the industry they are working in, making their analysis more relevant and impactful.
Q3. What key skills did Lila acquire during her data science education?
Answer: Statistics, machine learning, data analysis.
Lila’s education focused on core data science skills, enabling her to analyze data, build models, and apply statistical methods to derive insights.
Q4. What sources did Lila explore to procure data for her data science project?
Answer: Various repositories, websites, and databases.
Lila used a variety of data sources to gather the information necessary for her project, ensuring a comprehensive data set.
Q5. What does Lila do at the end of her first project as a junior data scientist to effectively convey insights and recommendations to stakeholders?
Answer: She creates a comprehensive report or presentation.
To communicate her findings effectively, Lila creates a detailed report or presentation that clearly presents her insights and recommendations to stakeholders.
All Quiz Answers of multiple Specializations or Professional Certificate programs:
Course 1: What is Data Science?
Course 2: Tools for Data Science
Course 3: Data Science Methodology
Course 4: Python for Data Science, AI & Development
Course 5: Python Project for Data Science
Course 6: Databases and SQL for Data Science with Python
Course 7: Data Analysis with Python
Also, Learn >> What is data science?
