PRACTICE QUIZ

Structured thinking involves recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options.

This describes the share phase of the data analysis process.

This is an example of reaching your target audience. In this scenario, people who read landscaping magazines are the target audience because they’re likely to be interested in shopping at the garden center.

A data analyst at a shoe retailer using data to inform the marketing plan for an upcoming summer sale is an example of making predictions.

Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes or classifications.

A vague question usually out of context and too broad to lead to a useful response.

The number of customers who responded to the promotion can be counted, making this question measurable.

A common example of an unfair question is one that makes assumptions. These are questions that assume the answer to the question being asked.

Structured thinking involves recognizing the current problem or situation you’re facing and identifying your options.

The share phase of the data analysis process typically involves communicating findings, summarizing results using data visualizations, and creating a slideshow to present to stakeholders.

Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes.

The question, “How could we improve our website to simplify the returns process for our online customers?” is action-oriented because it’s likely to result in specific answers that would lead to change.

The analyst is in the share phase of the data analysis process.

To better reach their target audience, they can advertise at a bus stop near a local culinary school, on a podcast for foodies, and on TV during the season finale of The Best Chef in the Universe. A target audience is the people you’re trying to reach. In this scenario, people who enjoy food and cooking are the store’s target audience.

This is an example of making predictions. Making predictions deals with making informed decisions about how things may be in the future.

Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes.

Closed-ended questions don’t encourage people to elaborate and share valuable details.

These questions are action-oriented. That means they’re more likely to result in specific answers that can be acted on to lead to change.

Questions that make assumptions often involve concepts that are formed without evidence. For example, an idea that is accepted as true without proof.

This is an example of making predictions. Making predictions deals with making informed decisions about how things may be in the future.

A common example of an unfair question is one that makes assumptions. Unfair questions assume the respondent’s answer to the question.

A recycling center sponsoring a podcast about saving the environment is an example of reaching a target audience. In this scenario, people who care about the environment are likely to be interested in recycling.

In data analytics, an algorithm is defined as a process or set of rules to be followed for a specific task.

In data analytics, qualitative data is subjective and measures qualities and characteristics.

Live data is a continuous source of incoming information.

A pivot table is a data summarization tool used to sort, reorganize, group, count, total, or average data.

A metric is a single, quantifiable type of data used when setting and evaluating goals.

A metric goal is measurable and evaluated using single, quantifiable data.

Return on investment compares the cost of an investment to the net profit gained from that investment.

Small data is specific and concerns a short time period. Big data is less specific and concerns a longer time period.

Static data is data that doesn’t change once it’s been recorded.

A data analyst using data to address a large-scale problem would most likely require big data that reflects change over time.

A process or set of rules to be followed for a specific task describes an algorithm.

In data analytics, quantitative data is specific and measures numerical facts.

Pivot tables are used to reorganize, group, and calculate totals from data.

A metric is a single, quantifiable type of data used when setting and evaluating goals.

In data analytics, a process or set of rules to be followed for a specific task is an algorithm.

Dashboards monitor live, incoming data from multiple datasets; reports use static collections of data.

Using key performance indicators to measure revenue and using annual profit targets to set and evaluate goals are examples of using metrics.

If a data analyst compares the cost of an investment to the net profit of that investment over a period of time, they’re analyzing the return on investment.

To sort, reorganize, group, count, total or average data, data analysts use a pivot table.

A metric goal is a measurable goal set by a company that is evaluated using metrics.

Return on investment compares the cost of an investment to the net profit gained from that investment.

A metric goal is a measurable goal set by a company that is evaluated using metrics.

In spreadsheets, data analysts begin formulas with an equal sign (=).

Attributes are used to label the type of data in each column in a spreadsheet.

A spreadsheet could be used to predict next quarter’s sales.

Formulas are created by the user, whereas functions are preset commands in spreadsheets.

In the function =MAX(B5:B15), B5:B15 represents the range. A range is a collection of two or more cells.

The correct spreadsheet formula for multiplying cell K3 times cell K8 is =K3*K8. The asterisk (*) is the operator for multiplication.

Putting data into context helps data analysts eliminate bias.

Defining the problem domain is part of the structured-thinking process.

A spreadsheet could be used to maintain information about accounts.

In the function =MAX(G3:G13), G3:G13 represents the range. A range is a collection of two or more cells.

The correct spreadsheet formula for multiplying cell D5 times cell D7 is =D5*D7. The asterisk (*) is the operator for multiplication.

To avoid bias when collecting data, a data analyst should keep context in mind.

The labels that describe the type of data contained in each column of a spreadsheet are called attributes.

To determine an organization’s annual budget, a data analyst might use a spreadsheet.

Formulas are written by the user, and functions are already defined.

In the function, A1:A12 represents the range. A range is a collection of two or more cells.

The correct spreadsheet formula for multiplying cells H2 times cell H5 is =H2*H5. The asterisk (*) is the operator for multiplication.

