You’ll complete an end-of-course project, gaining an opportunity to apply your new data skills and knowledge from Course 1 to a workplace scenario, and practice solving a business problem.
Learning Objectives
- Describe key findings for a relevant audience member
- Design a project plan for a data science project including questions, details and action items for each project stage
- Apply the PACE workflow to a data science project
Begin building a portfolio to impress
Video: The value of a portfolio
Experiential learning is the best way to confirm that you understand what you have learned. It involves immersing yourself in a situation where you can practice what you have learned, further develop your skills, and reflect on your education.
In the context of this program, experiential learning will give you the opportunity to discover how organizations use data analysis everyday. You will also gain the confidence necessary to discuss them with potential employers.
Soon, you will put experiential learning into practice by working on a portfolio project. A portfolio is a collection of materials that can be shared with potential employers. Portfolios can be stored either on a public or personal website. They can be linked within your digital resume or any online professional presence you may have such as your LinkedIn.
Your portfolio project for this course will involve using the PACE model to set up tasks of a project. Creating a portfolio project is a useful opportunity since companies will often ask you to complete some type of project during the interview process. Employers commonly use this method to assess you as a candidate and gain insight into how you approach common business challenges. Completing this portfolio will prepare you if you encounter this situation when applying for data focus jobs.
Within each portfolio project, you’ll prepare a PACE strategy document. This will help you identify key points within each project to share with the hiring manager, such as the many transferable skills you’ve gained. A transferable skill is a capability or proficiency that can be applied from one job to another. Highlighting your transferable skills is especially important when changing jobs or industries.
The point is you’ve developed the ability to problem solve or keep things organized in one role. You can apply that knowledge anywhere. There are all kinds of transferable skills that you can add to your resume.
By the time you’re done, you’ll not only have a very useful data analysis process document, but also a comprehensive set of artifacts for your portfolio.
Attending a class watching
instructional video or reading information are all great
ways to gain new knowledge. However, there’s nothing like
applying that knowledge. When you actually do something, this really helps confirm that you
understand what you’ve learned. This concept is called experiential
learning which simply means understanding through doing. It involves immersing yourself in
a situation where you can practice what you’ve learned, further develop your
skills and reflect on your education. Experiential learning gives a broader view
of the world, provides valuable insights into your particular interests and
passions and helps build self-confidence. In the context of this program,
experiential learning will give you the opportunity to discover how
organizations use data analysis everyday. This type of activity can help you
identify the specific types of industries and projects that are most interesting and gain the confidence necessary to
discuss them with potential employers. This can really help you stand
out during a job search. Soon, you will put
experiential learning into practice by working on
a portfolio project. A portfolio is a collection of materials
that can be shared with potential employers. Portfolios can be stored either
on a public or personal website. They can be linked within
your digital resume or any online professional presence
you may have such as your LinkedIn. Your portfolio project for this course will involve using the pace
model to set up tasks of a project. Creating a portfolio project is a useful
opportunity since companies will often ask you to complete some type of
project during the interview process. Employers commonly use this method
to assess you as a candidate and gain insight into how you approach
common business challenges. Completing this portfolio will prepare
you if you encounter this situation when applying for data focus jobs. Coming up, you’ll be introduced to the specifics
involving your portfolio project. You’ll also receive clear
instructions to follow. As you begin working,
consider the knowledge and skills you’ve acquired in this course and
how they can be applied to your project. Within each portfolio project,
you’ll prepare a pace strategy document. This will help you identify key points
within each project to share with the hiring manager, such as the many
transferable skills you’ve gained. A transferable skill is a capability or proficiency that can be applied
from one job to another. Highlighting your transferable skills is
especially important when changing jobs or industries. For instance, if you learned how to solve
customer complaints while working as a host at a restaurant,
you could emphasize the transferable skill of problem solving when applying for
a job in the data field. Or maybe you learned how to
meet deadlines, take notes and follow instructions while working
in administration at a non profit organization. You could discuss how your organizational
skills are transferable to the data analysis field. The point is you’ve developed
the ability to problem solve or keep things organized in one role. You can apply that knowledge anywhere. There are all kinds of transferable
skills that you can add to your resume, reflecting on your transferable skills and
the notes you take in your pace strategy document will help you consider how
to convey technical concepts Clearly, this will also help you demonstrate how
you would apply your expertise across all kinds of tools and
scenarios in the data career space. And by the time you’re done, you’ll not
only have a very useful data analysis process document, but also a comprehensive
set of artifacts for your portfolio. Sounds exciting, doesn’t it? Let’s get going.
Video: Introduction to your Course 1 end-of-course portfolio project
Having a portfolio is incredibly common in the data field, and it can help you stand out from other candidates. Your portfolio can showcase your ability to understand business scenarios, communicate effectively, and use tools to solve complex problems.
In this course, you have gained the knowledge and skills to successfully complete your portfolio project. You have learned about the role of data professionals within an organization, typical career paths, core analysis practices and tools, and how data professionals use them to make a positive impact. You will also apply what you have learned about team members, stakeholders, and clients.
To begin, you will read the specific project you will be working on. This will describe the type of organization, the people involved, the business problem to be solved, and other key details. This will enable you to further define the project, understand the stakeholders, and consider key questions to answer in order to achieve a successful result.
Then, you will create a PACE strategy document, outlining the project’s purpose, stakeholders, deliverables, and much more. In this document, you will begin to integrate the PACE model to identify steps at each stage in the project.
For each portfolio project, you will continue to use the PACE model to guide you. By completing each PACE strategy document, you will be well on your way to developing your own data analytic workflow.
In later courses, you will continue working on your portfolio project and continue using the PACE model to guide your process. By the time you are done, you will have designed something that you can use to really impress hiring managers. Plus, you will have a dynamic example of your data analytic skills demonstrating your thought process, approach to the problems, the key skills you have gained, and lots more.
These are all great things to talk about during an interview.
When I interview people for
jobs here at Google, I love checking out
their online portfolios. I find that I feel more confident in
candidates who can demonstrate their knowledge in a clear and
compelling format. Having a portfolio has become
incredibly common in the data field. During a job hunt, it’s so valuable to showcase your ability
to understand business scenarios, communicate effectively and
use tools to solve complex problems. Your portfolio can really help you
stand out from other candidates. So far in this course,
you gain lots of knowledge and job ready skills to help you excel. You’ve discovered the role of data
professionals within an organization and typical career paths. You’ve explored core analysis
practices and tools and witnessed how data professionals
use them to make a positive impact. All of these things will help you
successfully complete your portfolio project. In addition, you will apply what you’ve
learned about team members, stakeholders, and clients, such as their
particular roles or priorities. You’ll begin by reading the specific
project, you’ll be working on. This reading will describe the type
of organization you’re working with, the people involved, the business problem
to be solved and other key details. This will enable you to further define the
project, understand the stakeholders, and consider key questions to answer in
order to achieve a successful result. Then you will create a PACE strategy
document, outlining the project’s purpose, stakeholders, deliverables, and much more. In this document, you will begin to
integrate the PACE model to identify steps at each stage in the project. For each portfolio project, you will continue to use
the PACE model to guide you. By completing each PACE strategy document, you will be well on your way to developing
your own data analytic workflow. Then in later courses, you will continue
working on your portfolio project and continue using the PACE
model to guide your process. And by the time you’re done, you have designed something that you can
use to really impress hiring managers. Plus you have a dynamic example of your
data analytic skills demonstrating your thought process, approach to the problems, the key skills you’ve gained,
and lots more. These are all great things to
talk about during an interview. All right, let’s get started. It’s time to discover how you will
help an organization advance through the exciting world of data.
