2.1.1.Problem solving and effective questionning
Welcome to the second course of the Google Data Analytics Certificate! In this part of the program, you will learn how data analysts use structured thinking to tackle business problems. Think of yourself as a great detective who figures out a case by tracking down the evidence and who organizes it into a powerful story to solve the mystery. You will explore how to ask effective questions and use the answers to tell a meaningful story about the data. Plus, you will learn why it is so important to be on the same page as your stakeholders when you define the problem and present the data with an analysis.
- Foundations: Data, Data, Everywhere
- Ask Questions to Make Data-Driven Decisions (this course)
- Prepare Data for Exploration
- Process Data from Dirty to Clean
- Analyze Data to Answer Questions
- Share Data Through the Art of Visualization
- Data Analysis with R Programming
- Google Data Analytics Capstone: Complete a Case Study
Course 2 – Ask Questions to Make Data-Driven Decisions
- 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.
- 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.
- 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.
- 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.
- 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.
Each week of this course includes hands-on assignments and projects based on the life of a data analyst. To keep you engaged, each series of lessons offers a lot of different types of learning opportunities, including:
- Videos of instructors teaching new concepts and demonstrating the use of tools
- In-video questions that pop up during or at the end of a video to check your learning
- Readings to introduce new ideas and build on the concepts from the videos
- Discussion forums to discuss, explore, and reinforce new ideas for better learning
- Discussion prompts to promote thinking and engagement in the discussion forums
- Hands-on activities to introduce real-world, on-the-job situations, and the tools and tasks to complete assignments
- Practice quizzes to prepare you for graded quizzes
- Graded quizzes to measure your progress and give you valuable feedback
Hands-on activities promote additional opportunities to build your skills. Try to get as much out of them as possible. Assessments are based on the approach taken by the course to offer a wide variety of learning materials and activities that reinforce important skills. Graded and ungraded quizzes will help the content sink in. Ungraded practice quizzes are a chance for you to prepare for the graded quizzes. Both types of quizzes can be taken multiple times.
As a quick reminder, this course is designed for all types of learners, with no degree or prior experience required. Everyone learns differently, so the Google Data Analytics Certificate has been designed with that in mind. Personalized deadlines are just a guide, so feel free to work at your own pace. There is no penalty for late assignments. If you prefer, you can extend your deadlines by returning to Overview in the navigation pane and clicking Switch Sessions.
If you already missed previous deadlines, click Reset my deadlines instead.
If you would like to review previous content or get a sneak peek of upcoming content, you can use the navigation links at the top of this page to go to another course in the program. When you pass all required assignments, you will be on track to earn your certificate.
And, after you earn the certificate, you will be ready for an entry-level job as a junior or associate data analyst, helping organizations in a variety of industries make decisions with data-driven strategies.
The Google Data Analytics Certificate provides instruction and feedback for learners hoping to earn a position as an entry-level data analyst. While many learners will be brand new to the world of data analytics, others may be familiar with the field and simply wanting to brush up on certain skills.
If you believe this course will be primarily a refresher for you, we recommend taking the practice diagnostic quiz offered this week. It will enable you to determine if you should follow the speed track, which is an opportunity to proceed to Course 3 after having taken each of the Course 2 Weekly Challenges and the overall Course Challenge. Learners who earn 100% on the diagnostic quiz can treat Course 2 videos, readings, and activities as optional. Learners following the speed track are still able to earn the certificate.
- It is strongly recommended to go through the topics in the course in the order they appear because each new section builds on information and concepts covered in previous ones.
- Take advantage of the additional resources that are linked throughout the course. They are designed to support your learning.
- When you encounter useful links in the course, remember to bookmark them so you can refer to the information for study or review.
- Additional resources are free, but some sites place limits on how many articles can be accessed for free each month. Sometimes you can register on the site for full access, but you can always bookmark a resource and come back to view it later.
- Take part in all learning opportunities to gain as much knowledge and experience as possible.
- If something is confusing, feel free to rewatch a video, revisit a reading, or seek out additional resources.
Get ready to take the next step in your data analytics journey!
This course is all about asking good questions and planning out data analysis projects. You have already started learning about how data can be used to answer questions. Now, you’ll complete an entry in your learning log to track your thinking and reflections about what data means to you and how it relates to problem-solving. By the time you complete this activity, you will have a stronger understanding of data and the problem-solving process. This will be important as you learn to ask questions you can answer with data analysis — and you will encounter these questions again later in this course.
