Google Data Analytics Professional

Ask Questions to Make Data-Driven Decisions

WEEK1 - Effective questions

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 challenges and how analysts address them. You'll also learn about effective questioning techniques that can help guide your analysis.

Learning Objectives

• Explain the characteristics of effective questions with reference to the SMART framework
• Discuss the common types of problems addressed by a data analyst
• Explain how each step of the problem-solving roadmap contributes to common analysis scenarios
• Explain the data analysis process, making specific reference to the ask, prepare, process, analyze, share, and act phases
• Describe the key ideas associated with structured thinking including the problem domain, scope of work, and context

Problem-solving and effective questioning

Introduction

Structured thinking is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options. In this process, you address a vague, complex problem by breaking it down into smaller steps, and then those steps lead you to a logical solution.

Ximena talked a bit about why it’s important for data analysts to ask effective questions. She noted that effective questions lead to great insights, discoveries, and solutions to even the most challenging business problems.

Course Syllabus

Course 2 – Ask Questions to Make Data-Driven Decisions

1. Asking effective questions: 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’ll also learn about effective questioning techniques that can help guide your analysis.

2. Making data-driven decisions: 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.

3. Mastering spreadsheet basics: Spreadsheets are an important data analytics tool. In this part of the course, you’ll learn both why and how data analysts use spreadsheets in their work. You’ll also explore how structured thinking can help analysts better understand problems and come up with solutions.

4. Always remembering the stakeholder: 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.

5. Completing the Course Challenge: At the end of this course, you will be able to put everything you have learned into practice with the Course Challenge. The Course Challenge will ask you questions about key principles you have been learning about and then give you an opportunity to apply those principles in three scenarios.

Learning Log: Consider what data means to you

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Your data analytics certificate ROADMAP

Take action with data

Data analytics case study

• Problem: The owner wanted to expand his business. He knew advertising is a proven way to get more customers, but he wasn't sure where to start. There are all kinds of different advertising strategies, including print, billboards, TV commercials, public transportation, podcasts, and radio.

• Solution:

One of the key things to think about when choosing an advertising method is your target audience, in other words, the specific people you're trying to reach.

For example, if a medical equipment manufacturer wanted to reach doctors, placing an ad in a health magazine would be a smart choice. Or if a catering company wanted to find new cooks, it might advertise using a poster at a bus stop near a cooking school. Both of these are great ways to get your ad seen by your target audience.

The second thing to think about is your budget and how much the different advertising methods will cost.

For instance, a TV ad is likely to be more expensive than a radio ad. A large billboard will probably cost more than a small poster on the back of a city bus.

The business owner asked a data analyst, Maria, to make a recommendation. She started with the first step in the data analysis process, Ask. Maria began by defining the problem that needed to be solved. To do this, she first had to zoom out and look at the whole situation in context. That way she could be sure that she was focusing on the real problem and not just its symptoms. This leads us to another important part of the problem-solving process, collaborating with stakeholders and understanding their needs.

For Anywhere Gaming Repair, stakeholders included the owner, the vice president of communications, and the director of marketing and finance. Working together, Maria and the stakeholders agreed on the problem, not knowing their target audience's preferred type of advertising.

The next step was the prepare phase, where Maria collected data for the upcoming analysis process. But first, she needed to better understand the company's target audience, people with video game systems. After that, Maria collected data on the different advertising methods. This way, she would be able to determine which was the most popular one with the company's target audience.

Then she moved on to the process step. Here Maria cleaned the data to eliminate any errors or inaccuracies that could get in the way of the result. As we've learned, when you clean data, you transform it into a more useful format, create more complete information, and remove outliers.

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? Second, where are these people most likely to see an advertisement? Maria, first discovered that people between the ages of 18 and 34 are most likely to make video game-related purchases. She could confirm that Anywhere Gaming Repair's target audience was people 18-34 years old. This was who they should be trying to reach.

This was who they should be trying to reach. With this in mind, Maria then learned that both TV commercials and podcasts are very popular with people in the target audience. Because Maria knew Anywhere Gaming Repair had a limited budget and understood the high cost of TV commercials, her recommendation was to advertise in podcasts because they are more cost-effective.

Now that she had her analysis, it was 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.

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.

Now, you've seen how the six phases of data analysis can be applied to problem solving and how you can use that to solve real world problems.

