Coursera Google Data Analytics Professional Certificate Course 2 – Ask Questions to Make Data-Driven Decisions quiz
answers to all weekly questions (weeks 1 – 4): You may also be interested in
Google Data Analytics Professional Certificate Course 1: Foundations – Cliffs Notes. This course begins by talking about problem solving and some of the common types of
business problems that data analysts help solve. To do the job of a data analyst, you need to ask questions and problem-solve. In this part of the course, you’ll check out some common analysis problems and how analysts solve them. You will also learn about effective questioning techniques that can help guide your analysis. 1.1. Problem solving and effective questioning Problem solving begins
with asking effective questions. From issue to action: The six data analysis phases are Ask, Prepare, Process, Analyze, Share, and Act. <In the ask step, we define the problem we’re solving and make sure that we fully understand
stakeholder expectations.> 1.2. Take action with data 1.2.1. Data in action (the data analysis process) A short data analytics case study shows how the six phases of data analysis can be applied to effective problem solving of real world problems: “Anywhere Gaming Repair” is a small business service provider that comes to you to fix your broken video game systems or accessories. “The owner wanted to expand his business. He knew advertising as a proven way to
get more customers, but he wasn’t sure where to start”–esp. regarding a) suitable media (so, who is the target audience and what media does it use?), and 2) the advertising budget (how much different advertising methods will cost). The business owner ask a data analyst, Maria, to make a recommendation. Step 1: Maria begins by defining the problem that needed to be solved–via collaboration with stakeholders and understanding their needs. Anywhere Gaming
Repair wants to figure out how to bring in new customers. So the problem is how to determine the best advertising method for its target audience. Collected data will answer this problem. Step 2: Next step was the prepare phase, Maria collected data for the upcoming analysis process. First she needed to better understand the company’s target audience, i.e., people with video game systems. After that, Maria collected data on the different advertising methods
… to determine which was the most popular one with the company’s target audience. Here a data analyst is trying to understand their target audience. <They’re asking questions such as, “How can learning more about my target audience help me figure out how to solve this problem?” and “What research do I need to do about my target audience?”> Step 3: Then she moved on to the process step. Here Maria cleaned the data to eliminate any errors or
inaccuracies or inconsistencies “that could get in the way of the result … when you clean data, you transform it into a more useful format, create more complete information and remove outliers.” Here an analyst asks questions such as, <“What data errors might get in the way of my analysis?” or “How can I clean my data so the information I have is consistent?”> Step 4: Then it was time to analyze. In this step, Maria wanted to find out two things. First,
who’s most likely to own a video gaming system? (People between the ages of 18 and 34.) Second, where are these people most likely to see an advertisement? (TV commercials and podcasts are very popular with people in the target audience.) Step 5: Next it is time for Maria to share her recommendation so the company could make a data-driven decision. “She summarized her results using clear and compelling visuals of the analysis. This helped her stakeholders
understand the solution to the original problem.” Step 6: Finally, Anywhere Gaming Repair took action, they worked with a local podcast production agency to create a 30 second ad about their services. The ad ran on podcast for a month, and it worked. They saw an increase in customers after just the first week. By the end of week 4, they had 85 new customers. 1.2.2.