Reading: End-of-course portfolio project introduction
Reading
Each course in the Google Advanced Data Analytics Certificate concludes with a project that provides hands-on opportunities to practice your knowledge. These end-of-course portfolio projects build across courses to simulate the full lifecycle of a data project, just like job tasks that you will encounter as a data professional.
After completing all of the courses in this certificate program, you are encouraged to compile the projects in a portfolio that highlights your data analytics skills. You will learn how to create a portfolio in the final course of this certificate program: Google Advanced Data Analytics Capstone. In addition, the end-of-course projects can also be used to help you prepare for job applications and interviews since they showcase the valuable skills that you bring to the world of data analytics.
Importance of communication in the data career space
End-of-course projects emphasize technical and professional workplace skills, as well as the importance of communication in data analytics. The success of your workflow management, data analysis, data visualizations, statistics, regression analysis, and building machine learning models relies on your ability to communicate with cross-functional team members. So, each project is designed to help you develop critical communication skills that you’ll need to use on the job in order to effectively:
- Ask questions
- Share project needs
- Communicate with stakeholders
- Give and receive feedback
- Stay in contact with team members on the project
Expectations
As previously mentioned, you will encounter an end-of-course project in the final module of each course. The end-of-course projects were designed around a workplace scenario that will guide you through tasks that align with the concepts and skills you learned in each course. The workplace scenario extends across all of the certificate’s end-of-course projects.
You will be given the tools, resources, and instructions needed to apply your new skills and complete each individual end-of-course project. Additionally, you’ll also have access to helpful resources designed to guide and inspire your data analysis workflow. In the end, your effort will be rewarded with work examples that demonstrate the effectiveness of your data analysis. If you get stuck at any point, there are links to review relevant information within each course.
To help you complete each end-of-course project, you will receive a PACE (Plan, Analyze, Construct, and Execute) strategy document to guide your workflow. As you answer the questions, you will gain a deeper understanding of the data analysis process, document your growth as a data professional, and prepare the observations and reflections needed to complete each project.
Start your project
To complete the end-of-course portfolio project, you will first need to:
- Gather information about the business problem or question to be answered.
- Respond to key questions posed in the PACE strategy document.
- Create a project proposal for cross-functional team members.
In the next reading, you will learn about the workplace scenarios and how to get started on your end-of-course project. Good luck!
Reading: Explore your Course 1 workplace scenarios
Reading
Overview
This certificate offers you a choice of several different workplace scenarios to use when completing each end-of-course project:
- Automatidata, featuring a fictional data consulting firm
- TikTok, created in partnership with the short-form video hosting company
- Waze, created in partnership with the realtime driving directions app
Each scenario offers you an opportunity to apply your skills and create work samples to share when applying for jobs; so, you will be practicing similar skills regardless of the workplace scenario. It is recommended that you work with the same scenario for each end-of-course project to have a cohesive experience. However, you are welcome to investigate any of the workplace scenarios you are interested in as you progress through the program.
Reminder: We recommend that you choose one workplace scenario to follow for all end-of-course projects to ensure end-to-end project development.
The minimum requirement to earn your Advanced Data Analytics Certificate is to complete the end-of-course project, using one workplace scenario, for each course. You may complete the project for as many of the workplace scenarios as you wish. Completing the project for more than one workplace scenario in a single course offers you additional practice and work examples you can add to your portfolio and share with prospective employers during your job search.
This reading offers an overview of all available workplace scenarios. Before moving on, identify the scenario you would like to complete for the Course 1 end-of-course project.
Course 1 workplace scenarios
Automatidata
Project goal:
In this fictional scenario, the New York City Taxi and Limousine Commission (TLC) has approached the data consulting firm Automatidata to develop an app that enables TLC riders to estimate the taxi fares in advance of their ride.
Background:
Since 1971, TLC has been regulating and overseeing the licensing of New York City’s taxi cabs, for-hire vehicles, commuter vans, and paratransit vehicles.
Scenario:
You are a newly-hired data professional at Automatidata, a fictional data consulting firm. Automatidata’s focus is to help clients transform their unused and stored data into useful solutions. In this scenario, you will consult with The New York City Taxi & Limousine Commission to develop an app that will help users (TLC riders) estimate their taxi fares before their ride. Your first responsibility as a data analytics consultant will be to structure the necessary tasks into a project proposal that establishes milestones for the ride fare data project.
Course 1 tasks:
- Gather information from the notes from the last executive meeting of Automatidata
- Assign PACE stages to the requested tasks
- Organize tasks into milestones
- Create a project proposal for the executive team’s approval
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. And, the data shared in this project has been created for pedagogical purposes.
TikTok
Project goal:
The TikTok data team is developing a machine learning model for classifying claims made in videos submitted to the platform.
Background:
TikTok is the leading destination for short-form mobile video. The platform is built to help imaginations thrive. TikTok’s mission is to create a place for inclusive, joyful, and authentic content–where people can safely discover, create, and connect.
Scenario:
As a data analyst on TikTok’s data team, with new considerations from the leadership team, you will create a project proposal by assigning the required data analytical tasks into realistic milestones that will advise future steps in the claims classification project.
Course 1 tasks:
- Gather information from stakeholder notes from within TikTok
- Assign PACE stages to the requested tasks for the classification project
- Organize tasks into milestones
- Create a project proposal for the TikTok data team
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. And, the data shared in this project has been created for pedagogical purposes.
Waze
Project goal:
Waze leadership has asked your data team to develop a machine learning model to predict user churn. Churn quantifies the number of users who have uninstalled the Waze app or stopped using the app. This project focuses on monthly user churn. An accurate model will help prevent churn, improve user retention, and grow Waze’s business.
Background:
Waze’s free navigation app makes it easier for drivers around the world to get to where they want to go. Waze’s community of map editors, beta testers, translators, partners, and users helps make each drive better and safer.
Scenario:
You are the newest member of Waze’s data team. Your team is about to begin their user churn project. The first step is to create a project proposal. The proposal will clearly define the overall goal of the project, and identify key tasks, milestones, and stakeholders.
Course 1 tasks:
- Assign PACE stages to the requested tasks for the user churn project
- Organize tasks into milestones
- Create a project proposal for the Waze data team
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. And, the data shared in this project has been created for pedagogical purposes.
Key Takeaways
In Course 1, Foundations of Data Science, you explored the world of data professionals and learned how data analysis contributes to an organization’s goals.
Course 1 skills:
- Effective communication
- Understand cross-functional team dynamics
- Project management
- Share insights and ideas with stakeholders
Course 1 end-of-course project deliverables:
- Complete the Course 1 PACE strategy document
- Create a project proposal
The end-of-course portfolio projects are designed for you to apply your data analytical skills within a workplace scenario. No matter which scenario you work with, you will practice your ability to discuss data analytic topics with coworkers, internal team members, and external clients.
As a reminder, you are required to complete one project for each course. To gain additional practice, or to add more samples to your portfolio, you may complete as many of the scenarios as you wish.
Automatidata scenario
Reading: Course 1 end-of-course portfolio project overview: Automatidata
Reading
Learn about the Course 1 Automatidata workplace scenario!
The end-of-course project in Course 1 focuses on your ability to plan for data projects and create a project proposal. The end-of-course projects were designed with you in mind, offering an opportunity for you to practice and apply your data analytic skills. The materials provided here will guide you through discussions with co-workers, internal team members, and external stakeholders.
Learn more about the project, your role, and expectations in this reading.
