1.4.2 Module 1 Quiz – Data Analytic Projects Quiz Exam Answers Full 100% 2023 2024
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A college has deployed next-generation analytics. Which question would most likely be answered by next generation analytics that could not be answered by traditional analysis tools?
- Which class has the most sections available?
- What are the 10 most expensive textbooks required in any class?
- Which 100 classes fill the fastest once open enrollment is available for the general population?
- Which teacher had the highest number of students enrolled in classes during the last year?
- Which class sections do students consider the most in the online environment without actually enrolling?
Answers Explanation & Hints:
The question that would most likely be answered by next generation analytics that could not be answered by traditional analysis tools is “Which class sections do students consider the most in the online environment without actually enrolling?”
Next-generation analytics refers to advanced techniques for data analysis that go beyond traditional methods. In this case, it implies the use of sophisticated algorithms and machine learning models to extract insights from complex data sets.
The question about which class sections students consider the most in the online environment without actually enrolling is difficult to answer using traditional analysis tools because it requires analyzing unstructured data such as student reviews, comments, and feedback on social media or other online platforms.
Next-generation analytics can process this unstructured data and identify patterns and trends that traditional analysis tools cannot. This type of analysis can help colleges improve their online course offerings, identify areas for improvement, and ultimately enhance the learning experience for their students.
The other questions listed can be answered using traditional analysis tools such as data querying, statistical analysis, and data visualization.
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A college uses a learning management system (LMS) to host lecture materials and provide access to assignments and assessments. A teacher uses the LMS to check activities completed by students in a particular course. Which data analytics method is being used?
- indicative
- predictive
- descriptive
- prescriptive
- proactive
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Answers Explanation & Hints:
The data analytics method being used in this scenario is descriptive.
Descriptive analytics refers to the analysis of historical data to gain insights and understand what has happened in the past. In this case, the teacher is using the LMS to check the activities completed by students in a particular course, which is an example of looking at historical data. The teacher is not trying to make predictions or take any action based on this data, but rather simply trying to understand what has happened in the past.
Indicative analytics is a type of analytics that provides information about the current state of a system, while predictive analytics uses historical data to make predictions about the future. Prescriptive analytics provides recommendations or actions to take based on the analysis of historical data. Proactive analytics is a term that is not commonly used in data analytics, but it can refer to taking preemptive actions to prevent future problems based on analysis of historical data.
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A large online ordering company collects the sales history of its customers. Using this historical data, the online company sends customized sales advertisements to a specific group of customers. Which data analytics method is being used?
- indicative
- predictive
- descriptive
- prescriptive
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Answers Explanation & Hints:
Prescriptive analytics involves the use of advanced analytical techniques and algorithms to make recommendations and predictions about what actions should be taken in the future to optimize business outcomes. It relies on a combination of historical data, current data, and external data sources to provide guidance on what actions to take, and it aims to provide the best possible outcome by considering multiple scenarios and potential decision pathways.
In the case of the online ordering company, the use of historical sales data to send customized sales advertisements to a specific group of customers is an example of prescriptive analytics. The company is using this data to prescribe a course of action (sending targeted sales ads) based on an analysis of historical data to improve the outcome (sales revenue) of its business. By analyzing the data and identifying patterns and trends, the company can make recommendations on what actions to take to optimize the outcome.
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What is an example of descriptive data analysis?
- predicted oil price increases
- control of highway traffic signals
- an hourly traffic report for a highway
- a weather forecast description
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Answers Explanation & Hints:
An example of descriptive data analysis is an hourly traffic report for a highway.
Descriptive analytics refers to the analysis of historical data to gain insights and understand what has happened in the past. In this case, an hourly traffic report for a highway is an example of descriptive data analysis because it provides information about the current state of the system (i.e. the traffic on the highway) based on historical data. The report describes the traffic conditions and provides information about what has happened in the past hour.
Predicted oil price increases is an example of predictive analytics, which involves using historical data and statistical models to make predictions about the future. Control of highway traffic signals and weather forecast description are examples of prescriptive analytics, which provides recommendations or actions to take based on the analysis of historical data.
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What is a benefit of analyzing data in motion rather than a static data set?
- Critical decisions can be made more quickly.
- The data is more accurate.
- All historical data is used to arrive at a decision.
- The data is more structured.
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Answers Explanation & Hints:
A benefit of analyzing data in motion rather than a static data set is that critical decisions can be made more quickly.