From issue to action: The six data analysis phases

Questions to ask yourself in this step:

1. What are my stakeholders saying their problems are?
2. Now that I’ve identified the issues, how can I help the stakeholders resolve their questions?

Questions to ask yourself in this step:

1. What do I need to figure out how to solve this problem?
2. What research do I need to do?

Questions to ask yourself in this step:

1. What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve?
2. How can I clean my data so the information I have is more consistent?

Questions to ask yourself in this step:

1. What story is my data telling me?
2. How will my data help me solve this problem?
3. Who needs my company’s product or service? What type of person is most likely to use it?

Questions to ask yourself in this step:

1. How can I make what I present to the stakeholders engaging and easy to understand?
2. What would help me understand this if I were the listener?

Questions to ask yourself in this step:

1. How can I use the feedback I received during the share phase (step 5) to actually meet the stakeholder’s needs and expectations?

Nikki: The data process works

"I'm Nikki and I manage the education, evaluation, assessment, and research team. My favorite part of the data analysis process is finding the hardest problem and asking a million questions about it and seeing if it's even possible to get an answer. 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, what we did is we prepared the data by understanding who was the population of the new hires that we were examining. We prepared our data by going through and understanding who our populations were, by understanding 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. Depending on who our stakeholders were, 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. It was really satisfying to know that we have the data to support it and that it really, really worked. And not just that the data was there, but that we knew that our students were learning and that they were more productive, faster back on their jobs."

Solve problems with data

Common problem types

Six common types

1. Making predictions

For example, a hospital system might use remote patient monitoring to predict health events for chronically ill patients. The patients would take their health vitals at home every day, and that information combined with data about their age, risk factors, and other important details could enable the hospital's algorithm to predict future health problems and even reduce future hospitalizations.

Reviewing sales data and noticing that a random Tuesday in August had the highest sales last year

2. Categorizing things

This means assigning information to different groups or clusters based on common features. An example of this problem type is a manufacturer that reviews data on shop floor employee performance. An analyst may create a group for employees who are most and least effective at engineering. A group for employees who are most and least effective at repair and maintenance, most and least effective at assembly, and many more groups or clusters.

Reviewing social media reviews to identify common phrases, categorize them, and group each category into a broader theme

3. Spotting something unusual

In this problem type, data analysts identify data that is different from the norm. An instance of spotting something unusual in the real world is a school system that has a sudden increase in the number of students registered, maybe as big as a 30 percent jump in the number of students. A data analyst might look into this upswing and discover that several new apartment complexes had been built in the school district earlier that year. They could use this analysis to make sure the school has enough resources to handle the additional students.

4. Identifying themes

Going back to our manufacturer that has just reviewed data on the shop floor employees. First, these people are grouped by types and tasks. But now a data analyst could take those categories and group them into the broader concept of low productivity and high productivity. This would make it possible for the business to see who is most and least productive, in order to reward top performers and provide additional support to those workers who need more training.

5. Discovering connections

Here's what I mean; say a scooter company is experiencing an issue with the wheels it gets from its wheel supplier. That company would have to stop production until it could get safe, quality wheels back in stock. But meanwhile, the wheel companies encountering the problem with the rubber it uses to make wheels, turns out its rubber supplier could not find the right materials either. If all of these entities could talk about the problems they're facing and share data openly, they would find a lot of similar challenges and better yet, be able to collaborate to find a solution.

Collaborating with a supplier to discover that bad weather is the most common reason for late deliveries

6. Finding patterns

Ecommerce companies use data to find patterns all the time. Data analysts look at transaction data to understand customer buying habits at certain points in time throughout the year. They may find that customers buy more canned goods right before a hurricane, or they purchase fewer cold-weather accessories like hats and gloves during warmer months. The ecommerce companies can use these insights to make sure they stock the right amount of products at these key times.

The finding patterns problem type could involve using historical data to create a report that shows when batteries on critical equipment have been replaced. Historical patterns can be used to help implement proper maintenance to prevent battery failures in the future.

Six problem types

Making predictions 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.

Categorizing things 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.

Spotting something unusual 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.

Identifying themes 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: categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.

Discovering connections 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.

Finding patterns 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.