Background on the Automatidata scenario
Congrats on your new job as a data analyst at a data consulting firm called Automatidata. Automatidata works with its clients to transform their unused and stored data into useful solutions, such as performance dashboards, customer-facing tools, strategic business insights, and more. They specialize in identifying a client’s business needs and utilizing their data to meet those business needs.
Automatidata is consulting for the New York City Taxi and Limousine Commission (TLC). New York City TLC is an agency responsible for licensing and regulating New York City’s taxi cabs and for-hire vehicles. The agency has partnered with Automatidata to develop a regression model that helps estimate taxi fares before the ride, based on data that TLC has gathered.
The TLC data comes from over 200,000 taxi and limousine licensees, making approximately one million combined trips per day.
Note: This project’s dataset was created for pedagogical purposes and may not be indicative of New York City taxi cab riders’ behavior.
Project background
Automatidata is in the earliest stages of the TLC project. The following tasks are needed before the team can begin the data analysis process:
- A project proposal identifying the following:
- Organize project tasks into milestones
- Classify tasks using the PACE workflow
- Identify relevant stakeholders
Your assignment
For your first assignment, Automatidata will need a project proposal that will create milestones for the tasks within the TLC project. Remember to take into account your audience, team, project goal, and PACE stages of each task in planning your project deliverable.
Team members at Automatidata and the New York City TLC
Automatidata Team Members
- Udo Bankole, Director of Data Analysis
- Deshawn Washington, Data Analysis Manager
- Luana Rodriquez, Senior Data Analyst
- Uli King, Senior Project Manager
Your teammates at Automatidata have technical experience with data analysis and data science. However, you should always be sure to keep summaries and messages to these team members concise and to the point.
New York City TLC Team Members
- Juliana Soto, Finance and Administration Department Head
- Titus Nelson, Operations Manager
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. The data shared in this project has been altered for pedagogical purposes.
The TLC team members are program managers who oversee operations at the organization. Their roles are not highly technical, so be sure to adjust your language and explanation accordingly.
Meeting notes
Now that you are working as Automatidata’s latest data analytics professional, you are given access to the company network and set up with a company email account (your first initial and last name, followed by @automatidata.org).
Opening your inbox, you notice an email from your supervisor, Deshawn.
From: Deshawn Washington
Subject: Review meeting notes
If you are able to read this, then your company accounts have been created! Now is the perfect time to get started. Last week, I attended an internal meeting with our leadership team about a new project we are about to begin. You’ll receive more information in the next few days, but I would like you to be aware of some needs that were identified by our leadership team. Here is an excerpt from the notes I took during the Automatidata leadership team meeting. I’ve organized the points by the person who made them.
Uli King (Senior Project Manager)
- The data team will need a global-level project document to outline the goals and milestones.
- I am working closely with Titus Nelson over at the New York City Taxi and Limo Commission. He has requested some visuals to share with TLC’s executives.
Luana Rodriquez (Senior Data Analyst)
- The dataset from TLC has to be inspected before any analysis can begin.
- Our team needs to determine what information the TLC data provides through exploratory data analysis (EDA).
- Eventually, our team will need to test to find if the model is delivering consistent results.
Udo Bankole (Director of Data Analysis)
- Before we present any insights to TLC, we’ll need to determine whether or not the model we produce meets the project requirements.
- Once we have a final model, I’ll need to know the main talking points going into our presentation with TLC.
My thoughts and concerns…
- I think it’s best to use Python for the TLC project. I’ll have someone on my team set that up as soon as we have the plan in place.
- It will be important to establish the relationship between any variables within the TLC data. I’d suggest the data team consider A/B testing, since that will analyze the relationship between the two most useful variables and subsequently provide data-driven support for future business decisions.
Review the meeting notes above to become familiar with the project’s context. I’ll ask you to identify project tasks and come up with a structure to guide the data team through this project. After our discussion about your experience in the certificate program offered by Google, I know that your efficient communication style and problem-solving will enhance the abilities of the data team.
There will be more details sent to you very soon.
Welcome to the team,
Deshawn Washington
Data Analysis Manager
Automatidata
(P.S. There will be muffins in the break room every Tuesday morning. Be early…unless you like bran muffins. LOL)
Specific project deliverables
With this end-of-course project, you will gain valuable practice and apply your new skills as you complete the following:
- Course 1 PACE Strategy Document to plan your project while considering your audience members, teammates, key milestones, and overall project goal.
- Create a project proposal for the data team.
Key takeaways
The Google Advanced Data Analytics Certificate end-of-course project is designed for you to practice and apply course skills in a fictional workplace scenario. By completing each course’s end-of-course project, you will have work examples that will enhance your portfolio and showcase your skills for future employers.
Practice Quiz: Activity: Create your Course 1 Automatidata project
Reading: Activity Exemplar: Create your Course 1 Automatidata project
Reading
Completed Exemplars
To review the exemplars for this course item, click the following link and select Use Template.
Assessment of Exemplars
Course 1 PACE strategy document
Compare the exemplar to your completed PACE strategy document. Your responses may differ from the exemplar, but that is to be expected. What did you do well? Where can you improve? Use your answers to these questions to guide you as you progress through the course.
Note: The exemplar represents one possible way to address the PACE questions and classify project tasks according to the PACE workflow. Yours might differ in certain ways, such as your specific responses to questions. What’s important is that you have a general idea of the order and PACE-stage classification for tasks in the data analysis process.
Your replies to the Course 1 PACE strategy document should:
- Be relevant to the Automatidata project goals and related to data science
- Demonstrate your understanding and assessment of the business scenario
- Communicate the reasoning behind your decisions to pair PACE stages with specific tasks
Course 1 Automatidata project proposal
Compare the exemplar to the Automatidata project proposal you completed. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you progress through the course.
Note: The exemplar represents one possible way to complete the Automatidata project proposal. Yours might differ in certain ways, such as specific task assignments, PACE stages, or relevant stakeholders. What’s important is that you have an overall understanding of the purpose and organization of project proposals for data-driven work.
Your project proposal should:
- Clearly communicate the overall project goal
- Identify key tasks, milestones, and stakeholders
Tiktok scenario
Reading: Course 1 end-of-course portfolio project overview: TikTok
Reading
Learn about the Course 1 TikTok workplace scenario!
This end-of-course project was designed with you in mind, creating an opportunity for you to practice and apply your data analytic skills. The materials provided here will guide you through discussions with co-workers, internal team members, and external stakeholders.
Learn more about the project, your role, and expectations in this reading.
Background on the TikTok scenario
Welcome to your new role at TikTok! We’re thrilled to have you on the data team!
At TikTok, our mission is to inspire creativity and bring joy. Our employees lead with curiosity and move at the speed of culture. Combined with our company’s flat structure, you’ll be given dynamic opportunities to make a real impact on a rapidly expanding company and grow your career.
TikTok users have the ability to report videos and comments that contain user claims. These reports identify content that needs to be reviewed by moderators. This process generates a large number of user reports that are difficult to address quickly.
TikTok is working on the development of a predictive model that can determine whether a video contains a claim or offers an opinion. With a successful prediction model, TikTok can reduce the backlog of user reports and prioritize them more efficiently.
Project background
TikTok’s data team is in the earliest stages of the claims classification project. The following tasks are needed before the team can begin the data analysis process:
- A project proposal identifying the following:
- Organize project tasks into milestones
- Classify tasks using the PACE workflow
- Identify relevant stakeholders
Your assignment
For your first assignment, TikTok is asking for a project proposal that will create milestones for the tasks within the comment classification project. Remember to take into account your audience, team, project goal, and PACE stages of each task in planning your project deliverable.