From issue to action: The six data analysis phases 1.2.3. How the data analysis process works (example/short case study): “One of the problems that we’ve tackled here at Google is our Noogler onboarding program, which is how we onboard new hires.” <One of the
things that we’ve done is ask the question, how do we know whether or not Nooglers are onboarding faster through our new onboarding program than our old onboarding program where we used to lecture them. We worked really closely with the content providers to understand just exactly what does it mean to onboard someone faster? <Once we asked all the questions … we prepared the data by understanding who was the population of the new hires
that we were examining … who our sample set was, who our control group was, who our experiment group was, where were our data sources, and make sure that it was in a set, in a format that was clean and digestible for us to write the proper scripts for. <So the next step for us was to process the data to make sure that it was in a format that we could actually analyze in SQL, making sure that was in the right format, in the right columns, and in the right
tables for us. <To analyze the data, we wrote scripts in SQL and in R to correlate the data to the control group or the experiment group and interpret the data to understand, were there any changes in the behavioral indicators that we saw? <Once we analyze all the data, we want to report on it in a way that our stakeholders could understand … we prepared reports, dashboards and presentations, and shared that information
out. <Once all of our reports were complete, we saw really positive results and decided to act on it by continuing our project-based learning onboarding program.> 1.3. Solve problems with data Data analysts work with six basic problem types: 1.4. Craft effective questions We’re going to learn how to craft effective questions that lead to key insights you can use to solve all kinds of problems using the SMART framework, and how to ensure
that your questions are fair and objective. To start solving a problem, data analysts start by asking effective questions. Effective questions can be crafted following the SMART methodology. That means they’re specific, measurable, action-oriented, relevant, and time-bound. Specific questions are simple, significant and focused on a single topic or a few closely related
ideas (e.g., what percentage of kids achieve the recommended 60 minutes of physical activity at least five days a week? What are the top five features you would like to see in a car package?) Measurable questions can be quantified and assessed (e.g., how many times was our video shared on social channels the first week it was posted? On a scale of 1-10 with 10 being the most important how important is your car having four-wheel drive?) Action-oriented questions
encourage change (e.g., what design features will make our packaging easier to recycle?) Relevant questions matter, are important and have significance to the problem you’re trying to solve (e.g., What are the top five features you would like to see in a car package?) Time-bound questions specify the time to be studied (e.g., 1983 to 2004; Has four-wheel drive become more or less popular in the last three years?) Questions “should
be open-ended. This is the best way to get responses that will help you accurately qualify or disqualify potential solutions to your specific problem.” Questions should be fair. Questions don’t create or reinforce bias. “Fairness also means crafting questions that make sense to everyone. Questions are clear and have a straightforward wording that anyone can easily understand.” Unfair questions include leading questions (this product is great,
isn’t it?), and questions that makes assumptions (what do you love most about our exhibits?) Categorizing things is one of the six problem types data analysts solve. This type of problem might involve which of the following actions? Categorizing things involves classifying or grouping items in order to gain insights. Finding patterns is one of the six problem types data analysts aim to solve. This type of problem might involve which of the following? Finding patterns involves identifying trends from historical data. In the SMART methodology, questions that encourage change are described how? Action-oriented questions encourage
change. Fill in the blank: In data analytics, qualitative data _. Select all that apply. Qualitative data is subjective and measures qualities and characteristics. In data analytics, how are dashboards different from
reports? Dashboards monitor live, incoming data from multiple datasets and organize the information into one central location. Reports are static collections of data. Small data differs from big data in what ways? Select all that apply. Small data involves a small number of specific metrics over a shorter period of
time. It’s effective for analyzing day-to-day decisions. Big data involves larger and less specific datasets and focuses on change over a long period of time. It’s effective for analyzing more substantial decisions. Fill in the blank: Some of the most common symbols used in formulas include + (addition), – (subtraction), * (multiplication), and / (division). These are called _. Operators are symbols used in formulas, including + (addition), – (subtraction), * (multiplication), and / (division). In the function =SUM(G1:G35), identify the range. In the function =SUM(G1:G35), the range is G1:G35. A range is a collection of two or more cells. To
address a vague, complex problem, a data analyst breaks it down into smaller steps. They use a process to help them recognize the current problem or situation, organize available information, reveal gaps and opportunities, and identify options. What does this scenario describe? Structured thinking is the process of recognizing the current
problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options. Asking questions including, “Does my analysis answer the original question?” and “Are there other angles I haven’t considered?” enable data analysts to accomplish what tasks? Select all that apply. Data analysts ask thoughtful questions to help them reach solid conclusions, consider how to share data with others, and help team members make effective decisions. Marketing analytics is the process of measuring, analyzing, and managing a company’s marketing strategy and budget. Often, this
involves identifying the company’s target audience. The target audience includes which people? The target audience is the people the company is trying to reach. Video: Common problem typesThe finding patterns problem type could involve which of the following actions?
Video: SMART questionsQuestions leading to answers that can be quantified and assessed align with which component of the SMART methodology?
While considering a research question, a data analyst follows the SMART methodology. They limit their analysis to include data from July 2012 to August 2012. What component of the SMART framework describes this decision?
Take action with dataQuestion 1A data analytics team works to recognize the current problem. Then, they organize available information to reveal gaps and opportunities. Finally, they identify the available options. These steps are part of what process?
Question 2In which step of the data analysis process would an analyst ask questions such as, “What data errors might get in the way of my analysis?” or “How can I clean my data so the information I have is consistent?”
Question 3A data analyst has entered the analyze step of the data analysis process. Identify the questions they might ask during this phase. Select all that apply.
Question 4A data analyst is trying to understand their target audience. They’re asking questions such as, “How can learning more about my target audience help me figure out how to solve this problem?” and “What research do I need to do about my target audience?” The data analyst is in which phase of the data analysis process?