Analyzing data in motion refers to analyzing data as it is generated or received, rather than analyzing a static data set that has already been collected. Analyzing data in motion allows for real-time insights and decision-making, which is important in many industries where time is of the essence, such as finance, healthcare, and manufacturing.
In contrast, analyzing a static data set requires the data to be collected, processed, and then analyzed, which can take time and delay decision-making. Analyzing data in motion allows for quicker response times and more immediate action, which can be critical in situations where decisions need to be made quickly.
While analyzing data in motion can provide real-time insights, it is important to note that it may not always be more accurate than analyzing a static data set. In fact, analyzing data in motion can sometimes be less accurate due to issues such as data quality, data integrity, and data latency. The accuracy of the data depends on the quality and reliability of the data sources, as well as the algorithms used to analyze the data.
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Which type of results do data analysts calculate from larger datasets to inform decision making?
- converged
- numerous
- varied
- accurate
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Answers Explanation & Hints:
Accuracy is a crucial aspect of data analysis as it refers to the degree to which the insights, predictions, and recommendations derived from data are correct and reliable. Accuracy is achieved through several stages of the data analysis process, including data collection, cleaning, and preparation, as well as the application of appropriate analytical techniques and methods.
Data analysts work to ensure that the data they analyze is accurate by validating the data, removing errors, and ensuring that the data is relevant and representative of the population being studied. Analysts use statistical techniques to calculate measures of central tendency and variability, such as means, medians, and standard deviations, to assess the accuracy of the data and ensure that the results are reliable.
Accuracy is critical in decision making, as inaccurate data can lead to incorrect conclusions, flawed predictions, and misguided actions that can have negative consequences. Therefore, data analysts strive to produce results that are not only varied and insightful, but also accurate, so that decision makers can make informed decisions based on reliable data.
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What is a characteristic of predictive data analysis?
- establishes a future trend line based on past data
- uses simple math models
- uses a feedback system to track outcome of actions taken
- based solely on historical data
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Answers Explanation & Hints:
A characteristic of predictive data analysis is that it establishes a future trend line based on past data.
Predictive data analysis uses historical data and statistical models to make predictions about future trends and events. This involves analyzing patterns and relationships in the data to establish a future trend line, which can then be used to make predictions about future outcomes.
Predictive data analysis is typically more complex than simple math models, as it involves using advanced statistical techniques and machine learning algorithms to analyze the data. These techniques can include linear regression, time-series analysis, decision trees, and neural networks, among others.
While predictive data analysis is based on historical data, it is not solely based on historical data. It can also incorporate current data and real-time data streams to make more accurate predictions. Additionally, predictive data analysis can be used in a feedback system to track the outcome of actions taken, allowing for adjustments to be made and improving the accuracy of the predictions over time.
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Match the step in the Data Analysis Lifecycle with the description.
- Analyzing the Data ==> identifying patterns and correlations contained in the data to produce conclusions
- Preparing the Data ==> transforming the data into a format appropriate for analysis
- Getting the Data ==> locating the data and determining if there is enough data to complete the analysis
- Asking the Question ==> defining the problem to be solved or purpose of the analysis
- Presenting the Data ==> communicating insights derived from the analysis to decision makers
- Investigating the Data ==> determining if the data is valid, complete and relevant
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Answers Explanation & Hints:
These six steps comprise the Data Analysis Lifecycle and provide a framework for conducting effective data analysis. By following this process, data analysts can ensure that they are asking the right questions, using the appropriate data, and conducting rigorous analysis to produce actionable insights.
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What is the first step in any analytic project?
- defining the goal of the project
- finding relevant data for analysis
- creating representative charts and graphs
- making recommendations for changes
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Answers Explanation & Hints:
The first step in any analytic project is defining the goal of the project.
Before any data can be analyzed, it’s important to establish what problem you are trying to solve or what questions you are trying to answer. This involves clearly defining the goal of the project and identifying the specific business problem or research question that you are trying to address.
Once you have a clear goal in mind, you can then begin to collect and prepare the data that is relevant to the problem or question you are trying to answer. This may involve finding relevant data sources, cleaning and transforming the data to make it usable, and performing any necessary data quality checks.
After the data has been prepared, you can then move on to analyzing the data and creating charts and graphs to help visualize the results. Finally, based on your analysis, you can make recommendations for changes or improvements to the business process or research problem that you are investigating. But it all starts with defining the goal of the project.