A data analyst might use descriptive column headers in order to add context to the data.

Both formulas and functions in spreadsheets begin with an equal sign.

To label the type of data contained in each column in a spreadsheet, data analysts use attributes.

Formulas are instructions that perform specific calculations. And functions are preset commands that perform calculations. Formulas and functions assist data analysts in calculations, both simple and complex.

By negatively influencing data collection, bias can have a detrimental effect on analysis.

The primary stakeholder of this project is likely to be the vice president of finance, who can use this project’s findings to create an effective strategy for the future.

The customer-facing team includes anyone in an organization who interacts with customers or potential customers, such as the shoppers at a company’s retail store.

These questions enable data analysts to communicate clearly with stakeholders and team members.

Data analysts pay attention to sample size in order to represent a diverse set of perspectives and avoid skewed results or inaccurate judgements.

The analyst should respond saying they understand the vice president’s concerns, provide a status update, and let the vice president know when to expect the completed report. This shows the vice president that their concerns are understood and provides a status update.

Arriving at meetings prepared involves reading the agenda ahead of time, bringing materials to take notes with, and considering what questions you may be asked so you’re prepared to answer.

They should stay focused and attentive during the entire meeting. Listening and learning from others are great ways to learn about your company, its challenges, and its goals.

When you don’t understand the full context of a request, ask questions about the project goal, its data story, and the big picture vision.

The secondary stakeholders are most likely the data analyst and the project manager.

At an online marketplace, the customer-facing team includes anyone in an organization who interacts with current or potential shoppers.

Data analysts focus on sample size to make sure they have enough data so that a few unusual responses don’t skew results.

Even if the data analyst isn’t running the meeting, if their project is on the agenda, it’s a good idea to prepare to share updates and answer questions. This helps keep everyone informed and ensures effective communication.

When participating in an online meeting, it’s important to eliminate distractions, such as checking your email. This shows respect to the other participants.

To help shift a situation from problematic to productive, reframe the question, keep your emotions in check, and establish open lines of communication.

The vice president of finance is most likely to be the primary stakeholder.

These people are part of the customer-facing team. The customer-facing team includes anyone in an organization who interacts with customers or potential customers, such as the shoppers at a company’s retail store.

When leading an online meeting, acting professionally involves encouraging others to contribute, testing technology beforehand, and eliminating distractions.

You should reply to the email, asking if they can schedule a time to talk about this in person in order to allow both of you to share your perspectives. When people are feeling angry or emotional, it’s best to wait until things calm down. Then, give everyone the opportunity to share their perspectives.

The four key questions data analysts ask themselves when communicating with stakeholders are: Who is my audience? What do they already know? What do they need to know? And how can I communicate effectively with them?

They can prepare for an effective meeting by arriving on time, bringing materials to take notes with, and considering what project updates to share. COURSE CHALLENGE

You should send your supervisor a polite, concise email, asking them to confirm the meaning of WHM.

The data in column F (Survey Q6: What types of books would you like to see more of at Athena's Story?) is qualitative.

The visualization is called a pie chart.

Using historical data to make informed decisions about how things may be in the future is an example of making predictions.

Asking questions is a very important part of data analysis. The best course of action is to send your boss a polite, concise email, asking for the meaning of WHM.

Pie charts are effective at demonstrating the percentages of a whole, such as the percentage of customers who would be interested in purchasing books of different genres.

The correct syntax is =SUM(E2:E53). The SUM function adds the values of a range of cells. E2:E53 is the specified range.

It’s called a pie chart. Pie charts are effective at demonstrating the percentages of a whole, such as the percentage of customers who would be interested in purchasing books of different genres.

The quantitative data includes information from columns A, C, and D.

Professional emails use a polite greeting and closing, are free of typos, and are concise.

The qualitative data includes information from columns B, E, and F.

If you need information from the secondary stakeholders, you can ask the project manager and the data analytics coordinator.

In the SMART methodology, measurable questions can be quantified and assessed. This might : include a 1-to-5 scale or questions with ranked responses.

This situation presents an opportunity to communicate, collaborate, and foster positive working relationships.

Dashboards offer live monitoring of incoming data and enable stakeholders to interact with the data.

Return on investment is made up of two metrics: the net profit over a period of time and the cost of the investment. By comparing these two metrics, you can determine the profitability of the investment.

The secondary stakeholders responsible for managing the data are the data analytics coordinator and junior data analyst.

This situation presents an opportunity to communicate, collaborate, and foster positive working relationships.

Return on investment is made up of two metrics: the net profit over a period of time and the cost of the investment. By comparing these two metrics, you can determine the profitability of the investment.

This situation presents an opportunity to communicate, collaborate, and foster positive working relationships.

Dashboards offer live monitoring of incoming data and enable stakeholders to interact with the data.

If you need information from the primary stakeholder, you can ask the vice president of data and strategy.

Reports provide a snapshot of high-level, historical data and reflect data that’s already been cleaned and sorted.