Solve problems with dataQuestion 1A data analyst identifies keywords from customer reviews and labels them as positive or neutral. This an example of which problem type?
Question 2The spotting something unusual problem type could involve which of the following scenarios?
Question 3A data analyst at an online retailer looks at trends in historical sales data. They want to understand what happened in the past and, therefore, is likely to happen again in the future. This an example of which problem type?
Craft effective questionsQuestion 1A data analyst uses the SMART methodology to create a question that encourages change. This type of question can be described how?
Question 2A time-bound SMART question specifies which of the following parameters?
Question 3A data analyst working for a mid-sized retailer is writing questions for a customer experience survey. One of the questions is: “Do you prefer online or in-store?” Then, they rewrite it to say: “Do you prefer shopping at our online marketplace or shopping at your local store?” Describe why this is a more effective question.
Question 4A data analyst at a social media company is creating questions for a focus group. They use common abbreviations such as PLS for “please” and LMK for “let me know.” This is fair because the participants use social media a lot and are likely to be technically savvy.
Weekly challenge 1Question 1Structured thinking involves which of the following processes? Select all that apply.
Question 2The prepare step of the data analysis process involves defining the problem you’re trying to solve and understanding stakeholder expectations.
Question 3The share phase of the data analysis process typically involves which of the following activities? Select all that apply.
Question 4A garden center wants to attract more customers. A data analyst in the marketing department suggests advertising in popular landscaping magazines. This is an example of what practice?
Question 5A data analyst is working for a local power company. Recently, many new apartments have been built in the community, so the company wants to determine how much electricity it needs to produce for the new residents in the future. A data analyst uses data to help the company make a more informed forecast. This is an example of which problem type?
Question 6Describe the key difference between the problem types of categorizing things and identifying themes.
Question 7Which of the following examples are closed-ended questions? Select all that apply.
Question 8The question, “Why don’t our employees complete their timesheets each Friday by noon?” is not action-oriented. Which of the following questions are action-oriented and more likely to lead to change? Select all that apply.
Question 9In the SMART methodology, time-bound questions are simple, significant, and focused on a single topic or a few closely related ideas.
Question 10Which of the following questions make assumptions? Select all that apply.
Week 2: Making data-driven decisionsNext, we’ll explore data of all kinds and its impact on decision making and learn how to share data through reports and dashboards. In analytics, data drives decision making. In this part of the course, you’ll explore data of all kinds and its impact on decision making. You’ll also learn how to share your data through reports and dashboards. Learning Objectives
2.1. Learn about data driven decisions 2.2. Understand the power of data 2.3. Follow the evidence 2.4. Connecting the data dots Video: How data empowers decisionsFill in the blank: Data-inspired decision-making explores different data sources to find _.
Video: Qualitative and quantitative dataFill in the blank: Quantitative data is specific and _.
Which of the following examples would be determined using qualitative data?
Video: The big reveal: Sharing your findingsA dashboard would be most beneficial for which of the following scenarios?
Understand the power of dataQuestion 1What is the difference between qualitative and quantitative data?
Question 2Fill in the blank: Data-inspired decision-making deals with exploring different data sources to find out _.
Question 3Which of the following examples describes using data to achieve business results? Select all that apply.
Question 4If someone is describing their feelings or emotions, it is qualitative data.
Follow the evidenceQuestion 1Fill in the blank: Pivot tables in data processing tools are used to _ data.
Question 2In data analytics, how are dashboards different from reports?
Question 3Describe the difference between data and metrics.
Question 4Return on Investment (ROI) uses which of the following metrics in its definition?
Connecting the data dotsQuestion 1Describe the key differences between small data and big data. Select all that apply.
Question 2Which of the following is an example of small data?
Question 3The amount of exercise time to burn a minimum of 400 calories is a problem that requires big data.
Weekly challenge 2Question 1Fill in the blank: In data analytics, a process or set of rules to be followed for a specific task is _.
Question 2Fill in the blank: In data analytics, qualitative data _. Select all that apply.
Question 3In data analytics, reports use live, incoming data from multiple datasets; dashboards use static collections of data.
Question 4A pivot table is a data-summarization tool used in data processing. Which of the following tasks can pivot tables perform? Select all that apply.
Question 5A metric is a single, quantifiable type of data that can be used for what task?
Question 6Fill in the blank: A _ goal is measurable and evaluated using single, quantifiable data.
Question 7If 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 investment scope.
Question 8Fill in the blank: A data analyst is using data to address a large-scale problem. This type of analysis would most likely require _. Select all that apply.
