Business Intelligence Concepts Tools And Applications Quiz Answers

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Business Intelligence Concepts Tools And Applications Quiz Answers – Coursera Google Data Analytics Professional Certificate Course 6: Sharing Data through the Art of Visualization – Visualize Data quiz answers for all weekly questions (weeks 1 – 4):

Data Visualization is the graphical representation of data. In this part of the course, accessibility; We’ll introduce key concepts, including design thinking and other factors that help visualize data in your analysis.

Business Intelligence Concepts Tools And Applications Quiz Answers

After analyzing their data; A junior analyst creates bar graphs, charts, and graphs to help explain findings to stakeholders. Creates line graphs and pie charts. What are all these examples of?

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Causation occurs when an action leads directly to a result. Causality describes a clear cause-and-effect relationship between variables. Question 4

According to David McCandless, there are four elements of effective data visualization. They include information; the story Includes goals and vision style. L3 Data Mapping Design Question 1

Any design element can add visual style to your data and help you build structure for your visualization.

Fill in the blank: Design thinking is a process used to solve complex problems in a _ way.

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Design thinking is a process used to solve complex problems in a user-centered way. This allows the data analyst to develop alternative strategies for visualization. Question 4

While creating data visualizations for your stakeholders, you realize that certain colors can make it more difficult for your audience to understand your data. Which stage of the design process does it represent?

Considering the appropriate colors for the visual sense is part of the empathic design phase. During the empathy phase, You consider the emotions and needs of the target audience in your data visualization. Explore L4 conceptualization considerations Question 1

Three basic visual considerations are headings; Subtitles and labels. They all contain text but are formatted differently for different purposes. Question 2

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Labeling the data visualization directly helps viewers identify the data more efficiently. Legends are often less effective because they are positioned away from the data.

Labeling the data visualization directly helps viewers quickly identify the data. Legends are often less effective because they are positioned away from the data. Question 3

Alternative text provides a textual alternative to non-textual content. Alt text ensures that users who need to visualize your data in different ways, such as a screen reader, still absorb the information. Question 4

You want to make sure that you’re visualizing the data and that it’s usable. What strategies do you use to simplify the vision? Select all that apply.

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Simplifying your data visualizations can help you understand your audience and focus on the data that matters. To do this, Avoid overly complex images and unnecessary information. Weekly Challenge 1 Question 1

A data analyst wants to create a visualization that demonstrates how often data values ​​fall within certain ranges. What type of data visualization should I use?

To demonstrate how many data values ​​fall within certain ranges; A data analyst should use a histogram. Question 2

A data analyst notices that two variables in their data are rising and falling at the same time. They recognize that these variables are somehow related. What is this an example of?

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When a data analyst notices two variables rising and falling at the same time. This is an example of correlation. Correlation measures the degree to which two variables change in relation to each other. Question 3

Fill in the blank: A data analyst creates a presentation for stakeholders. They include _ visualizations because you want them to be interactive and change automatically over time.

What are the key elements of effective visualization that you should focus on when creating a data visualization? Select all that apply.

There are five stages of the design process: empathy; setting visualization, Prototyping and testing. The Ideate phase is when you start generating your data visualization ideas. Question 7

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Data analysts want to make their visualizations more accessible by adding textual explanations directly to the visualization. What do you call this?

It is labeling. Labeling the data directly instead of relying on legends makes data visualizations more accessible. Question 8

Identifying the components of your data visualization makes content easier. This can help make them more accessible to visually impaired audience members. What are some methods that data analysts use to distinguish elements?

Data analysts distinguish between foreground and background, and separate elements of data visualization by using contrasting colors and shapes. Week 2: Creating data visualizations with Tableau.

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Tableau is a tool that helps data analysts create effective data visualizations. In this part of the course, You’ll learn all about Tableau and explore the importance of creativity and clarity as you visualize your data analysis findings.

As a business intelligence and analytics platform; What can Tableau help you do with data? Select all that apply.

Tableau lets you observe and understand data to make decisions. databases; statistics, or linking data in CSV files; Interactive dashboards can be created and shared with data. Question 2

You’re comparing Tableau to Looker and Google Data Studio for your company’s data visualization needs. What’s so special about Tableau?

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Although Tableau uses Looker and Google Data Studio are strictly browser-based. While desktop solutions are suitable for companies that maintain services on their private networks, desktop solutions are preferred by companies that adopt cloud-based services. Question 3

Tableau Public’s Gallery features data visualization examples created by other Tableau Public users from around the web. L3 Create visualizations in Tableau Question 1.

A different color layer in Tableau shows a value by color intensity and the scale of the value by hue. Although the color palette is not used to distinguish the accuracy or reliability of the data, you should always check that the values ​​you use in a visualization are accurate and reliable. Question 2

A data analyst creates a Tableau visualization to compare trade (volume of goods and services exchanged) between the European Union (EU) and Australia. Which color choice can be misleading?

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Many people associate green with positive outcomes and red with negative outcomes. Green may misrepresent a trade surplus for the EU and red may misrepresent a trade deficit for Australia. Choosing beige and purple won’t lead people to the wrong conclusions. Question 3

Reducing the number of country labels and using only one font can make the visual more effective. Doing this makes the labels easier to read. Weekly Challenge 2 Questions 1

Fill in the blank: When using Tableau; People can control the data they see with imagination. Here is an example of Tableau being.

Tableau is an interactive example where people can control the data they see. Question 2

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A data analyst is using the Color tool in Tableau to apply a color scheme to a data visualization. They use a high-contrast color scheme because they want to make vision accessible to people with color vision deficiency. What is the opposite?

The data analyst ensures that there is contrast in the color scheme so that the vision is accessible to people with color vision impairment. Question 3

While looking at an object, they are using the Pan tool to rotate the view. Question 5

You are working with World Happiness data in Tableau. Which Marks shelf tool do you use to show the population of each country on a map?

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When working with World Happiness data in Tableau; What can you do with the Filter tool?

You can use the Filter tool to show only countries with a global happiness score of 3.5 or below. Question 7

Typically, All visualizations you create using Tableau Public are available to other users. Click any icon to hide the visualization.

Fill in the blank: In Tableau; The _ palette displays a range of two values. It uses a color to show the distance to a data point and a color intensity to show its magnitude.

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In Tableau, The variable palette shows two ranges of values. It uses a color to show the distance to a data point and a color intensity to show its magnitude. Week 3: Creating data stories.

Connecting your goals to data through insights is essential to data storytelling. In this part of the course, You’ll learn about data-driven stories and their attributes. You’ll also understand how to use Tableau to create dashboards and dashboard filters.

Data storytelling involves communicating the meaning of a dataset through visuals and using a narrative that is customized to your audience. Question 2

The data analyst presents their data story to the audience. They are meant to grab the audience’s interest and attention. Which data storytelling concept does this describe?

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Spotlighting involves scanning through data to quickly identify the most important insights. By identifying a wide range of ideas and identifying the concepts that appear to occur on the white board. Use the L3 Tableau Dodash Board

Fill in the empty: A database is a collection of data from several data pairs to a central location. It makes the information _. Select whatever it relates.

To track the information; The Dashboard is used to analyze and to be visible. You didn’t choose all the right answers in question 2.

The data analysts choose them.

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