Course One
Foundations of Data Science
Instructions
Use this PACE strategy document to record decisions and reflections as you work through this
end-of-course project. You can use this document as a guide to consider your responses and
reflections at different stages of the data analytical process. Additionally, the PACE strategy
documents can be used as a resource when working on future projects.
Course Project Recap
Regardless of which track you have chosen to complete, your goals for this project are:
● Complete the 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.
Relevant Interview Questions
Completing this end-of-course project will empower you to respond to the following interview
topics:
● As a new member of a data analytics team, what steps could you take to get 'up to speed'
with a current project? What steps would you take? Who would you like to meet with?
To get up to speed on a current project:
1. Understand the Project Scope: Review documentation, goals, and deliverables.
2. Analyze Existing Data: Familiarize yourself with the data sources, tools, and current
analyses.
3. Meet with Key Stakeholders: Schedule meetings with the project lead, data engineers, and
business analysts to understand their perspectives.
4. Study Past Reports: Learn from previous reports and analyses to grasp the project's
evolution.
5. Hands-On Practice: Start working on smaller tasks to apply your learning and identify
gaps.
6. Meeting with team members, especially those handling data and business insights, is
crucial.
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How would you plan an analytics project?
To plan an analytics project:
1. Define Objectives: Clearly outline the project goals and the business questions to answer.
2. Identify Stakeholders: Determine who will use the results and who needs to be involved.
3. Data Collection: Identify data sources, assess data quality, and plan for data acquisition.
4. Choose Tools and Techniques: Select appropriate analytical tools and methods based on
the data and objectives.
5. Develop a Timeline: Break the project into phases with milestones and deadlines.
6. Assign Roles and Responsibilities: Clarify who will handle specific tasks.
7. Plan for Analysis: Outline steps for data cleaning, analysis, and validation.
8. Document and Communicate: Ensure regular updates and clear documentation for
transparency and future reference.
● What steps would you take to translate a business question to an analytical solution?
To translate a business question into an analytical solution:
1. Clarify the Question: Ensure a clear understanding of the business question, its context,
and objectives.
2. Define Metrics and KPIs: Identify the key metrics and performance indicators relevant to
the question.
3. Identify Data Requirements: Determine what data is needed to answer the question and
where it can be sourced.
4. Choose the Analytical Approach: Select appropriate statistical, machine learning, or data
visualization techniques.
5. Conduct the Analysis: Implement the chosen methods, ensuring data is clean and
validated.
6. Interpret Results: Relate the analytical findings to the original business question, providing
actionable insights
● Why is actively managing data an important part of a data analytics team's
responsibilities?
Actively managing data is crucial for a data analytics team because:
1. Data Quality: Ensures accuracy, consistency, and reliability of data, which is vital for
producing valid results.
2. Compliance and Security: Helps in adhering to data privacy regulations and protecting
sensitive information.
3. Efficiency: Streamlines data processes, reducing time and effort in data preparation and
analysis.
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4. Scalability: Facilitates handling growing data volumes and integrating new data sources.
5. Insightful Decision-Making: Provides a solid foundation for generating actionable insights,
supporting informed business decisions.
6. Cost Management: Prevents unnecessary costs related to data storage and management
inefficiencies.
● What are some considerations you might need to be mindful of when reporting results?
When reporting results, consider the following:
1. Audience: Tailor the complexity and detail to the audience's knowledge level.
2. Clarity: Use clear, straightforward language and visuals to convey findings.
3. Context: Provide context to help interpret the data, including background information and
assumptions.
4. Relevance: Focus on the most pertinent insights and actionable recommendations.
5. Accuracy: Ensure data accuracy and transparency about methodologies and potential
limitations.
6. Bias and Ethics: Be mindful of biases in data or analysis and present results ethically.
7. Visualization: Use appropriate charts, graphs, and visuals to enhance understanding and
engagement.
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Reference Guide
This project has three tasks; the following visual identifies how the stages of PACE are
incorporated across those tasks.
Data Project Questions & Considerations
PACE: Plan Stage
● Who is your audience for this project?
The Waze data team and cross-functional team members (Product Managers, Marketing
Team, Customer Support, Data and Analytics Teams)
● What are you trying to solve or accomplish? And, what do you anticipate the impact of this
work will be on the larger needs of the client?
The goal of the project is to build a machine learning model that predicts user churn for the
Waze app. By identifying users likely to stop using the app, we aim to: Improve User
Retention, Enhance User Experience, Optimize Marketing Efforts, Inform Product
Development.