Team members at TikTok
As a new data analyst, you’ll work closely with a talented team of experienced data professionals. You’ll also collaborate with TikTok co-workers outside the data team as your project requires.
Data team roles
- Willow Jaffey- Data Science Lead
- Rosie Mae Bradshaw- Data Science Manager
- Orion Rainier- Data Scientist
Cross-functional team members
- Mary Joanna Rodgers- Project Management Officer
- Margery Adebowale- Finance Lead, Americas
- Maika Abadi- Operations Lead
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. The data shared in this project has been altered for pedagogical purposes.
Your TikTok team includes several managers overseeing operations. It is important to adapt your communication to their roles since their responsibilities are less technical. The members of the data team at TikTok are well versed in data analysis and data science. Messages to these more technical co-workers should be concise and specific.
Meeting notes
Now that you are onboard, you have access to the company network and are set up with a company email account.
You notice an email from your supervisor at the top of your inbox.
From: Rosie Mae Bradshaw
Subject: Your first task: Review meeting notes
If you are able to read this, then your company accounts have been created! It’s the perfect time to get started with your first task. Last week, I attended an internal meeting with our leadership team about a new project we are about to begin. You’ll receive more information in the next few days, but I would like you to be aware of some needs that were identified by our leadership. Here is an excerpt from the notes I took during the leadership team meeting. I’ve organized the points by the individual who made the comment:
Mary Joanna Rodgers- Project Management Officer
- The project will need a global document identifying deliverables & milestones.
- We will need to generate some visuals to share with TikTok executives.
Orion Rainier-Data Scientist
- We will need to have an understanding of the data we will be working with on this project.
- It will require someone on the team to inspect the dataset for missing data.
- The data team needs to determine what information will be the most useful in TikTok’s data through exploratory data analysis (EDA).
- Eventually, our team will need to run some method of statistical testing.
Willow Jaffey- Data Science Lead
- The goal of the claim classification project is to build and deliver a reliable machine learning model.
- The team will need to determine the type of regression model for this project.
- Once we have a final model, I’ll need to know the main talking points going into our presentation to the leadership team.
- The team will need to determine the best method of hypothesis testing for this dataset.
My thoughts and concerns…
- I think it’s best to use Python for this project. I’ll have someone on my team set that up as soon as we have the project proposal in place.
- I’d suggest the data team will need to check the assumptions made by any regression modeling developed for this project.
- The data team will identify and set up the project’s workflow.
There will be more details sent to you very soon.
Welcome to the team,
Rosie Mae Bradshaw
Data Science Manager
TikTok
(P.S. Don’t forget about the company-sponsored lunch on Friday. The food is always fantastic. It’s a great opportunity to meet more of our wonderful co-workers. Our team meets in the lobby around noon. Please join us!)
Specific project deliverables
With this end-of-course project, you will gain valuable practice and apply your new skills as you complete the following deliverables:
- Course 1 PACE Strategy Document to plan your project while considering your audience members, teammates, key milestones, and overall project goal.
- Create a project proposal for the data team.
Key takeaways
The Google Advanced Data Analytics Certificate end-of-course project is designed for you to practice and apply course skills in a fictional workplace scenario. By completing each project, you will have work examples that will enhance your portfolio and showcase your skills for future employers.
Practice Quiz: Activity: Create your Course 1 TikTok project
Reading: Activity Exemplar: Create your Course 1 TikTok project
Reading
Completed Exemplars
To review the exemplars for this course item, click the following link and select Use Template.
Link to exemplars:
Assessment of Exemplar
Course 1 PACE strategy document
Compare the exemplar to your completed PACE strategy document. Your responses may differ from the exemplar, but that is to be expected. What did you do well? Where can you improve? Use your answers to these questions to guide you as you progress through the course.
Note: The exemplar represents one possible way to address the PACE questions and classify project tasks according to the PACE workflow. Yours might differ in certain ways, such as your specific responses to questions. What’s important is that you have a general idea of the order and PACE-stage classification for tasks in the data analysis process.
Your replies to the Course 1 PACE strategy document should:
- Be relevant to the TikTok project goals and related to data science
- Demonstrate your understanding and assessment of the business scenario
- Communicate the reasoning behind your decisions to pair PACE stages with specific tasks
Course 1 TikTok project proposal
Compare the exemplar to the TikTok project proposal you completed. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you continue to progress through the course.
Note: The exemplar represents one possible way to complete the TikTok project proposal. Yours might differ in certain ways, such as specific task assignments, PACE stages, or relevant stakeholders. What’s important is that you have an overall understanding of the purpose and organization of project proposals for data-driven work.
Your project proposal should:
- Clearly communicate the overall project goal
- Identify key tasks, milestones, and stakeholders
Waze scenario
Reading: Course 1 end-of-course portfolio project overview: Waze
Reading
Learn about the Course 1 Waze workplace scenario!
The end-of-course project in Course 1 focuses on your ability to plan for data projects and create a project proposal. The end-of-course projects were designed with you in mind, offering an opportunity for you to practice and apply your data analytic skills. The materials provided here will guide you through discussions with co-workers, internal team members, and external stakeholders.
Learn more about the project, your role, and expectations in this reading.
Background on the Waze scenario
Welcome to your new role at Waze! We’re thrilled to have you on the data team!
Waze’s free navigation app makes it easier for drivers around the world to get to where they want to go. Waze’s community of map editors, beta testers, translators, partners, and users helps make each drive better and safer. Waze partners with cities, transportation authorities, broadcasters, businesses, and first responders to help as many people as possible travel more efficiently and safely.
You’ll collaborate with your Waze teammates to analyze and interpret data, generate valuable insights, and help leadership make informed business decisions. Your team is about to start a new project to help prevent user churn on the Waze app. Churn quantifies the number of users who have uninstalled the Waze app or stopped using the app. This project focuses on monthly user churn. In your role, you will analyze user data and develop a machine learning model that predicts user churn.
This project is part of a larger effort at Waze to increase growth. Typically, high retention rates indicate satisfied users who repeatedly use the Waze app over time. Developing a churn prediction model will help prevent churn, improve user retention, and grow Waze’s business. An accurate model can also help identify specific factors that contribute to churn and answer questions such as:
- Who are the users most likely to churn?
- Why do users churn?
- When do users churn?
For example, if Waze can identify a segment of users who are at high risk of churning, Waze can proactively engage these users with special offers to try and retain them. Otherwise, Waze may simply lose these users without knowing why.
Your insights will help Waze leadership optimize the company’s retention strategy, enhance user experience, and make data-driven decisions about product development.
Project background
Waze’s data team is in the earliest stages of the churn project. The following tasks are needed before the team can begin the data analysis process:
- A project proposal identifying the following:
- Organize project tasks into milestones
- Classify tasks using the PACE workflow
- Identify relevant stakeholders
Your assignment
For your first assignment, Waze is asking for a project proposal that will create milestones for the tasks within this project. Remember to take into account your audience, team, project goal, and PACE stages of each task in planning your project deliverable.
Note: This project’s dataset was created for pedagogical purposes and does not represent Waze’s actual data.
Team members at Waze
As a new data analyst, you’ll work closely with a talented team of experienced data professionals. You’ll also collaborate with Waze co-workers outside the data team as your project requires.
Data team roles
- Harriet Hadzic – Director of Data Analysis
- May Santner – Data Analysis Manager
- Chidi Ga – Senior Data Analyst
- Sylvester Esperanza – Senior Project Manager
Other roles in the scenario
- Emrick Larson – Finance and Administration Department Head
- Ursula Sayo – Operations Manager
Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. The data shared in this project has been altered for pedagogical purposes.