Problems in the real world

You've been learning about six common problem types of data analysts encounter, making predictions, categorizing things, spotting something unusual, identifying themes, discovering connections, and finding patterns.

1. Making predictions

Let's think back to our real-world example from a previous video. In that example, Anywhere Gaming Repair wanted to figure out how to bring in new customers. So the problem was, how to determine the best advertising method for anywhere gaming repair's target audience.

To help solve this problem, the company used data to envision what would happen if it advertised in different places. Now nobody can see the future but the data helped them make an informed decision about how things would likely work out. So, their problem type was making predictions.

2. Categorizing things

Let's say a business wants to improve its customer satisfaction levels. Data analysts could review recorded calls to the company's customer service department and evaluate the satisfaction levels of each caller. They could identify certain keywords or phrases that come up during the phone calls and then assign them to categories such as politeness, satisfaction, dissatisfaction, empathy, and more.

Categorizing these keywords gives us data that lets the company identify top-performing customer service representatives and those who might need more coaching. This leads to happier customers and higher customer service scores.

3. Spotting something unusual

Some of you may have a smartwatch, my favorite app is for health tracking. These apps can help people stay healthy by collecting data such as their heart rate, sleep patterns, exercise routine, and much more. There are many stories out there about health apps actually saving people's lives.

One is about a woman who was young, athletic and had no previous medical problems. One night she heard a beep on her smartwatch, a notification said her heart rate had spiked. Now in this example think of the watch as a data analyst. The watch was collecting and analyzing health data. So when her resting heart rate was suddenly 120 beats per minute, the watch spotted something unusual because according to its data, the rate was normally around 70. Thanks to the data her smart watch gave her, the woman went to the hospital and discovered she had a condition that could have led to life-threatening complications if she hadn't gotten medical help.

4. Identifying themes

We see a lot of examples of this in the user experience field. User experience designers study and work to improve the interactions people have with the products they use every day. Let's say a user experience designer wants to see what customers think about the coffee maker his company manufactures. This business collects anonymous survey data from users, which can be used to answer this question. But first, to make sense of it all, he will need to find themes that represent the most valuable data, especially information he can use to make the user experience even better. So the problem the user experience designer's company faces is how to improve the user experience for its coffee makers. The process here is kind of like finding categories for keywords and phrases in customer service conversations.

But identifying themes goes even further by grouping each insight into a broader theme. Then the designer can pinpoint the themes that are most common. In this case, he learned users often couldn't tell if the coffee maker was on or off. He ended up optimizing the design with improved placement and lighting for the on/off button, leading to product improvement and happier users.

5. Discovering connections

This example is from the transportation industry and uses something called third-party logistics. Third-party logistics partners help businesses ship products when they don't have their own trucks, planes, or ships. A common problem these partners face is figuring out how to reduce wait time. Wait time happens when a truck driver from the third-party logistics provider arrives to pick up a shipment but it's not ready. So she has to wait. That costs both companies time and money and it stops trucks from getting back on the road to make more deliveries.

So how can they solve this? Well, by sharing data the partner companies can view each other's timelines and see what's causing shipments to run late. Then they can figure out how to avoid those problems in the future. So a problem for one business doesn't cause a negative impact for the other. For example, if shipments are running late because one company only delivers Mondays, Wednesdays, and Fridays, and the other company only delivers Tuesdays and Thursdays, then the companies can choose to deliver on the same day to reduce wait time for customers.

6. Finding patterns

Oil and gas companies are constantly working to keep their machines running properly. So the problem is, how to stop machines from breaking down. One way data analysts can do this is by looking at patterns in the company's historical data.

For example, they could investigate how and when a particular machine broke down in the past and then generate insights into what led to the breakage. In this case, the company saw a pattern indicating that machines began breaking down at faster rates when maintenance wasn't kept up in 15-day cycles. They can then keep track of current conditions and intervene if any of these issues happen again.

Problem types in data analysis

Making predictions

• Uinsg data to make informed decisions about how things may be in the future

Categorizing things

• Groupding data based on common features.

Spotting something unusual

• Identifying data that is different from the norm.

Identifying themes

• Recognizing broader concepts and trends from categorized data.

Discovering connections

• Identifying similar challenges across different entities - and using data and insights to find common solutions.

Finding patterns

• Using historical data about what happened in the past to understand how likely it is to happen again.

