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Struggling with the ‘What is Data Science?’ quizzes on Coursera’s IBM Specialization? This post offers helpful tips and resources to guide you through the course material, not just answer keys. Learn the concepts and ace the quizzes yourself!

In today’s environment, we use Data Science to identify patterns in data and draw meaningful, data-driven conclusions and predictions.
This course is designed for everyone and covers topics such as how data scientists utilize machine learning and deep learning, as well as how businesses employ data science.

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You will meet numerous data scientists who will share their knowledge and experiences with data science. By taking this introductory course, you will begin your path into this growing industry.

There are four modules in the course “What is Data Science”:

1. Defining Data Science and What Data Scientists Do
2. Data Science Topics
3. Applications and Careers in Data Science
4. Data literacy for Data Science (Optional)

Week 1 – What is Data Science

Week 1 All Quiz – What is Data Science

1. You are a data scientist about to start a new project. What would one of your key roles be?

• Focusing solely on data visualization
• Asking questions to clarify the business need
• Designing data collection methods
• Collecting vast quantities of data from varied sources

2. When did the term “data science” come into existence and who is credited with coining the term?

• 1960s, no specific person credited
• 2009-2011, DJ Patil or Andrew Gelman
• 1990s, DJ Patil and Andrew Gelman
• Early 2000s, led by business analysts

3. As an aspiring data scientist, what primary qualities should you possess to succeed in the field?

• Curiosity and storytelling skills.
• Proficiency in analytics platforms and software.
• Extensive experience with data analysis software.
• Strong expertise in a specific industry.

4. 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?

• Suggest creating more parking lots and garages in the city
• Suggest implementation of surge charges for ride-sharing services.
• Gather and analyze streetcar operations data and identify congested routes
• Suggest implementation of strict speed limits and traffic fines

5. 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?

• Regression analysis
• Unstructured data extraction
• Nearest neighbor algorithm
• Data visualization with R

6. 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?

• Delimited text file formats (CSV/TSV)
• Extensible Markup Language (XML)
• JavaScript Object Notation (JSON)
• Microsoft Excel Open XML Spreadsheet (XLSX)

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

Answer – False

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

Answer – False

9. 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

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

Answer – $112,000

11. 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.

Answer – False

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

Answer – True

Week 2 – What is Data Science

Week 2 All Quiz – What is Data Science

1. What key benefit does cloud computing offer users, particularly in contrast to traditional software installations on their local computers?

• Users have more control over their applications in the cloud.
• Users can access the latest version of applications without purchasing retail copies.
• Cloud computing eliminates the need for users to store any data locally.
• Cloud computing requires users to purchase and install their own applications locally.

2. What are the primary advantages of using cloud for data scientists?

• The Cloud enables data scientists to work with large datasets and deploy advanced computing algorithms and tools available centrally.
• The Cloud offers up-to-date tools and libraries for data scientists but restricts access to specific time zones.
• The Cloud allows data scientists to store data locally and use advanced computing algorithms.
• The Cloud can only be accessed from laptops, not tablets or phones.

3. What are the common characteristics of Big Data, often called the “V’s of Big Data”?

• Volume, Vector, and Verification
• Vision, Velocity, and Visualization
• Velocity, Volume, Variety, Veracity, and Value
• Variety, Verification, and Value

4. 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?

• Interest in data science has shifted primarily to parents rather than students.
• Interest in data science has declined, leading to a decrease in enrollment in undergraduate courses.
• Interest in data science and business analytics has increased, leading to a growing number of students enrolling in related undergraduate courses.
• Interest in data science has remained constant, with no significant changes in enrollment in related undergraduate courses.

5. Which open-source technology provides distributed storage and processing of big data, allowing scalability and support for various data formats?

• Apache Hive
• Apache Spark
• NoSQL databases
• Apache Hadoop

6. 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?

• Data mining
• Machine learning
• Big data
• Deep learning

7. How does Generative AI contribute to addressing the challenges faced by data scientists, researchers, and analysts when exploring significant data patterns and insights?

• By replacing the role of data scientists in analyzing data patterns
• By speeding up the traditional manual analysis process
• By automating the process of collecting and preprocessing data
• By enabling the derivation and evaluation of hypotheses from diverse data sources

8. 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?

• Linear Regression
• Decision Trees
• Neural Networks
• Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)

9. 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?

• Linear Algebra Transformations
• Deep Learning
• Traditional Neural Networks
• Basic Pattern Recognition

10. In the context of fintech, what is a common application of machine learning that resembles the recommendation system used by Netflix?

• Recommendations
• Cluster Analysis
• Precision and Recall
• Predictive Analytics

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

Answer – False

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

Answer – Data Visualization

13. “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:

Answer – In-sample Forecast

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

Answer – True

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

Answer – True

16. “What are typical land taxes in a house sale?” is a question that can be put to regression analysis.

Answer – False

Week 3 – What is Data Science

Week 3 All Quiz – What is Data Science

1. 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?

• Begin hiring a team of data scientists.
• Invest in expensive data analytics software.
• Document your existing data collection and archiving practices.
• Increase your advertising budget to attract more customers.

2. 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?

