Week 1

Making predictions is one of the six data analytics problem types. It deals with using data to inform decisions about how things may be in the future. Select the scenario that's an example of making predictions. A data analyst at a shoe retailer uses data to inform the marketing plan for an upcoming summer sale. A data analyst at a gas company uses historical data to analyze which time of year customers use the most gas. A data analyst at a school system uses data to make a connection between home sales and new student enrollment. A data analyst at a technology company uses data to identify a unique drop in social media engagement.

A data analyst at a shoe retailer uses data to inform the marketing plan for an upcoming summer sale.

The spotting something unusual problem type could involve which of the following scenarios? A data analyst at a clothing retailer creates a list of common topics, categorizes them, and groups each category into a broader subject area for further analysis. A data analyst working for an agricultural company examines why a dataset has a surprising and rare data point. A data analyst at an arts nonprofit classifies similar data points into groups for further analysis. A data insight helps a landscaping company envision what will happen in the future

A data analyst working for an agricultural company examines why a dataset has a surprising and rare data point.

Question 1 A data analyst uses the SMART methodology to create a question that encourages change. This type of question can be described how? Stimulating Motivational Results-focused Action-oriented

Action-oriented

What are things you should avoid when making questions?

Avoid technical jargon. Prioritize your questions: Ask the most important and impactful questions first to save time. Make your time count: Stay on subject during the conversation. Clarify your understanding: To avoid confusion, briefly summarizing the given answers to make sure you understood it correctly. This will go a long way in helping you avoid mistakes.

A data analyst identifies and classifies keywords from customer reviews to improve customer satisfaction. This is an example of which problem type? Categorizing things Finding patterns Spotting something unusual Making predictions

Categorizing things

What problem type? An example of a problem requiring analysts to categorize things is a company's goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.

Categorizing things

What type of questions is this? "How was the customer trial?"

Closed-ended questions: questions that ask for a one-word or brief response only This is a closed-ended question because it doesn't encourage people to expand on their answer. It is really easy for them to give one-word responses that aren't very informative. A better question might be, "Tell me about the customer experiences from the trial." This encourages people to provide more detail besides "It went well."

What problem type? A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.

Discovering connections

At a minimum, good notes include:

Facts: Any concrete piece of information is usually worth writing down. Dates, times, names, and other specifics that pop up. Context: Facts without context are useless. Note any relevant details that are needed in order to understand the information you gather. Unknowns: Sometimes you may miss an important question during a conversation. Make a note when this happens so you know to figure out the answer later.

In the SMART methodology, time-bound questions are simple, significant, and focused on a single topic or a few closely related ideas. True False

False

Question 4 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. True False

False

A data analyst at an online retailer works with historical sales data. The analyst identifies repeating trends in the sales data. This is an example of which problem type? Making predictions Finding patterns Categorizing things Identifying themes

Finding patterns

What problem type? Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.

Finding patterns

The question, "Why was the Monday afternoon yoga class successful?" is not measurable. Which of the following questions presents a measurable way to learn about the yoga class? Is yoga a great way to stretch and strengthen your body? How many customers responded to our recent half-price yoga promotion? Why do people like taking yoga classes on Mondays? Do yoga instructors seem more energetic at the beginning of the week?

How many customers responded to our recent half-price yoga promotion?

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. What story is my data telling me? The analyze step involves thinking analytically about data. Data analysts might ask how the data can help them solve the problem and what story the data is trying to tell How can I create an engaging presentation to stakeholders? What is the question I'm trying to answer? How will my data help me solve this problem?

How will my data help me solve this problem? What story is my data telling me?

What problem type? User experience (UX) designers might rely on analysts to analyze user interaction data. Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. Themes are most often used to help researchers explore certain aspects of data. In a user study, user beliefs, practices, and needs are examples of themes. By now you might be wondering if there is a difference between categorizing things and identifying themes, The best way to think about it is this: Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes.

Identifying themes

Describe the key difference between the problem types of categorizing things and identifying themes. Categorizing things involves assigning items to categories. Identifying themes takes those categories a step further, grouping them into broader themes. Categorizing things involves determining how items are different from each other. Identifying themes brings different items back together in a single group. Categorizing things involves assigning grades to items. Identifying themes involves creating new classifications for items. Categorizing things involves taking inventory of items. Identifying themes deals with creating labels for items.

Identifying themes takes those categories a step further, grouping them into broader themes.

Question 10 On a customer service questionnaire, a data analyst asks, "If you could contact our customer service department via chat, how much valuable time would that save you?" Why is this question unfair? It uses slang words that not everyone can understand It is closed-ended It makes assumptions It is vague

It makes assumptions

What type of questions is this? This product is too expensive, isn't it?"

Leading questions questions that only have a particular response This is a leading question because it suggests an answer as part of the question. A better question might be, "What is your opinion of this product?" There are tons of answers to that question, and they could include information about usability, features, accessories, color, reliability, and popularity, on top of price. Now, if your problem is actually focused on pricing, you could ask a question like "What price (or price range) would make you consider purchasing this product?" This question would provide a lot of different measurable responses.

What problem type? A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can't guarantee future results, but they can help predict the best placement of advertising to reach the target audience.

Making predictions

Example Questions for: Specific: Does the question focus on a particular car feature? Measurable: Does the question include a feature rating system? Action Oriented: Does the question influence creation of different or new feature packages? Relevant: Does the question identify which features make or break a potential car purchase? Time-bound: Does the question validate data on the most popular features from the last three years?

On a scale of 1-10 (with 10 being the most important) how would you rate 4-wheel drive? What are the top 5 features you would like to see in a car package? When would you consider buying a car without 4-wheel drive? How much more would you pay for a car with 4-wheel drive? Has 4-wheel drive become more or less popular in the last three years?

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? Ask Share Act Prepare

Prepare

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?" Process Ask Analyze Prepare

Process

Question 4 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? Monitoring social media feedback Reaching your target audience Collecting customer information Developing a data analytics case study

Reaching your target audience

A data analyst creates data visualizations and a slideshow. Which phase of the data analysis process does this describe? Act Share Prepare Process

Share

What problem type? A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual. Analysts who have analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn't trend normally.

Spotting something unusual

Question 2 A time-bound SMART question specifies which of the following parameters? The desired change the analysis should produce The era, phase, or period of analysis The topic or subject of the analysis The metrics or measures related to the analysis

The era, phase, or period of analysis

Question 3 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. The first question is leading, whereas the second question could have many different answers. The first question is closed-ended, whereas the second question encourages the respondent to elaborate. The first question contains slang that might not make sense to everyone, whereas the second question is easily understandable. The first question is vague, whereas the second question includes important context.

The first question is vague, whereas the second question includes important context.

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? Making connections Using structured thinking Categorizing things Applying the SMART methodology

Using structured thinking

Question 1 Organizing available information and revealing gaps and opportunities are part of what process? Categorizing things Identifying connections between two or more things Applying the SMART methodology Using structured thinking

Using structured thinking

What type of questions is this? Example: "Does that tool work for you?"

Vague questions: questions that aren't specific or don't provide context This question is too vague because there is no context. Is it about comparing the new tool to the one it replaces? We just don't know. A better question might be, "When it comes to data entry, how much time does the tool save you?" This question gives context (data entry) and helps frame responses that are measurable (time).

Question 7 Which of the following examples are leading questions? Select all that apply. In what ways did our product meet your needs? What do you enjoy most about our service? How did you learn about our company? How satisfied were you with our customer representative?

What do you enjoy most about our service? How satisfied were you with our customer representative?