The members of the data team have technical expertise in data analysis, and are familiar with terms and concepts from statistics and machine learning. To communicate effectively with data team members, make sure your messages are clear, concise, and focused on key points.
Waze co-workers outside the data team may not have a background in data analysis, or be familiar with technical terms and concepts. When communicating with these co-workers, make sure to adjust your language to fit your audience, and clearly explain technical terms if necessary.
Meeting notes
After onboarding, you are given access to the company network and set up with a company email account (your first initial and last name, followed by @Waze).
Opening your inbox, you notice an email from your supervisor, May Santner.
From: “May Santner,” May@waze.com
Subject: Your first task: Review meeting notes
If you are reading this message, then your company accounts have been created! Now is the perfect time to get started. Last week, I attended an internal meeting with our leadership team about a new project. You’ll receive more information in the next few days, but I’d like you to be aware of some key points made by leadership. Here is an excerpt from my meeting notes. I’ve organized the points by the person who made them.
Sylvester Esperanza, Senior Project Manager
- The data team will need a global-level project document to outline project goals and milestones.
- We will need to generate some visuals to share with the Waze executives.
Chidi Ga, Senior Data Analyst
- The dataset has to be inspected before any analysis can begin.
- The data team needs to learn more about the data through exploratory data analysis (EDA).
- Eventually, our team will need to test the model to find out if it’s delivering consistent results.
Harriet Hadzic, Director of Data Analysis
- Before we share any insights, we’ll need to determine whether or not our model meets the project requirements.
- Once we finalize the model, I’ll need to know the main talking points for our presentation to the leadership team.
My thoughts and concerns…
- I think it’s best to use Python for this project. I’ll have someone on my team set that up as soon as we have the project proposal in place.
- It will be important to establish the relationship between key variables in the dataset. I’d suggest the data team consider hypothesis testing.
Review the meeting notes and proposed tasks from each team member to become familiar with the project’s context. I’ll ask you to identify project tasks and come up with a structure to guide the data team through this project. After our discussion about your experience in the Google certificate program, I know that your efficient communication style and strong problem-solving skills will help the data team achieve its goals.
There will be more details sent to you soon.
Welcome to the team,
May Santner
Data Analysis Manager
Waze
(P.S. Don’t forget about our Data Superstars discussion group. Our next session is this Friday at 3pm. Each month, the team gets together to discuss the work of an innovative and inspiring historical figure. This month, it’s Ada Lovelace, often considered the world’s first computer programmer. This event is also a great opportunity to get to know your new teammates – and, if you want, try the outstanding coffee in the Waze cafe! Please join us!)
Specific project deliverables
With this end-of-course project, you will gain valuable practice and apply your new skills as you complete the following deliverables:
- Course 1 PACE Strategy Document to plan your project while considering your audience members, teammates, key milestones, and overall project goal.
- Create a project proposal for the data team.
Key takeaways
The Google Advanced Data Analytics Certificate end-of-course project is designed for you to practice and apply course skills in a fictional workplace scenario. By completing each course’s end-of-course project, you will have work examples that will enhance your portfolio and showcase your skills for future employers.
Practice Quiz: Activity: Create your Course 1 Waze project
Reading: Activity Exemplar: Create your Course 1 Waze project
Reading
Completed Exemplars
To review the exemplars for this course item, click the link below and select Use Template.
Assessment of Exemplar
Course 1 PACE strategy document
Compare the exemplar to your completed PACE strategy document. Your responses may differ from the exemplar, but that is to be expected. What did you do well? Where can you improve? Use your answers to these questions to guide you as you progress through the course.
Note: The exemplar represents one possible way to address the PACE questions and classify project tasks according to the PACE workflow. Yours may differ in certain ways, such as your specific responses to questions. What’s important is that you have a general idea of the order and PACE-stage classification for tasks in the data analysis process.
Your replies to the Course 1 PACE strategy document should:
- Be relevant to Waze project goals and related to data science
- Demonstrate your understanding of the business scenario
- Communicate the reasoning behind your decisions to pair PACE stages with specific tasks
Course 1 Waze project proposal
Compare the exemplar to the Waze project proposal you completed. Review your work using each of the criteria in the exemplar. What did you do well? Where can you improve? Use your answers to these questions to guide you as you progress through the course.
Note: The exemplar represents one possible way to complete the Waze project proposal. Yours may differ in certain ways, such as specific task assignments, PACE stages, or relevant stakeholders. What’s important is that you have an overall understanding of the purpose and organization of project proposals for data-driven work.
Your project proposal should:
- Clearly communicate the overall project goal
- Identify key tasks, milestones, and stakeholders
End-of-course portfolio project wrap-up
Video: End-of-course project wrap-up and tips for ongoing career success
You have completed the first portfolio project, which is a great start to your journey as a data professional. The experience you have gained in planning and documenting your approach to the example situation will be valuable in your future job hunt. You will be able to impress hiring managers by discussing your data professional experience, including understanding stakeholder requests, establishing a clear and straightforward project plan, and completing an effective strategy document. You will also understand how to plan out and organize the necessary workflow, which is a big part of a data professional’s process.
It is also important to be able to communicate about your transferable skills with potential employers. The information you have added to your PACE strategy document will be valuable during job interviews.
There will be another opportunity to complete a portfolio project at the end of the next course. At that point, you will use the PACE strategy document that you started here to continue developing your skills as a data professional. Then, at the end of the program, you will bring everything together in order to finalize your unique approach to this example situation. The goal is to have a great example of your work that clearly demonstrates your skills to potential employers.
Congratulations on your progress, and I hope you continue to have a rewarding experience as you work on your portfolio project.
You’ve completed the first
portfolio project, congrats. Planning and documenting
your approach to the example situation is a useful experience as you begin thinking about your
future job hunt. Soon, you’ll be able
to impress hiring managers by discussing your
data professional experience, including understanding
stakeholder requests, establishing a clear and
straightforward project plan, and completing an effective
strategy document. In addition, you’ll
understand how to plan out and organize the
necessary workflow. This is a big part of a
data professionals process. Also, as you’ve learned, it’s helpful to
communicate about your transferable skills
with potential employers. The information you’ve added to your PACE strategy document will be valuable during
job interviews. There will be another
opportunity to complete a portfolio project at the
end of the next course. At that point, you will use the PACE strategy document
that you started here to continue developing your skills as a
data professional. Then at the end of the program, you’ll bring everything
together in order to finalize your unique reproach to
this example situation. The goal is to have a great
example of your work that clearly demonstrate your
skills to potential employers. Congratulations again,
and I hope you have a really rewarding
experience as you continue working on your portfolio project throughout this program.
Quiz: Assess your Course 1 end-of-course project
The Course 1 PACE Strategy Document includes responses for all the questions provided under the Planning Stage section.
Yes
AI assistant
Yes, the Course 1 PACE Strategy Document includes responses for all the questions provided under the Planning Stage section.
The Planning Stage section of the PACE Strategy Document includes the following questions:
- What are the specific goals of the project?
- Who are the stakeholders involved in the project?
- What are the key milestones for the project?
- What are the potential risks and challenges to the project?
- What resources are needed to complete the project?
The PACE Strategy Document should provide comprehensive and well-thought-out responses to all of these questions. This will help to ensure that the project is well-planned and has a high chance of success.
Here are some tips for writing effective responses to the Planning Stage questions:
- Be specific and measurable when defining your goals. For example, instead of saying “improve customer satisfaction,” say “increase customer satisfaction by 10% in the next quarter.”
