OverviewNow that you have been introduced to the SMART framework for asking questions, pause to apply what you are learning. In this self-reflection, you will consider the questions you would ask in a specific scenario. This self-reflection will help you develop insights into your own learning and prepare you to apply your knowledge of the SMART question framework to your own data investigations. As you answer questions—and come up with questions of your own—you will consider concepts, practices, and principles to help refine your understanding and reinforce your learning. You’ve done the hard work, so make sure to get the most out of it: This reflection will help your knowledge stick!The scenarioYou are three weeks into your new job as a junior data analyst. The company you work for has just collected data on their weekend sales. Your manager asks you to perform a thorough exploration of this data. To get this project started, you must ask some questions and get some information.SMART questionsAs a refresher, SMART questions are:Specific: Questions are simple, significant, and focused on a single topic or a few closely related ideas.Measurable: Questions can be quantified and assessed.Action-oriented: Questions encourage change.Relevant: Questions matter, are important, and have significance to the problem you’re trying to solve. Time-bound: Questions specify the time to be studied.Next, you will use the SMART framework to ask effective questions about the scenario above. Then, you will reflect on the topics your SMART questions should address.Ask the right type of questionsYou can apply the SMART framework to all types of questions. The type of questions you ask can help you explore deeper with your data. Consider the ways your questions help you examine objectives, audience, time, security, and resources.Some common topics for questions include: ObjectivesAudienceTimeResourcesSecurityThink about how you can ask SMART questions about each of these topics.ReflectionConsider the scenario above:Based on the SMART framework, which questions are most important to ask? How will these questions clarify the requirements and goals for the project?How does asking detailed, specific questions benefit you when planning for a project? Can vague or unclear questions harm a project?Now, write 2-3 sentences (40-60 words) in response to each of these questions

.Question 1A data analyst uses the SMART methodology to create a question that encourages change. In the SMART methodology, how is this question type described? 1 pointMotivational TransitionalAction-orientedResults-focused2.Question 2What is prioritized when asking a specific SMART question? Select all that apply.1 pointFocusing on a single topic or a few closely related ideasEnsuring the question is significantKeeping the question simpleAsking about the era, phase, or period involved3.Question 3A data analyst working for a coffee roaster is writing questions for a customer experience survey. One of the questions asks, “Do you prefer online or in-store?” Then, they rewrite it to ask, “Do you prefer purchasing coffee beans through our online marketplace or by shopping at our retail store?” Why is this a more effective question?1 pointThe first question is leading, whereas the second question could have many different answers.The first question contains slang that might not make sense to everyone, whereas the second question is easily understandable.The first question is closed-ended, whereas the second question encourages the respondent to elaborate.The first question is vague, whereas the second question includes important context.4.Question 4A data team at a high-tech company writes questions for a focus group. They use common abbreviations such as PLS for “please” and LMK for “let me know.” A supervisor then suggests spelling everything out in order to ensure the questions are fair. What are they trying to achieve?1 pointWriting questions that do not make assumptionsAvoiding leading people to a particular responsePresenting questions with straightforward wordingAsking irrelevant questions

Why is it important to create SMART questions about your datasets? How do these questions benefit your work as a data professional?Why is it important to determine your SMART questions and answers before crafting an SOW?Why is it important to perform data analysis on datasets?Now, write 2-3 sentences (40-60 words) in response to each of these questions.

What SMART questions did you ask? How did these questions tie into the field of the person you chatted with? What insights did you discover during your conversation? How did the SMART framework help you arrive at your conclusions?Now, write 2-3 sentences (40-60 words) in response to each of these questions

Before you begin your conversation about data, consider each of the above steps. Think about potential candidates, brainstorm some SMART questions, and get an idea of the information you want to record during your conversation. Then, reflect on your conversation:What SMART questions did you ask? How did these questions tie into the field of the person you chatted with? What insights did you discover during your conversation? How did the SMART framework help you arrive at your conclusions?

Question 1OverviewNow that you have learned about the importance of keeping track of changes in your data analysis, you can pause for a moment and track what you are learning. In this self-reflection, you will consider your thoughts about changelogs and respond to brief questions. This self-reflection will help you develop insights into your own learning and prepare you to incorporate changelogs into your data cleanings procedures. As you answer questions—and come up with questions of your own—you will consider concepts, practices, and principles to help refine your understanding and reinforce your learning. You’ve done the hard work, so make sure to get the most out of it: This reflection will help your knowledge stick! The importance of changelogsIn previous activities, you’ve reviewed the different types of questions to ask before exploring data, the importance of pre-cleaning data, the basic functions of SQL, how to clean data with spreadsheets, and more. As a junior data analyst, most of your projects will consist of these activities. As you have experienced, each of these tasks follows a complicated process. Therefore, consistent and accurate record-keeping is essential to keeping you on track.A changelog is a document used to record the notable changes made to a project over its lifetime across all of its tasks. It is typically curated so that the changes it records are listed chronologically across all versions of the project.The major benefit to using changelogs is that contributors and users connected with the project get a specific list of what important alterations have been made, when they were made, and sometimes, what version they were released for. It is an invaluable tool for communicating how the project has evolved over time to coworkers, management, and stakeholders.Best practices for changelogsA changelog for a personal project may take any form desired. However, in a professional setting and while collaborating with others, readability is important. These guiding principles help to make a changelog accessible to others: Changelogs are for humans, not machines, so write legibly.Every version should have its own entry.Each change should have its own line.Group the same types of changes. For example, Fixed should be grouped separately from Added.Versions should be ordered chronologically starting with the latest.The release date of each version should be noted.All the changes for each category should be grouped together. Types of changes usually fall into one of the following categories:Added: new features introducedChanged: changes in existing functionalityDeprecated: features about to be removedRemoved: features that have been removedFixed: bug fixesSecurity: lowering vulnerabilitiesExamine a sample changelogExamine the figure below for an example of a changelog. Note that the following example is written in Markdown, as it is common to keep changelogs as a readme file in a code repository. 12345678910111213141516# ChangelogThis file contains the notable changes to the project Version 1.0.0 (02-23-2019)## New    - Added column classifiers (Date, Time, PerUnitCost, TotalCost, etc. )    - Added Column “AveCost” to track average item cost ## Changes     - Changed date format to MM-DD-YYYY    - Removal of whitespace (cosmetic) ## Fixes    - Fixed misalignment in Column "TotalCost" where some rows did not match with correct dates    - Fixed SUM to run over entire column instead of partial What to record in a changelogNow that you're familiar with the example, consider what changes you need to record in a changelog. To start, you record the various changes, additions, and fixes that were discussed above. Arrange them using bullets or numbering with one change per line. Group similar changes together with a label describing the change immediately above them.Use different version numbers for each milestone reached in your project. Within each version, place the logged changes that were made since the previous version (milestone). Dates are not generally necessary for each change, but they are recommended for each version.In an upcoming course, you will have the opportunity to complete a capstone project. This will be a great chance to demonstrate your ability to organize a project like a professional data analyst by keeping your own changelog. You can do this using a simple text file or spreadsheet and include your changelog with the project write-up. It will help you stay organized and collaborate with others. Keep this in mind when you reach the capstone project in an upcoming course, and don’t be afraid to revisit this lesson if you have questions.ReflectionConsider what you reviewed about changelogs in this reflection: What makes for a good changelog? How do you decide if a change is significant enough to include in the changelog? Now, write 2-3 sentences (40-60 words) in response to each of these questions. Type your response in the text box below.