Anmol: From hypothesis to outcome

"Hi, I'm Anmol. I'm the Head of Large Advertiser Marketing Analytics within the Marketing Team at Google. At its core, my job is about connecting the right user with the right message at the right time. The first step is really to get a broad sense of the certain pattern that's occurring. So for example, we know that this particular segment of users is more responsive to this type of content. Once we're able to actually see this hypothesis through the data, we do testing to ensure that the hypothesis is actually correct. So for example, we would test sending these pieces of content to this segment of users, and actually verify within a controlled environment whether that response rate is actually higher for that type of content, or whether it isn't. Once we're able to actually verify that hypothesis, we go back to the stakeholders, in this case, our marketers, and say, we've proven within a relatively high degree of certainty that this particular segment is more responsive to this type of content, and because of that, we're recommending that you produce more of this type of content. Our stakeholders really get to see the whole evolution from hypothesis to proven concept, and they're able to come with us on the journey on how we're proving out these hypotheses and then eventually turning them into strategies and recommendations for the business. The outcome in this case was that we were able to actually change the way our whole marketing team worked to actually make it much more user-centric. Instead of, from our perspective, coming up with content that we think the users need, we're actually going in the other direction of figuring out what users need first, proving that they need certain things or they don't need certain things, and then using that information going back to marketers and coming up with content that fulfills their need. So it really changed the direction of how we produce things."

Craft effective questions

SMART questions

Now that we've talked about six basic problem types, it's time to start solving them. To do that, data analysts start by asking the right questions. We're going to learn how to ask effective questions that lead to key insights you can use to solve all kinds of problems.

As a data analyst, I ask questions constantly. It's a huge part of the job. If someone requests that I work on a project, I ask questions to make sure we're on the same page about the plan and the goals.

Is the data showing me something superficially? Is there a conflict somewhere that needs to be resolved? The more questions you ask, the more you'll learn about your data, and the more powerful your insights will be at the end of the day.

Some questions are more effective than others. Let's say you're having lunch with a friend and they say, "These are the best sandwiches ever, aren't they?" Well, that question doesn't really give you the opportunity to share your own opinion, especially if you happen to disagree and didn't enjoy the sandwich very much. This is called a leading question because it's leading you to answer in a certain way.

Or maybe you're working on a project and you decide to interview a family member. Say you ask your uncle, did you enjoy growing up in Malaysia? He may reply, "Yes." But you haven't learned much about his experiences there. Your question was closed-ended. That means it can be answered with a yes or no. These kinds of questions rarely lead to valuable insights.

Now what if someone asks you, do you prefer chocolate or vanilla? Well, what are they specifically talking about? Ice cream, pudding, coffee flavoring, or something else? What if you like chocolate ice cream but vanilla in your coffee? What if you don't like either flavor? That's the problem with this question. It's too vague and lacks context. **Knowing the difference between effective and ineffective questions is essential for your future career as a data analyst. **

After all, the data analyst process starts with the ask phase. So it's important that we ask the right questions.

Effective questions follow the SMART methodology. That means they're specific, measurable, action-oriented, relevant and time-bound.

Specific

That question is much more specific and can give you more useful information. Now, let's talk about measurable questions.

Measurable

That question is measurable because it lets us count the shares and arrive at a concrete number.

Action-oriented

You might remember that problem-solving is about seeing the current state and figuring out how to transform it into the ideal future state. Well, action-oriented questions help you get there.

This brings you answers you can act on.

Relevant questions Let's say you're working on a problem related to a threatened species of frog.

This question would give us answers we can use to help solve our problem.

Time-bound questions

The time period we want to study is 1983 to 2004.

This limits the range of possibilities and enables the data analyst to focus on relevant data.

As a quick reminder

There we had an unfair question because it was phrased to lead you toward a certain answer. This made it difficult to answer honestly if you disagreed with the sandwich's quality.

Another common example of an unfair question is one that makes assumptions. For instance, let's say a satisfaction survey is given to people who visit a science museum. If the survey asks, what do you love most about our exhibits? This assumes that the customer loves the exhibits which may or may not be true. Fairness also means crafting questions that make sense to everyone.

