Skills You'll LearnSpreadsheet, Questioning, Decision-Making, Problem Solving, Data Analysis Show
Reviews
AS Aug 21, 2021 i love the course as it helped me understand the importance of asking the right questions, understanding the problem and expectations of stakeholders and impotance and the right way of communicatilon. MM Sep 30, 2022 Excellent and thoroughly discussed all the lessons. Great learning experience! Looking forward to more valuable knowledge and insights from the bright and highly intelligent instructors and analysts. From the lesson Effective questions Taught By
Skills You'll LearnData Analysis, Tableau Software, Data Visualization (DataViz), Visualization (Computer Graphics) Reviews
IT Nov 7, 2018 I was pleased by the quality of this course. I really learned how to use tableau and the tips on researching and presenting were well founded. The teachers were positive and hard working. Thank you! TT Sep 19, 2020 An in detail course for beginners on Tableau. Excellent work by the professors in terms of explaining key concepts and helping students learn the tool properly. All should definitely go for it :) !! From the lesson Asking The "Right Questions" Taught By
There are so many technical aspects that go into creating a data visualization, ranging from exhausting data prep to reconciling advanced calculations. I'd like to argue that one of the most important aspects is not technical at all - it's knowing your audience. Your programming skills, art degree, or years of mastering a BI tool will not help here. You could create the da Vinci of dashboards, it could answer every business question you can think of, but if you design it for the wrong audience, its effectiveness will never be utilized. Table of Contents Show
To ensure that this never happens to any of your data visualizations, before you start any project, make sure you have exhausted the answers to these four questions: What is The Business Context?The answer to this question will form the foundation for the rest of your requirements gathering. Before you can design anything for anyone, you must have a firm grasp on why you are doing it. What problems are they looking to solve? How do these questions fit into larger ones or any overarching business initiatives. Once you think you understand, ask even more questions to be sure. Which department does this fit into? Do you have anything you currently use to answer these questions, can I look at those? Do you have any pre-conceived notions of what the findings of this analysis will be? Who Will Use This?So, who is your audience anyway? Knowing how to appeal to the primary users of your dashboards is critical. What level of analysis are they looking to do? Are you creating something for analysts that want to slice, dice, drill, further analyze, get to the bottom of many questions? You'll want to build a robust, analytical tool for them - several sets of interactive dashboards that enable a steady flow of analysis. In this case, they may want to see data at a more granular level - consider scatter plots, box and whisker, parameter-driven breakdowns where applicable. On the other end of the spectrum, they might be looking for high-level numbers and light trending. This may be a manager who wants to check progress but isn't the one to get down to the lower level questions. In this case, the vizzer should have incredibly intuitive, lightly interactive dashboards with simpler chart types than in the case of an analyst. Suggested design elements would be large, high-level numbers and trending bar or line graphs. What Are They Used to Seeing?As vizzers develop more industry knowledge, it becomes easier to overlook how your user will want to see their data, and just always churn out reports that use only best practices. One of the biggest audiences we at Arkatechture run into are Excel/Crystal Report consumers. If you don't ask these questions and present them with a data visualization that is 100% best practice, I can guarantee you that 90% will have an negative initial reaction. Don't be fooled by "oh, that's pretty" or "that looks like what I know we should be doing," that doesn't mean they understand what they're looking at. It's easy to get discouraged by these negative reactions, but think of it as a challenge. Meet people who are used to looking at "tabular" reports in the middle. There are several techniques to do that, one we use a lot is to make a best practice visualization, but then include a tabular view of the same data below it. When new to consuming visual dashboards, trust is often an issue and people don't trust bars and lines, they trust numbers. Once you show them that bars and lines match the numbers, then you can slowly start weaning them off of the numbers altogether and they will find the speed of analysis much faster. Form a good relationship with any stakeholders and be sure they are comfortable getting dashboards to a point where they will engage with them often. How Will They Use This?Now that you know who your user is, what they want and what they've had, you will need to consider how they will use the dashboards. When will they be most likely to get into the analysis flow and how often? This will let you know how frequently the data should be updated. Get to know their ideal scenario - will they pull the dashboard up every morning over a cup of coffee and stare at it? In this case, the visualization should be simple, to the point and able to be acted upon without much interaction. Will they be using it to cross-compare other reports, answer deeper questions? Will the questions vary? In this case, you will want the dashboards to be incredibly flexible to answer a multitude of questions and slice things any way may be pertinent. Once you go through these questions a time or two, it becomes second nature. You'll find yourself getting fewer requests for design overhauls and more engagement with your dashboards. Once they reach high engagement, you have their attention and their trust and can start incorporating more best practices and more advanced visualization techniques - which every vizzer knows is the true way to measure progress! If this was helpful, you should check out our blog post on 10 questions to ask to increase dashboard adoption!This is the second course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. You’ll build on your understanding of the topics that were introduced in the first Google Data Analytics Certificate course. The material will help you learn how to ask effective questions to make data-driven decisions while connecting with stakeholders’ needs. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources. Enroll on Coursera Ask Questions to Make Data-Driven Decisions Quiz AnswersAsk Questions to Make Data-Driven Decisions Week 01 Quiz AnswersPractice Quiz-1 AnswersL2 Take action with data:Q1. A 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?