Week 3: Learning spreadsheet basicsNext, we will explore spreadsheets further and discover how they can help make data analysis even more effective, and we’ll start learning about structured thinking and how structured thinking can help analysts better understand business problems and come up with solutions. Spreadsheets are an important data analytics tool. In this part of the course, you will learn both why and how data analysts use spreadsheets in their work. You will also explore how structured thinking can help analysts better understand problems and come up with solutions. Learning Objectives
3.1. Introduction to spreadsheets 3.2. Working with spreadsheets 3.3. Using formulas in spreadsheets 3.4. Using functions in spreadsheets 3.5. Save time with structured thinking Answers to week 3 quiz questionsVideo: Get to work with spreadsheetsTo perform calculations in a spreadsheet, data analysts use formulas and functions.
Video: Formulas for successIn spreadsheets, what is the term for the symbols used in formulas to perform a specific calculation?
Video: Functions 101Which of the following are functions? Select all that apply.
Video: Scope of work and structured thinkingWhat process do data analysts use to recognize the current situation, organize information, and identify options?
Microsoft Excel resources
Google Sheets resources
Working with spreadsheetsQuestion 1To sort and filter the data in a spreadsheet, data analysts must use multiple formulas.
Question 2Which time-saving tool do data analysts use to organize data and perform calculations?
Question 3Within a spreadsheet, data analysts use which tools to save time and effort by automating commands? Select all that apply.
Using formulas in spreadsheetsQuestion 1Which of the following are examples of operators used in formulas? Select all that apply.
Question 2In a spreadsheet, a function should always start with which of the following operators?
Question 3What is the term for the set of cells that a data analyst selects to include in a formula?
Question 4In a formula, the plus sign (+) is the operator for addition, and the plus-minus (±) is the operator for subtraction.
Question 5If the cells in a spreadsheet contain anything other than numbers, which of the following errors might occur?
Using functions in spreadsheetsQuestion 1Data analysts use which of the following functions to quickly perform calculations in a spreadsheet? Select all that apply.
Question 2What is the term for a preset command in a spreadsheet?
Question 3You are working with spreadsheet data about a cross-country relay race. Each runner’s times are located in cells H2 through H28. To find the runner with the fastest time, what is the correct MIN function syntax? Type your answer below.
Question 4A data analyst at an electronics company needs to compare earnings of the four departments of the company over time. They collect the earning data of each unit for the previous three years and create a visualization. Which type of visualization would be most effective?
Question 5To visualize data, data analysts use which of the following graphs or charts? Select all that apply.
Save time with structured thinkingQuestion 1Fill in the blank: In order to save time and money, a data analyst defines the _ at the start of a project. Select all that apply.
Question 2The outline used to define a data analyst’s contribution to a project is called what?
Question 3To address a vague, complex problem, data analysts break it down into smaller steps. They use a process that helps them recognize the current problem or situation. Then, they organize available information, reveal gaps and opportunities, and identify the options. What process does this scenario describe?
Weekly challenge 3Question 1Both formulas and functions in spreadsheets begin with what symbol?
Question 2Attributes are used in spreadsheets for what purpose?
Question 3Which of the following tasks might be performed using spreadsheets?
Question 4Fill in the blank: Combining formulas and functions enables the function to run based on a _ set by the formula.
Question 5Which of the following statements describes a key difference between formulas and functions?
Question 6Fill in the blank: Putting data into context helps data analysts eliminate _.
Question 7Defining the problem domain is part of which data analytics process?
Question 8A data analyst uses structured thinking to recognize the current problem or situation. Select the final step to structured thinking.
Week 4: Always remember the stakeholderFinally, we’ll learn some proven strategies for managing the expectations of stakeholders while establishing clear communication with team members to achieve our objectives. Successful data analysts learn to balance needs and expectations. In this part of the course, you’ll learn strategies for managing the expectations of stakeholders while establishing clear communication with your team to achieve your objectives. Learning Objectives
4.1. Learn about communication best practices 4.2. Balance team and stakeholder needs 4.3. Communication is key 4.4. Recognizing data limitations 4.5. Optional -The workplace secret sauce, teamwork Answers to week 4 quiz questionsBalance team and stakeholder needsQuestion 1As a data analyst, it’s important to communicate often. Sharing detailed notes, creating reports, and using a changelog are all ways to communicate with the people who have invested time and resources in a project. Who are these people?
Question 2The customer-facing team does which of the following activities? Select all that apply.