It's important for questions to be clear and have straightforward wording that anyone can easily understand. Unfair questions also can make your job as a data analyst more difficult. They lead to unreliable feedback and missed opportunities to gain some truly valuable insights. You've learned a lot about how to craft effective questions, like how to use the SMART framework while creating your questions and how to ensure that your questions are fair and objective.

More about SMART questions

Self-Reflection: Data analyst scenarios

#####The scenario

You 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 “deep dive” into this data. To get this project started, you must ask some questions and get some information.

SMART questions As 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.

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Ask the right type of questions You 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:

• Objectives

• Audience

• Time

• Resources

• Security

Think about how you can ask SMART questions about each of these topics.

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Consider 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?

Here are a few questions you might want to ask:

• When is the project due?
• Are there any specific challenges to keep in mind?
• Who are the major stakeholders for this project, and what do they expect this project to do for them?
• Who am I presenting the results to?

Here are some examples of questions you might ask based on the suggested topics:

• Objectives: What are the goals of the deep dive? What, if any, questions are expected to be answered by this deep dive?
• Audience: Who are the stakeholders? Who is interested or concerned about the results of this deep dive? Who is the audience for the presentation?
• Time: What is the time frame for completion? By what date does this need to be done?
• Resources: What resources are available to accomplish the deep dive's goals?
• Security: Who should have access to the information?

These questions can help you focus on techniques and analyses that produce results of interest to stakeholders. They also clarify the deliverable’s due date, which is important to know so you can manage your time effectively. When you start work on a project, you need to ask questions that align with the plan and the goals and help you explore the data. The more questions you ask, the more you learn about your data, and the more powerful your insights will be.

Asking thorough and specific questions means clarifying details until you get to concrete requirements. With clear requirements and goals, it’s much easier to plan and execute a successful data analysis project and avoid time-consuming problems down the road.

Evan: Data opens doors

"Hi, I'm Evan. I'm a learning portfolio manager here at Google, and I have one of the coolest jobs in the world where I get to look at all the different technologies that affect big data and then work them into training courses like this one for students to take. I wish I had a course like this when I was first coming out of college or high school. It was honestly a data analyst course that's geared in the way like this one is if you've already taken some of the videos really prepares you to do anything you want. It will open all of those doors that you want for any of those roles inside of the data curriculum. Well, what are some of those roles? There are so many different career paths for someone who's interested in data.

Generally, if you're like me, you'll come in through the door as a data analyst maybe working with spreadsheets, maybe working with small, medium, and large databases, but all you have to remember is 3 different core roles. Now there's many in special, whether specialties, within each of these different careers, but these three are the data analysts, which is generally someone who works with SQL, spreadsheets, databases, might work as a business intelligence team creating those dashboards.

Now where does all that data come from? Generally, a data analyst will work with a data engineer to turn that raw data into actionable pipelines.

So you have data analysts, data engineers, and then lastly, you might have data scientists who basically say the data engineers have built these beautiful pipelines. Sometimes the analyst do that too. The analysts have provided us with clean and actionable data. Then the data scientists then worked actually to turn it into really cool machine learning models or statistical inferences that are just well beyond anything you could have ever imagined. We'll share a lot of resources in links for ways that you can get excited for each of these different roles. And the best part is, if you're like me when I went into school, I didn't know what I wanted to do and you don't have to know at the outset which path you want to go down. Try 'em all. See what you really, really like. It's very personal. Becoming a data analyst is so exciting. Why? Because it's not just like a means to an end. It's just taking a career path where so many bright people have gone before and have made the tools and technologies that much easier for you and me today. For example, when I was starting to learn SQL or the structured query language that you're going to be learning as part of this course, I was doing it on my local laptop and each of the queries would take like 20, 30 minutes to run and it was very hard for me to keep track of different SQL statements that I was writing or share them with somebody else. That was about 10 or 15 years ago. Now, through all the different companies and all the different tools that are making data analysis tools and technologies easier for you, you're going to have a blast creating these insights with a lot less of the overhead that I had when I first started out. So I'm really excited to hear what you think and what your experience is going to be."

[Self-Reflection: Ask your own SMART questions]

click this pdf important, worthwhile to review

my chitchat insights with chatgpt or it could have been like how many persons shop online using the app (and which app) for your store? and the specific ages, peak times in the day etc.

Module 1 challenge

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Course2 Module 1 Glossary