Q2. In 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?” Q3. A 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.
Q4. 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?” The data analyst is in which phase of the data analysis process? Practice Quiz-2 AnswersL3 Solve problems with data:Q1. A data analyst identifies keywords from customer reviews and labels them as positive or neutral. This an example of which problem type?
Q2. The spotting something unusual problem type could involve which of the following scenarios?
Q3. A 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?
Practice Quiz-3 AnswersL4 Craft effective questions:Q1. A data analyst uses the SMART methodology to create a question that encourages change. This type of question can be described how?
Q2. A time-bound SMART question specifies which of the following parameters?
Q3. A 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.
Q4. A 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. Ask Questions to Make Data-Driven Decisions Weekly challenge 1 AnswersQ1. Structured thinking involves which of the following processes? Select all that apply.
Q2. The prepare step of the data analysis process involves defining the problem you’re trying to solve and understanding stakeholder expectations. Q3. The share phase of the data analysis process typically involves which of the following activities? Select all that apply.
Q4. A 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?
Q5. A 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?
Q6. Describe the key difference between the problem types of categorizing things and identifying themes.
Q7. Which of the following examples are closed-ended questions? Select all that apply.
Q8. The 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.
Q9. In the SMART methodology, time-bound questions are simple, significant, and focused on a single topic or a few closely related ideas. Q10. Which of the following questions make assumptions? Select all that apply.
Ask Questions to Make Data-Driven Decisions Week 02 Quiz AnswersPractice Quiz-1 AnswersL2 Understand the power of data:Q1. What is the difference between qualitative and quantitative data?
Q2. Fill in the blank: Data-inspired decision-making deals with exploring different data sources to find out _____.
Q3. Which of the following examples describes using data to achieve business results? Select all that apply.
Q4. If someone is describing their feelings or emotions, it is qualitative data. Practice Quiz-2 AnswersL3 Follow the evidence:Q1. Fill in the blank: Pivot tables in data processing tools are used to _____ data.
Q2. In data analytics, how are dashboards different from reports?
Q3. Describe the difference between data and metrics.
Q4. Return on Investment (ROI) uses which of the following metrics in its definition?
Practice Quiz-3 AnswersL4 Connecting the data dots:Q1. Describe the key differences between small data and big data. Select all that apply.
Q2. Which of the following is an example of small data?
Q3. The amount of exercise time to burn a minimum of 400 calories is a problem that requires big data. Ask Questions to Make Data-Driven Decisions Weekly challenge 2 AnswersQ1. Fill in the blank: In data analytics, a process or set of rules to be followed for a specific task is _____.
Q2. Fill in the blank: In data analytics, qualitative data _____. Select all that apply.
Q3. In data analytics, reports use live, incoming data from multiple datasets; dashboards use static collections of data. Q4. A pivot table is a data-summarization tool used in data processing. Which of the following tasks can pivot tables perform? Select all that apply.
Q5. A metric is a single, quantifiable type of data that can be used for what task?
Q6. Fill in the blank: A _____ goal is measurable and evaluated using single, quantifiable data.
Q7. 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 investment scope. Q8. Fill 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.
Ask Questions to Make Data-Driven Decisions Week 03 Quiz AnswersPractice Quiz-1 AnswersL2 Working with spreadsheets:Q1. To sort and filter the data in a spreadsheet, data analysts must use multiple formulas. Q2. Which time-saving tool do data analysts use to organize data and perform calculations?
Q3. Within a spreadsheet, data analysts use which tools to save time and effort by automating commands? Select all that apply.