The anticipated impact includes increased user retention rates, better customer satisfaction,
and overall improved app performance, aligning with Waze's business goals of maintaining a
loyal user base and enhancing service quality.
● What questions need to be asked or answered?
To effectively address the user churn prediction project, key questions to ask include:
What specific data is available on user behavior, app usage, and demographics?
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What is the condition of the provided dataset?
Are there any data quality issues or gaps?
How is data privacy and compliance being ensured?
What variables will be the most useful?
Are there trends within the data that can provide insight?
What steps can I take to reduce the impact of bias?
How is churn defined for this project?
What is the historical churn rate?
What machine learning models and techniques are suitable for this data?
How will the model's accuracy and effectiveness be measured?
What specific business outcomes are we aiming to achieve with the churn
prediction?
How will the results be integrated into Waze’s existing processes or systems?
What kind of interventions can be planned based on the predictions?
How can these insights be communicated to different stakeholders
● What resources are required to complete this project?
To complete the Waze churn prediction project, the following resources are required:
dataset, Tools and Technology, input from stakeholders, Collaboration Platforms, Time and
Budget.
● What are the deliverables that will need to be created over the course of this project?
The key deliverables for the Waze churn prediction project include: Project Plan, a dataset
scrubbed for exploratory data analysis, Visualizations and Dashboards, Feature
Engineering Report, Model Development and Evaluation, Churn Prediction Model, Insights
and Recommendations, Implementation Plan.
THE PACE WORKFLOW
[Alt-text: The PACE Workflow with the four
stages in a circle: plan, analyze, construct, and
execute.]
You have been asked to demonstrate for the
company's data team how you would use the PACE
workflow to organize and classify tasks for the
upcoming project. Select a PACE stage from the
dropdown buttons. A few tasks involve more than one
stage of the PACE workflow. Additionally, not every
workplace scenario will require every task. Refer back
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to the Course 1 end-of-course portfolio project overview reading if you need more information
about the tasks within the project.
Project tasks
Following are a group of tasks your company’s data team has determined need to be completed
within this project. The data analysis manager has asked you to organize these tasks in
preparation for the project proposal document. First, identify which stage of the PACE workflow
each task would best fit under using the drop down menu. Next, give an explanation of why you
selected the stage for each task. Review the following readings to help guide your selections and
explanation: The PACE stages and Communicate objectives with a project proposal. You will later
reorder these tasks within a project proposal.
1. Evaluating the model: Execute
Why did you select this stage for this task?
Assessing the model's performance ensures its reliability and validity, a vital execution
step.
2. Conduct hypothesis testing: Analyze and Construct
Why did you select these stages for this task?
During the analyzing stage, it is determined that a statistical test will be used. During the
construction phase, the test is carried out.
3. Begin exploring the data: Analyze
Why did you select this stage for this task?
Initial data exploration helps understand the dataset's structure and identify potential
issues, a key part of the analysis phase
4. Data exploration and cleaning: Plan and Analyze
Why did you select these stages for this task?
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Planning takes place when you first make choices about the methods needed. The
cleaning process then takes place in the analyzing stage.
5. Establish structure for project workflow (PACE): Plan
Why did you select this stage for this task?
This task involves setting up the project's framework, timelines, and responsibilities,
essential for initial planning.
6. Communicate final insights with stakeholders: Execute
Why did you select this stage for this task?
Communication is necessary at various points throughout a project. Final insights are
shared with stakeholders in the execute phase of the data project workflow.
7. Compute descriptive statistics: Analyze
Why did you select this stage for this task?
Provides a summary of the data, helping to understand distributions, central tendencies,
and variances
8. Visualization building: Analyze and Construct
Why did you select these stages for this task?
Visualization begins with data assessment and is created during the construction stage.
9. Write a project proposal: Plan
Why did you select this stage for this task?
This task outlines the project's objectives, scope, and methodology, serving as a
foundational planning document
10. Build a regression model: Analyze and Construct
Why did you select this stage for this task?
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During the analyzing stage, the model is examined in detail to be sure it will meet the
needs of the task. The building of the regression model will take place in the construction
phase.
11. Compile summary information about the data: Analyze
Why did you select this stage for this task?
Inspecting a dataset to compile information would take place in the analysis phase.
12. Build machine learning model: Construct
Why did you select this stage for this task?
The building of a data model would take place in the construct stage.
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