Pause for a moment and think about the word “data.” What does it mean to you?
Although it’s clear that data is a major part of a data analyst’s job, it’s only part of the big picture. The other part is problem solving. Being a successful data analyst means understanding that each problem is unique and working methodically to solve that problem with data.
By definition, most new problems data analysts face start in unknown territory. It’s up to the data analyst and their problem solving skills to think strategically, ask good questions, and use data to come up with solutions to these problems.
You’ll reflect on some of these questions in your learning log template which is linked below.
Access your learning log
To use the learning log for this course item, click the link below and select “Use Template.”
Link to learning log template: Consider what data means to you
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
In your learning log template, write 3-5 sentences (60-100 words) reflecting on what data means to you. Here are some questions to help you get started:
- How would you describe “data” to someone who is unfamiliar with the word?
- What does data represent?
- What is data used for?
- Where does data come from?
- How do you get data?
- How do you feel about data?
Then write 2-3 sentences (40-60 words) reflecting on the problem-solving process by answering each of the questions below:
- When you come across a problem and you aren’t sure of the answer or solution, what do you do?
- How do you identify new and interesting problems to begin with? Is there a process you use to identify problems you want to solve?
When you’ve finished your entry in the learning log template, make sure to save the document so your response is somewhere accessible. This will help you continue applying data analysis to your everyday life. You will also be able to track your progress and growth as a data analyst.
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. As you begin learning about the ask phase of the data analysis process, think about how asking the right questions can help you become an expert data detective.
You might consider how asking effective questions has helped you analyze a financial decision, changed the way you tackled a problem at work, or helped you plan a family event. Please include a written response of two or more paragraphs (100-250 words total). Then, visit the discussion forum to read what other learners have written, and engage in discussion within at least two posts.
This reading provides an overview of a speed track we offer to those familiar with data analytics.
If you are brand new to data analytics, you can skip the diagnostic quiz after this reading, and move directly to the next activity: Data in action.
The Google Data Analytics Certificate is a program for anyone. A background in data analysis isn’t required. But you might be someone who has some experience already. If you are this type of learner, we have designed a speed track for this course. Learners who opt for the speed track can refresh on the basic topics and take each of the weekly challenges and the Course Challenge at a faster pace.
To help you decide if you’re a good match for the speed track for this course:
- Take the optional diagnostic quiz.
- Refer to the scoring guide to determine if you’re a good fit for the speed track. A score of 90% or higher is the target goal for someone on the speed track.
- Based on your individual score, follow the recommendations in the scoring guide for your next steps.
Important reminder: If you’re eligible for the speed track, you’re still responsible to complete all graded activities. In order to earn your certificate, you will need an overall score of 80% or higher on all graded materials in the program.
Optional speed track for those experienced in data analytics
The Google Data Analytics Certificate provides instruction and feedback for learners hoping to earn a position as an entry-level data analyst. While many learners will be brand new to the world of data analytics, others may be familiar with the field and simply wanting to brush up on certain skills.
If you believe this course will be primarily a refresher for you, we recommend taking this practice diagnostic quiz. It will enable you to determine if you should follow the speed track, which is an opportunity to proceed to Course 3 after taking each of the Course 2 Weekly Challenges and the overall Course Challenge. Learners who earn 100% on the diagnostic quiz can treat Course 2 videos, readings, and activities as optional. Learners following the speed track are still able to earn the certificate.
Get ready to take the next step in your data analytics journey with the question below!
Categorizing things is one of the six problem types data analysts solve. This type of problem might involve which of the following actions?
A. Analyzing how one action leads to or affects another
B. Using data to envision how something might happen in the future
C. Noticing something outside of the ordinary
D. Classifying or grouping items
The correct answer is D. Classifying or grouping items. Explain: 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?
A. Analyzing how one action leads to or affects another
B. Noticing something outside of the ordinary
C. Identifying trends from historical data
D. Taking categorized items and grouping them into broader topic areas
The correct answer is C. Identifying trends from historical data. Explain: Finding patterns involves identifying trends from historical data.
In the SMART methodology, questions that encourage change are described how?
A. Time-bound
B. Relevant
C. Specific
D. Action-oriented
The correct answer is D. Action-oriented. Explain: Action-oriented questions encourage change.