Practice Quiz-2 AnswersL3 Using formulas in spreadsheets:Q1. Which of the following are examples of operators used in formulas? Select all that apply. 1 / 1 point
Q2. In a spreadsheet, a function should always start with which of the following operators?
Q3. What is the term for the set of cells that a data analyst selects to include in a formula?
Q4. In a formula, the plus sign (+) is the operator for addition, and the plus-minus (±) is the operator for subtraction. Q5. If the cells in a spreadsheet contain anything other than numbers, which of the following errors might occur?
Practice Quiz-3 AnswersL5 Save time with structured thinking:Q1. Fill 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.
Q2. The outline used to define a data analyst’s contribution to a project is called what?
Q3. To 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?
Ask Questions to Make Data-Driven Decisions Weekly challenge 3 AnswersQ1. Both formulas and functions in spreadsheets begin with what symbol?
Q2. Attributes are used in spreadsheets for what purpose?
Q3. Which of the following tasks might be performed using spreadsheets?
Q4. Fill in the blank: Combining formulas and functions enables the function to run based on a _____ set by the formula. Q5. Which of the following statements describes a key difference between formulas and functions?
Q6. Fill in the blank: Putting data into context helps data analysts eliminate _____.
Q7. Defining the problem domain is part of which data analytics process?
Q8. A data analyst uses structured thinking to recognize the current problem or situation. Select the final step to structured thinking.
Ask Questions to Make Data-Driven Decisions Week 04 Quiz AnswersPractice Quiz-1 AnswersL2 Balance team and stakeholder needs:Q1. As 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?
Q2. The customer-facing team does which of the following activities? Select all that apply.
Q3. The 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.
Practice Quiz-2 AnswersL3 Communication is key:Q1. To 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.
Q2. You’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. Q3. A 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?
Q4. Focusing on stakeholder expectations enables data analysts to achieve what goals? Select all that apply.
Q5. Setting realistic stakeholder expectations at every stage of a project might involve which of the following tasks? Select all that apply.
Practice Quiz-3 AnswersL4 Recognize data limitations:Q1. A 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.
Q2. If 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. Q3. 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.
Ask Questions to Make Data-Driven Decisions Weekly challenge 4 AnswersQ1. A 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 _____.
Q2. A 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?
Q3. To 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.
Q4. A 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?
Q5. Data analysts pay attention to sample size in order to achieve what goals? Select all that apply.
Q6. A 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. Q7. Which of the following steps are key to leading a professional online meeting? Select all that apply.
Q8. Conflict 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.
Next Course Quiz Answers >> Ask Questions to Make Data-Driven Decisions << Previous Course Quiz Answers Foundations: Data, Data, Everywhere All Course Quiz Answers of Google Data Analytics Professional Certificate Course 01: Foundations: Data, Data, Everywhere Course 02: Ask Questions to Make Data-Driven Decisions Course 03: Prepare Data for Exploration Course 04: Process Data from Dirty to Clean Course 05: Analyze Data to Answer Questions Course 06: Share Data Through the Art of Visualization Course 07: Data Analysis with R Programming Course 08: Google Data Analytics Capstone: Complete a Case Study Ask Questions to Make Data-Driven Decisions Course Review:In our experience, we suggest you enroll in the Ask Questions to Make Data-Driven Decisions Course and gain some new skills from Professionals completely free and we assure you will be worth it. Ask Questions to Make Data-Driven Decisions course is available on Coursera for free, if you are stuck anywhere between quiz or graded assessment quiz, just visit Networking Funda to get Ask Questions to Make Data-Driven Decisions Quiz Answers This Course is a part of the Google Data Analytics Professional Certificate Conclusion:I hope this Ask Questions to Make Data-Driven Decisions Quiz Answers would be useful for you to learn something new from this Course. If it helped you then don’t forget to bookmark our site for more Coursera Quiz Answers. This course is intended for audiences of all experiences who are interested in learning about Data Analytics in a business context; there are no prerequisite courses. Keep Learning!
What smart questions did you ask in data analytics?To sum it up, here are the most important data questions to ask:. What exactly do you want to find out?. What standard KPIs will you use that can help?. Where will your data come from?. How can you ensure data quality?. Which statistical analysis techniques do you want to apply?. In 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?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?” during the process step. This is when data is cleaned in order to eliminate any possible errors, inaccuracies, or inconsistencies.
What process do data analysts use to recognize the current situation organize information and identify options?In the function =SUM(G1:G35), identify the range. 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.
Which data analytics helps answer questions about what should be done?Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made.
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