Question 3The human resources director approaches a data analyst to propose a new data analysis project. The analyst has a lot of experience in human resources and believes the director is taking the wrong approach, and it will lead to some problems. Select the data analyst’s best course of action.
Communication is keyQuestion 1To communicate clearly with stakeholders and team members, there are four key questions data analysts ask themselves. The first is: Who is my audience? Identify the remaining three questions. Select all that apply.
Question 2You’re working on a data analysis project, and you run into an obstacle. You try to find a solution, but you’re having no luck, and now the project is going off schedule. The best course of action is to put in extra hours to keep looking for a solution, rather than bothering your team with the problem.
The best course of action is to ask your team for help. Taking initiative to solve problems is a great practice, but your team is an excellent resource if you find that you’re unable to find a solution by yourself. Question 3A colleague sent you a question via email nearly two days ago. You know it’s going to take a while for you to find the answer because you need to do some research first. You’re too busy to get it done today. What’s the best course of action?
Question 4Focusing on stakeholder expectations enables data analysts to achieve what goals? Select all that apply.
Question 5Setting realistic stakeholder expectations at every stage of a project might involve which of the following tasks? Select all that apply.
Recognize data limitationsQuestion 1A stakeholder has asked a data analyst to produce a report very quickly. What are some strategies the analyst can apply to ensure their work isn’t rushed, answers the right question, and delivers useful results? Select all that apply.
Question 2If a sample size is too small, a few unusual responses can skew the results. To avoid this problem, data analysts aim to collect lots of data and chart trends over longer time periods.
Question 3Asking questions including, “Does my analysis answer the original question?” and “Are there other angles I haven’t considered?” enable data analysts to accomplish what tasks? Select all that apply.
The workplace secret sauce, teamworkQuestion 1Your supervisor gives you a new data analysis project with unclear instructions, and you become frustrated trying to figure out how to proceed. Before moving forward, what should you do? Select all that apply.
Question 2You’re working on a data analysis project with a coworker, and the two of you disagree on what the data is telling you. Things get tense. The best course of action is to go to your supervisor and politely explain that your coworker is looking at the data incorrectly. Then, ask to work with a different coworker on future projects.
Question 3A director emails you asking for a report by the end of the week. This type of report takes at least 10 days to complete correctly. What is the best course of action?
Weekly challenge 4Question 1A data analytics team is working on a project to measure the success of a company’s new financial strategy. The vice president of finance is most likely to be the _.
Question 2A data analyst is researching the buying behavior of people who shop at a company’s retail store and those who might shop there in the future. During the analysis, it will be important to stay in communication with the team that most often interacts with these shoppers. What is the name of this team?
Question 3To communicate clearly with stakeholders and team members, there are four key questions data analysts ask themselves. One of them is: What does my audience need to know? Identify the remaining three questions. Select all that apply.
Question 4A data analyst feels overworked. They often stay late to finish work, and have started missing deadlines. Their supervisor emails them another project to complete, and this causes the analyst even more stress. How should they handle this situation?
Question 5Data analysts pay attention to sample size in order to achieve what goals? Select all that apply.
Question 6A data analyst has been invited to a meeting. They review the agenda and notice that their data analysis project is one of the topics that will be discussed. They plan to arrive on time and have a pen and paper to take notes. But they do not spend time considering project updates they could share or questions they may be asked. This is okay because they’re not the one running the meeting.
Question 7Which of the following steps are key to leading a professional online meeting? Select all that apply.
Question 8Conflict is a natural part of working on a team. What are some ways to help shift a situation from problematic to productive? Select all that apply.
Course challengeScenario 1, questions 1-5Question 1You’ve just started a job as a data analyst at a small software company that provides data analytics and business intelligence solutions. Your supervisor asks you to kick off a project with a new client, Athena’s Story, a feminist bookstore. They have four existing locations, and the fifth shop has just opened in your community. Athena’s Story wants to produce a campaign to generate excitement for an upcoming celebration and introduce the bookstore to the community. They share some data with your team to help make the event as successful as possible. Your task is to review the assignment and the available data, then present your approach to your supervisor. Then, review the email, and review the Customer Survey and Historical Sales datasets:
After reading the email, you notice that the acronym WHM appears in multiple places. You look it up online, and the most common result is web host manager. That doesn’t seem right to you, as it doesn’t fit the context of a feminist bookstore. How do you proceed?
Question 2Scenario 1 continued The qualitative data includes information from which columns? Select all that apply.