Fill in the blank: In data analytics, qualitative data _____. Select all that apply.
- is subjective
Explain: Qualitative data is subjective and measures qualities and characteristics.
-
is specific
-
measures qualities and characteristics
Explain: Qualitative data is subjective and measures qualities and characteristics.
- measures numerical facts
In data analytics, how are dashboards different from reports?
A. Dashboards provide a high level look at historical data. Reports provide a more detailed look at live, interactive data.
B. Dashboards monitor live, incoming data from multiple datasets and organize the information into one central location. Reports are static collections of data.
C. Dashboards contain static data. Reports contain data that is constantly changing.
D. Dashboards are used to share updates with stakeholders only periodically. Reports give stakeholders continuous access to data.
The correct answer is B. Dashboards monitor live, incoming data from multiple datasets and organize the information into one central location. Reports are static collections of data. Explain: 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 is effective for analyzing day-to-day decisions. Big data is effective for analyzing more substantial decisions.
Explain: 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.
- Small data focuses on short, well-defined time periods. Big data focuses on change over a long period of time.
Explain: 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.
- Small data involves datasets concerned with a small number of specific metrics. Big data involves datasets that are larger and less specific.
Explain: 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.
- Small data is typically stored in a database. Big data is typically stored in a spreadsheet.
Fill in the blank: Some of the most common symbols used in formulas include + (addition), - (subtraction), * (multiplication), and / (division). These are called _____.
A. domains
B. counts
C. references
D. operators
Explain: Operators are symbols used in formulas, including + (addition), - (subtraction), * (multiplication), and / (division).
In the function =SUM(G1:G35), identify the range.
A. =SUM(G1)
B. =SUM
C. G35
D. G1:G35
The correct answer is D. G1:G35. Explain: 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?
A. Gap analysis
B. Structured thinking
C. Data-driven decision-making
D. Analytical thinking
The correct answer is B. Structured thinking. Explain: 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.
- Consider the best ways to share data with others.
Explain: 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.
- Help team members make informed, data-driven decisions
Explain: 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.
- Use data to get to a solid conclusion
Explain: 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.
- Identify primary and secondary stakeholders
Thank you for taking the optional diagnostic quiz. In order to earn your certificate, you will need an overall score of 80% or higher on the graded materials in this program. Your diagnostic quiz results will help you decide if you should follow the speed track for this course. The speed track allows you to skip over the lesson material and go straight to the weekly challenges and the course challenge, which lead to your overall score. Read on to figure out your next steps based on your quiz score:
If you scored 100% on the diagnostic quiz:
- You are probably familiar with the Ask phase of the data analysis process and can take the speed track to move on to Course 3.
- You must take each of the weekly challenges and the course challenge, which will count toward the 80% overall score needed to earn the certificate. To help you find these items more quickly, we’ve identified them with asterisks in the course materials (for example: course challenge).
- After you complete the weekly challenges and course challenge, proceed to Course 3.
- You are welcome to review videos, readings, and activities throughout the course based on your interests.
If you scored between 90% and 99% on the diagnostic quiz:
- You are probably familiar with the Ask phase of the data analysis process and might consider taking the speed track to move on to Course 3.
- However, we still recommend that you go through the Course 2 lesson materials to review areas where you might have some gaps before proceeding to Course 3.
- You must take each of the weekly challenges and the course challenge, which will count toward the 80% overall score needed to earn the certificate. To help you find these items more quickly, we’ve identified them with asterisks in the course materials (for example: course challenge).
- After you complete the weekly challenges and course challenge, proceed to Course 3.
- You are welcome to review videos, readings, and activities throughout the course based on your interests.
If you scored between 80% and 89% on the diagnostic quiz:
- You likely have some background knowledge on the Ask phase of the data analysis process.
- However, we recommend that you go through the Course 2 lesson materials to review areas where you might have some gaps before proceeding to Course 3.
- You must take the weekly challenges and the course challenge, which will count toward the 80% overall score needed to earn the certificate. To help you find these items more quickly, we’ve identified them with asterisks in the course materials (for example: course challenge).
If you scored less than 80% on the diagnostic quiz:
- No problem — this course was made for you!
- We strongly recommend that you go through all of the Course 2 videos, readings, and activities, as the concepts taught are building blocks that will set you up for success on your learning path.
- You must take the weekly challenges and the course challenge, which will count toward the 80% overall score needed to earn the certificate.