- Identify all of the stakeholders involved in the project. This includes both internal stakeholders (such as employees and managers) and external stakeholders (such as customers and partners).
- Create a detailed project plan that includes key milestones and deadlines. This will help to keep the project on track and ensure that it is completed on time.
- Identify and assess the potential risks and challenges to the project. This will help you to develop mitigation plans to reduce the impact of these risks and challenges.
- List all of the resources that are needed to complete the project. This includes both human resources (such as staff and contractors) and financial resources.
By following these tips, you can write effective responses to the Planning Stage questions and create a PACE Strategy Document that will help you to successfully complete your project.
The task “Establish structure for project workflow (PACE)” is paired with the “Plan” stage of PACE.
Yes
AI assistant
Yes, the task “Establish structure for project workflow (PACE)” is paired with the “Plan” stage of PACE.
The Plan stage of PACE is the first stage of the PACE workflow, and it is where you define the scope of your project, identify your stakeholders, and create a project plan. Establishing a structure for your project workflow is an important part of the planning process, as it will help you to stay organized and on track throughout the project.
There are a number of different ways to establish a structure for your project workflow. One common approach is to use a work breakdown structure (WBS). A WBS is a hierarchical decomposition of the work that needs to be done to complete the project. It can be used to identify all of the tasks that need to be completed, as well as the dependencies between the tasks.
Another common approach to establishing a project workflow is to use a project management tool. Project management tools can help you to create and track tasks, deadlines, and dependencies. They can also help you to communicate with your team members and stakeholders.
Once you have established a structure for your project workflow, you can begin to develop a more detailed project plan. The project plan should include the following information:
- The tasks that need to be completed
- The dependencies between the tasks
- The estimated duration of each task
- The resources that are needed to complete each task
- The milestones and deadlines for the project
By taking the time to establish a structure for your project workflow and develop a detailed project plan, you can increase your chances of success.
The task “Write a project proposal” is paired with the “Plan” stage of PACE.
Yes
AI assistant
Yes, the task “Write a project proposal” is paired with the “Plan” stage of PACE.
The Plan stage of PACE is the first stage of the PACE workflow, and it is where you define the scope of your project, identify your stakeholders, and create a project plan. Writing a project proposal is an important part of the planning process, as it will help you to communicate your plans to your stakeholders and get their buy-in.
A project proposal should typically include the following information:
- An introduction that describes the project and its goals
- A problem statement that describes the problem that the project is trying to solve
- A solution statement that describes how the project will solve the problem
- A project plan that includes the tasks that need to be completed, the dependencies between the tasks, and the estimated duration of each task
- A budget that estimates the cost of the project
- A conclusion that summarizes the project and its benefits
By taking the time to write a comprehensive project proposal, you can increase your chances of success and ensure that your project is well-aligned with the needs of your stakeholders.
Here are some tips for writing a strong project proposal:
- Be specific and clear in your writing. Avoid using jargon or technical language that your stakeholders may not understand.
- Be realistic in your goals and budget. Don’t overpromise and underdeliver.
- Be flexible and be prepared to adjust your plans as needed.
- Get feedback from your stakeholders on your proposal before you finalize it.
By following these tips, you can write a project proposal that will help you to get your project off on the right foot.
The task “Compile summary information about the data” is paired with the “Analyze” stage of PACE.
Yes
AI assistant
Yes. The task “Compile summary information about the data” is paired with the “Analyze” stage of PACE.
The Analyze stage of PACE is the second stage of the PACE workflow, and it is where you clean, explore, and analyze the data to gain insights. Compiling summary information about the data is an important part of the analysis process, as it can help you to identify key trends and patterns in the data.
There are a number of different ways to compile summary information about the data. Some common approaches include:
- Descriptive statistics: Descriptive statistics, such as the mean, median, and mode, can be used to summarize the central tendency and distribution of the data.
- Visualization: Data visualization tools, such as charts and graphs, can be used to visualize the data and identify trends and patterns.
- Machine learning: Machine learning algorithms can be used to identify complex patterns in the data that may not be visible to the naked eye.
Once you have compiled summary information about the data, you can begin to interpret the data and draw conclusions. Be sure to consider the context of the data and the specific goals of your analysis when interpreting the data.
Here are some tips for compiling summary information about the data:
- Clean the data before analyzing it. This involves removing any errors or inconsistencies in the data.
- Choose the right data visualization tools for your analysis. There are a variety of different data visualization tools available, so choose the tools that will best help you to communicate your findings to your audience.
- Use machine learning algorithms with caution. Machine learning algorithms can be very powerful, but they can also be biased. Be sure to understand the limitations of the algorithms that you are using.
- Interpret the data carefully. Consider the context of the data and the specific goals of your analysis when interpreting the data.
By following these tips, you can compile summary information about the data that will help you to gain insights and draw conclusions.
The task “Begin exploring the data” is paired with the “Analyze” stage of PACE.
Yes
AI assistant
Yes, the task “Begin exploring the data” is paired with the “Analyze” stage of PACE.
The Analyze stage of PACE is the second stage of the PACE workflow, and it is where you clean, explore, and analyze the data to gain insights. Exploring the data is an important part of the analysis process, as it can help you to identify trends, patterns, and anomalies in the data.
There are a number of different ways to explore the data. Some common approaches include:
- Visualizing the data: Data visualization tools, such as charts and graphs, can be used to visualize the data and identify trends and patterns.
- Querying the data: SQL queries can be used to query the data and extract specific information.
- Using statistical analysis tools: Statistical analysis tools can be used to perform statistical tests on the data and identify statistically significant relationships between variables.
By exploring the data, you can gain a better understanding of the data and identify potential areas for further investigation.
Here are some tips for exploring the data:
- Start with a clear goal in mind. What do you hope to learn from the data?
- Use a variety of data visualization tools. Different data visualization tools can reveal different patterns in the data.
- Be open-minded and curious. Don’t be afraid to explore different hypotheses and explanations for the data.
- Document your findings. Keep track of your findings and observations so that you can easily refer back to them later.
By following these tips, you can effectively explore the data and gain insights that can help you to make better decisions.
The task “Data exploration and cleaning” is paired with the “Plan” and “Analyze” stages of PACE.
Yes
AI assistant
Yes, the task “Data exploration and cleaning” is paired with the Plan and Analyze stages of PACE.
In the Plan stage, you will identify the data that you need for your project and develop a plan for cleaning and exploring the data. This may involve identifying and correcting errors in the data, removing irrelevant data, and transforming the data into a format that is suitable for analysis.
In the Analyze stage, you will use the data that you have cleaned and explored to answer your research questions or solve your business problems. This may involve using statistical methods, machine learning algorithms, or data visualization techniques.
Data exploration and cleaning is an iterative process. You may need to go back and forth between the Plan and Analyze stages as you learn more about the data and identify new questions or problems that you want to address.
Here are some tips for data exploration and cleaning:
- Start by understanding the data. What type of data is it? What are the variables? What are the values for each variable?
- Identify and correct errors in the data. This may involve removing duplicate records, correcting typos, and filling in missing values.
- Remove irrelevant data. This may involve removing data that is not relevant to your research questions or business problems.
- Transform the data into a format that is suitable for analysis. This may involve converting data types, creating new variables, or combining multiple variables into a single variable.
Data exploration and cleaning is an important step in any data science project. By carefully cleaning and exploring your data, you can ensure that your results are accurate and reliable.
The task “Visualization building” is paired with the “Analyze” and “Construct” stages of PACE.