Question 3Next, you review the customer feedback in column F of the Customer Survey (link to download CSV instead below). The attribute of column F is, “Survey Q6: What types of books would you like to see more of at Athena’s Story?” In order to verify that children’s literature and feminist zines are among the most popular genres, you create a visualization. This will help you clearly identify which genres are most likely to sell well during the Women’s History Month campaign. Your visualization looks like this: Fill in the blank: The visualization you create demonstrates the percentages of each book genre that make up the total number of survey responses. It’s called a _ chart.
Question 4Now that you’ve confirmed that children’s literature and feminist zines are among the most requested book genres, you review the Historical Sales. You’re pleased to see that columns D and E have something in common: They both contain data that’s specific to children’s literature and feminist zines. This will provide you with the information you need to make data-inspired decisions. In addition, the children’s literature and feminist zines metrics will help you organize and analyze the data about each genre in order to determine if they’re likely to be profitable. Next, you use the SUM function to calculate the total sales over 52 weeks for feminist zines. What is the correct syntax? Type your answer below.
Question 5After familiarizing yourself with the project and available data, you present your approach to your supervisor. You provide a scope of work, which includes important details, a schedule, and information on how you plan to prepare and validate the data. You also share some of your initial results and the pie chart you created. In addition, you identify the problem type, or domain, for the data analysis project. You decide that the historical sales data can be used to provide insights into the types of books that will sell best during Women’s History Month this coming year. This will also enable you to determine if Athena’s Story should begin selling more children’s literature and feminist zines. Using historical data to make informed decisions about how things may be in the future is an example of discovering connections.
Scenario 2, questions 6-10Question 6You’ve completed this program and are now interviewing for your first junior data analyst position. You’re hoping to be hired by an event planning company, Patel Events Plus. So far, you’ve successfully completed the first round of interviews with the human resources manager and director of data and strategy. Now, the vice president of data and strategy wants to learn more about your approach to managing projects and clients. You arrive Thursday at 1:45 PM for your 2 PM interview. Soon, you’re taken into the office of Mila Aronowicz, vice president of data and strategy. After welcoming you, she begins the behavioral interview. First, she hands you a copy of Patel Events Plus’s organizational chart. As you’ve learned in this course, stakeholders are people who invest time, interest, and resources into the projects you’ll be working on as a data analyst. Let’s say you’re working on a project involving data and strategy. Based on what you find in the organizational chart, if you need information from the secondary stakeholders, who can you ask? Select all that apply.
Question 7Next, the vice president wants to understand your knowledge about asking effective questions. Consider and respond to the following question. Select all that apply. Let’s say we just completed a big event for a client and wanted to find out if they were satisfied with their experience. Provide some examples of measurable questions that you could include in the customer feedback survey.
Question 8Now, the vice president presents a situation having to do with resolving challenges and meeting stakeholder expectations. Consider and respond to the following question. You’re working with a dataset that the data analytics coordinator should have cleaned, but it turns out that it wasn’t. Your supervisor thought the dataset was ready for use, but you discover nulls, redundant data, and other issues. The project is due in less than two weeks. How would you handle that situation?
Question 9Your next interview question deals with sharing information with stakeholders. Consider and respond to the following question. Let’s say you want to share information about an upcoming event with stakeholders. It’s important that they’re able to access and interact with the data in real time. Would you create a report or a dashboard?
Question 10Your final behavioral interview question involves using metrics to answer business questions. Your interviewer hands you a copy of PatelEventsData. Then, she asks: Recently, Patel Events Plus purchased a new venue for our events. If we asked you to calculate the return on investment of this purchase, which metrics would you use?
Related contentBasic Statistics Mini-Course Google Data Analytics Professional Certificate Course 1: Foundations – Cliffs Notes Google Data Analytics Professional Certificate Course 3: Prepare Data – quiz answers Google Data Analytics Professional Certificate Course 4: Process Data – quiz answers Google Data Analytics Professional Certificate Course 5: Analyze Data – quiz answers Google Data Analytics Professional Certificate Course 6: Share Data – quiz answers Google Data Analytics Professional Certificate Course 7: Data Analysis with R – quiz answers Google Data Analytics Professional Certificate Course 8: Capstone – quiz answers Back to DTI Courses What is the main difference between a formula and a function coursera?Formula is a user-defined statement used for calculation. It can contain one or more functions depending on the complexity. Answer. A function is a system-defined program used to calculate.
Which of the following statements describes a key difference between formulas and functions?Which of the following statements describes a key difference between formulas and functions? Formulas are written by the user, and functions are already defined.
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