• Analyzing customer preferences and behaviors can lead to a competitive advantage.
• The key to success in data science is collecting as much data as possible.
• Implementing wearable devices is the most effective way to collect data for analysis.
• Data science primarily benefits online shopping websites like Amazon.

3. In the realm of healthcare, how do data science and predictive analytics contribute to improving patient outcomes and assisting physicians?

• Data science in healthcare primarily focuses on gene markers and environmental factors.
• Data science in healthcare is limited to basic descriptive analytics.
• Data science systems ensure that all physicians have access to the latest information about diseases and treatments.
• Data science tools mainly benefit oncologists in recommending specific tests for patients.

4. What are some fundamental skills and knowledge areas that individuals should possess when aspiring to become data scientists?

• Proficiency in programming, algebra, geometry, calculus, probability, statistics, and database concepts.
• Extensive computer science theory and mathematics beyond calculus.
• A strong background in physics and statistics.
• A background in computer science with a focus on databases.

5. You are responsible for hiring a data scientist for your e-commerce company. What is your primary consideration when assessing potential candidates?

• You evaluate their problem-solving abilities and analytical thinking.
• You assess their knowledge of mathematical and statistical concepts.
• You prioritize candidates who demonstrate curiosity, have a sense of humor, storytelling ability, and show a passion for the e-commerce industry.
• You focus on their proficiency in specific data analysis tools and programming languages.

6. Which of these qualities would make you a successful data scientist?

• Familiarity with the latest web development frameworks.
• Expertise in front-end development and user interface design.
• Fluency in multiple programming languages.
• Programming skills, math knowledge, curiosity, and experimentation.Upgrade to submitLikeDislikeReport an issue

7. Why did Lila focus on communication and storytelling skills?

• To communicate her findings effectively as a data scientist
• To become a writer
• To improve her public speaking abilities
• To impress her friends

8. What is the importance of domain knowledge in data science?

• It helps you become a software developer.
• It allows you to apply data science skills effectively in a specific field.
• It helps you become a better e-commerce website developer.
• It’s not important at all.

9. What key skills did Lila acquire during her data science education?

• History and literature knowledge
• Statistics, machine learning, data analysis
• Music and art appreciation
• Cooking and gardening

10. What sources did Lila explore to procure data for her data science project?

• Product professionals, data engineers, and domain specialists
• Websites and libraries
• Various repositories, websites, and databases
• Only repositories and databases

11. What does Lila do at the end of her first project as a junior data scientist to effectively convey insights and recommendations to stakeholders?

• She shares raw data with stakeholders.
• She organizes a meeting with stakeholders.
• She writes a blog post.
• She creates a comprehensive report or presentation.

Week 3 Final Quiz: What is Data Science

1. Which of the following statements best describes what data science is about?

• Data Science is just about studying data, similar to how biological sciences is about studying biology.
• Data Science is the process of using data to understand different things, validate hypotheses, and uncover insights and trends hiding behind data.
• Data Science was defined in the 80s and 90s and is only about exploring questions and finding answers.
• Data Science is only about using algorithms to analyze large datasets.

2. When did data science emerge as a recognized, established field?

• In the 1980s
• In the 1990s
• In the 2010s
• In the 1970s

3. Why do experts estimate that millions of jobs in data science might remain vacant?

• Not enough schools are teaching statistics.
• People prefer working in the service industry.
• There is a lack of readily available talent.
• A job in data science doesn’t pay well.

4. According to the video ‘Advice for New Data Scientists,’ which quality is an absolute must for aspiring data scientists?

• Being Argumentative
• Comfort with analytics platforms
• Ability to tell a great story
• Curiosity

5. In the video “How Big Data is Driving Digital Transformation,” whose support is crucial for successful digital transformation?

• Lead developers
• External stakeholders
• Chief Executive Officer and others
• Marketing department

6. What advantage does cloud computing offer to data scientists?

• Makes it so they do not need to understand statistics
• Keeps their data private
• Provides more computing power than a single physical machine
• Provides a quicker way to digitize analog data

7. What type of data is not organized in a predefined way, like email?

• Variety data
• Structured data
• Velocity data
• Unstructured data

8. Which of the following best describes the Hadoop software?

• A programming language
• A word processor
• An open-source statistical tool
• A collection of tools providing distributed storage and processing of big data

9. Which term can be described as “a collection of small computing units that take inspiration from the biology of the human brain and take incoming data to learn to make decisions over time.”

• Artificial neural networks
• Nervous system
• Hard drive
• Data science

10. When data mining, which statement is true?

• The statistics used in a data mining exercise largely depend on the quality of the data.
• The hypothesis of a data mining exercise largely depends on the quality of the data.
• The success of a data mining exercise largely depends on the quality of the data.
• The programming language used largely depends on the quality of the data.

11. What is the primary focus of generative AI models?

• To determine business needs
• To replace a need for curiosity
• Create new instances that replicate the underlying distribution of data
• Create a story about the data for the stakeholders

12. Which of the following is an example of a data science application?

• Data storage
• Recommendation engine
• Mimic the human brain
• SpreadsheetsUpgrade to submitLikeDislikeReport an issue

Week 4 – What is Data Science

Week 4 All Quiz – What is Data Science

There are no quizzes or assignments in week 4.