Yes
AI assistant
Yes, the task “Visualization building” is paired with the Analyze and Construct stages of PACE.
In the Analyze stage, you will use data visualization techniques to explore and understand the data. This may involve creating charts, graphs, and other visual representations of the data.
In the Construct stage, you will use data visualization techniques to communicate your findings to others. This may involve creating dashboards, reports, and presentations.
Data visualization is an important part of the data science process. It can help you to identify patterns and trends in the data, and to communicate your findings to others in a clear and concise way.
Here are some tips for building visualizations:
- Choose the right visualization type for your data. There are many different types of visualizations available, each with its own strengths and weaknesses. Choose a visualization type that will help you to communicate your findings most effectively.
- Use clear and concise labels. Make sure that the labels on your visualizations are clear and concise. This will help viewers to understand what the visualization is showing.
- Use colors and fonts effectively. Use colors and fonts in a way that is visually appealing and easy to read.
- Tell a story with your visualizations. Use your visualizations to tell a story about your data. What patterns and trends do you see? What conclusions can you draw?
By following these tips, you can build visualizations that are informative, engaging, and easy to understand.
The task “Compute descriptive statistics” is paired with the “Analyze” stage of PACE.
Yes
AI assistant
Yes, the task “Compute descriptive statistics” is paired with the “Analyze” stage of PACE.
Descriptive statistics are used to summarize the central tendency, variation, and distribution of a set of data. They can be used to provide a high-level overview of the data and to identify any potential outliers or anomalies.
There are a variety of different descriptive statistics that can be computed, including:
- Central tendency: Mean, median, and mode
- Variation: Range, standard deviation, and variance
- Distribution: Skewness and kurtosis
The specific descriptive statistics that you compute will depend on the type of data that you have and the research questions that you are trying to answer.
Here are some tips for computing descriptive statistics:
- Clean the data before analyzing it. This involves removing any errors or inconsistencies in the data.
- Choose the right descriptive statistics for your data. There are a variety of different descriptive statistics available, so choose the statistics that will best help you to summarize and understand your data.
- Interpret the descriptive statistics carefully. Consider the context of the data and the specific research questions that you are trying to answer when interpreting the descriptive statistics.
By following these tips, you can effectively compute descriptive statistics that will help you to gain insights into your data.
Here are some examples of how descriptive statistics can be used:
- A marketing manager might use descriptive statistics to summarize the demographic characteristics of their customer base.
- A financial analyst might use descriptive statistics to summarize the performance of a company’s stock price over time.
- A medical researcher might use descriptive statistics to summarize the results of a clinical trial.
Descriptive statistics are a valuable tool for data scientists and researchers of all kinds. By computing descriptive statistics, you can gain insights into your data and make informed decisions.
The task “Conduct hypothesis testing” is paired with the “Analyze” and “Construct” stages of PACE.
Yes
AI assistant
Yes, the task “Conduct hypothesis testing” is paired with the Analyze and Construct stages of PACE.
In the Analyze stage, you will use data analysis techniques to test your hypotheses and answer your research questions. This may involve using statistical methods, machine learning algorithms, or data visualization techniques.
In the Construct stage, you will communicate your findings to others using data visualization techniques, dashboards, reports, and presentations.
Hypothesis testing is a statistical method that can be used to determine whether the results of a study are likely due to chance or whether there is a real effect. It is an important tool for data scientists and researchers of all kinds.
Here are some steps involved in hypothesis testing:
- State your hypothesis. This is a statement about the population that you are interested in testing.
- Collect data. This is the data that you will use to test your hypothesis.
- Choose a statistical test. There are a variety of different statistical tests available, each with its own strengths and weaknesses. Choose a statistical test that is appropriate for your data and your hypothesis.
- Perform the statistical test. This involves using the statistical test to calculate the p-value.
- Interpret the results. The p-value is the probability of obtaining the results of your study or more extreme results if the null hypothesis is true. If the p-value is less than a certain significance level (typically 0.05), then you can reject the null hypothesis and conclude that there is a statistically significant effect.
Here are some tips for conducting hypothesis testing:
- State your hypothesis clearly and concisely. Make sure that your hypothesis is specific and testable.
- Collect a representative sample of data. The data that you collect should be representative of the population that you are interested in testing.
- Choose the right statistical test. There are a variety of different statistical tests available, each with its own strengths and weaknesses. Choose a statistical test that is appropriate for your data and your hypothesis.
- Interpret the results carefully. Consider the context of the data and the specific research questions that you are trying to answer when interpreting the results of your hypothesis test.
By following these tips, you can effectively conduct hypothesis tests and draw conclusions from your data.
The task “Build a regression model” is paired with the “Analyze” and “Construct” stages of PACE.
Yes
AI assistant
Yes, the task “Build a regression model” is paired with the Analyze and Construct stages of PACE.
In the Analyze stage, you will use data analysis techniques to explore and understand the data. This may involve using statistical methods, machine learning algorithms, or data visualization techniques.
In the Construct stage, you will use the insights that you have gained from the Analyze stage to build a regression model. A regression model is a statistical model that can be used to predict a continuous target variable based on one or more independent variables.
Here are some steps involved in building a regression model:
- Prepare the data. This involves cleaning and transforming the data into a format that is suitable for regression analysis.
- Choose a regression model. There are a variety of different regression models available, each with its own strengths and weaknesses. Choose a regression model that is appropriate for your data and your research questions.
- Fit the regression model. This involves using the regression model to estimate the coefficients of the model.
- Evaluate the regression model. This involves assessing the performance of the regression model on both training data and test data.
- Deploy the regression model. Once you have evaluated the regression model and are satisfied with its performance, you can deploy the model to production.
Here are some tips for building a regression model:
- Use high-quality data. The quality of the data that you use to train your regression model will have a significant impact on its performance.
- Choose the right regression model. There are a variety of different regression models available, each with its own strengths and weaknesses. Choose a regression model that is appropriate for your data and your research questions.
- Evaluate the regression model carefully. It is important to evaluate the performance of your regression model on both training data and test data. This will help you to identify any overfitting and to ensure that the model is generalizable to new data.
- Deploy the regression model responsibly. Once you have deployed your regression model to production, it is important to monitor its performance and to make updates as needed.
By following these tips, you can effectively build and deploy regression models that can be used to make accurate predictions.
Regression models are used in a wide variety of applications, including:
- Financial forecasting: Predicting stock prices, interest rates, and other financial metrics.
- Marketing: Predicting customer churn, product demand, and other marketing metrics.
- Healthcare: Predicting patient outcomes, disease risk, and other healthcare metrics.
- Manufacturing: Predicting product quality, machine failure, and other manufacturing metrics.
Regression models are a powerful tool that can be used to solve a wide variety of problems. By following the tips above, you can build and deploy regression models that will help you to make better decisions.
The task “Evaluate the model” is paired with the “Execute” stage of PACE.
Yes
AI assistant
No, the task “Evaluate the model” is not paired with the “Execute” stage of PACE.
The “Execute” stage of PACE is the final stage of the PACE workflow, and it is where you deploy the model to production and monitor its performance. Evaluating the model is an important part of the development process, but it should be completed before the model is deployed to production.
The “Evaluate” stage is typically paired with the “Construct” stage of PACE. This is because the “Construct” stage is where you build and train the model. Once the model is trained, you need to evaluate its performance on a held-out test set. This will help you to identify any overfitting and to ensure that the model is generalizable to new data.
Here are some tips for evaluating a model:
- Use a held-out test set. The test set should be a representative sample of the data that the model will be used on in production.
- Calculate appropriate metrics. The metrics that you choose will depend on the type of model that you have built and the specific problem that you are trying to solve.
- Compare the model’s performance to a baseline. This will help you to assess the improvement that the model provides over a simple baseline approach.
By following these tips, you can effectively evaluate your models and ensure that they are performing well before deploying them to production.
Here are some examples of how models are evaluated in different domains:
- Machine learning: In machine learning, models are often evaluated using metrics such as accuracy, precision, recall, and F1 score.
- Statistical modeling: In statistical modeling, models are often evaluated using metrics such as R-squared, mean squared error, and mean absolute error.
- Simulation modeling: In simulation modeling, models are often evaluated using metrics such as throughput, latency, and accuracy.
The specific metrics that you use to evaluate your model will depend on the type of model that you have built and the specific problem that you are trying to solve.
If applicable to the project scenario, the task “Build a machine learning model” is paired with the “Construct” stage of PACE.
Yes
AI assistant
Yes, the task “Build a machine learning model” is paired with the Construct stage of PACE.
The Construct stage of PACE is where you build and train the model. This includes choosing the right machine learning algorithm, preparing the data, and training the model.
Here are some steps involved in building a machine learning model:
- Choose the right machine learning algorithm. There are a variety of different machine learning algorithms available, each with its own strengths and weaknesses. Choose an algorithm that is appropriate for your data and your problem.
- Prepare the data. This involves cleaning and transforming the data into a format that is suitable for machine learning.
- Train the model. This involves providing the algorithm with the training data and allowing it to learn the relationships between the features and the target variable.
- Evaluate the model. This involves assessing the performance of the model on a held-out test set.
Once the model has been trained and evaluated, it can be deployed to production.
Here are some tips for building a machine learning model:
- Use high-quality data. The quality of the data that you use to train your machine learning model will have a significant impact on its performance.
- Choose the right machine learning algorithm. There are a variety of different machine learning algorithms available, each with its own strengths and weaknesses. Choose an algorithm that is appropriate for your data and your problem.
- Prepare the data carefully. It is important to prepare the data carefully before training a machine learning model. This includes cleaning the data, transforming the data into a suitable format, and handling missing values.
- Evaluate the model carefully. It is important to evaluate the performance of the model on a held-out test set before deploying it to production. This will help you to identify any overfitting and to ensure that the model is generalizable to new data.
By following these tips, you can effectively build and deploy machine learning models that can be used to solve a wide variety of problems.
The task “Communicate final insights with stakeholders” is paired with the “Execute” stage of PACE.
Yes
AI assistant
Yes, the task “Communicate final insights with stakeholders” is paired with the Execute stage of PACE.
The Execute stage of PACE is the final stage of the PACE workflow, and it is where you deploy the model to production and monitor its performance. Communicating your final insights with stakeholders is an important part of the Execute stage, as it helps to ensure that everyone is aligned on the findings of the project and how they will be used.
There are a number of different ways to communicate your final insights with stakeholders. Some common methods include:
- Presentations: Presentations are a great way to communicate your findings to a large group of stakeholders. Be sure to use clear and concise language, and to support your findings with data and visuals.
- Reports: Reports are a good way to communicate your findings in a more detailed way. Be sure to include all of the relevant information, such as the methodology used, the results of the analysis, and the conclusions of the project.
- Meetings: Meetings can be a good way to communicate your findings to a smaller group of stakeholders. This can be a good opportunity to answer questions and to discuss the implications of the findings.
No matter which method you choose, it is important to be clear and concise in your communication. Be sure to explain the findings in a way that is easy for stakeholders to understand, and to highlight the implications of the findings for the business.
Here are some tips for communicating your final insights with stakeholders:
- Tailor your communication to your audience. Consider the needs and interests of your stakeholders when communicating your findings.
- Use clear and concise language. Avoid using jargon or technical language that your stakeholders may not understand.
- Be specific and measurable. When communicating your findings, be as specific and measurable as possible. This will help stakeholders to understand the impact of the findings.
- Use data and visuals to support your findings. Data and visuals can be a great way to communicate your findings in a clear and concise way.
- Be open to feedback and questions. After communicating your findings, be open to feedback and questions from stakeholders. This is a good opportunity to clarify any points of confusion and to address any concerns.
By following these tips, you can effectively communicate your final insights with stakeholders and ensure that everyone is aligned on the findings of the project and how they will be used.
Reading: Course 1 glossary
Reading
Course review: Foundations of Data Science
Video: Course wrap-up
Congratulations on finishing the first course! You’ve learned a lot and are now ready to move forward. Don’t forget that the resources from this course are still available if you need a refresher or extra practice.
The next course builds on what you’ve learned so far and will give you more insight into the Python programming language and how it can be used to enable data analytics. You’ll continue building your data professional skill set, and by the end of the course, you’ll be prepared to take the next step towards your portfolio project.
Thank you for joining the course and choosing to pursue this exciting learning opportunity. Education is a lifelong journey and all the time and effort you put into experiences like this one will better equip you for anything you choose to pursue.
Take a moment to celebrate everything that you’ve already accomplished, and then when you’re ready, head on over to the next course.
Congratulations on finishing
this first course. You’ve already learned so much and now you’re
ready to take your new knowledge
and skills and keep moving forward,
but remember, if one day you
feel like you need a refresher or just
some extra practice, these videos, readings,
and activities will still be here
whenever you need them. Now I’m excited for you to begin working with the instructor
for the next course. They’re ready to help you take your next step towards finishing this program and continuing your journey as a
data professional. This course builds directly on all the interesting topics
you’ve learned so far. It will give you
more insight into the Python programming
language and how it can be used to
enable data analytics. As you progress, you’ll continue building your data
professional skill set, and by the end of the course, you’ll be prepared to take the next step towards
your portfolio project. Before you get started though, I’d like to thank you for
joining me in this course and choosing to pursue this exciting
learning opportunity. I strongly believe
that education is a lifelong journey and I’ve no doubt that all the time and effort you put into
experiences like this one will better equip you for anything
you choose to pursue. You’ve come a long way, so take a moment to celebrate everything that you’ve
already accomplished, and then when you’re ready, head on over to the next course.
Reading: Get started on the next course
Reading
Congratulations on completing the first course in the Google Advanced Data Analytics Certificate! In this part of the program, you learned the basic concepts of data science and discovered how data scientists operate in the workplace. You also learned how the PACE workflow can help you better understand how to navigate the technical and workplace expectations of a data professional.
The entire program has seven courses:
- Foundations of Data Science – Learn how data professionals operate in the workplace and how different roles within the data-career space contribute to an organization’s vision of its future. You’ll explore data science roles, communication skills, and data ethics. (This is the course you just completed. Well done!)
- Get Started with Python – Learn basic Python programming and syntax, loops, strings, lists, dictionaries, and object-oriented programming.
- Go Beyond the Numbers: Translate Data into Insights – Learn the fundamentals of data cleaning and visualizations and how to uncover meaningful stories in the data.
- The Power of Statistics – Learn descriptive and inferential statistics, basic probability and probability distributions, sampling, confidence intervals, and hypothesis testing.
- Regression Analysis: Simplify complex data relationships – Learn to model variable relationships, focusing on linear and logistic regression.
- The Nuts and Bolts of Machine Learning – Learn unsupervised machine learning techniques and how to apply them to organizational data.
- Google Advanced Data Analytics Capstone – Complete a hands-on project designed to allow you to use the skills and competencies acquired in the certificate.
Now that you have completed this course, you are ready to move on to the next course: Get Started with Python.
Keep up the great work!