You will put everything you have learned about Python so far into practice with an end-of-course project. You will select a business problem from a list of options and use the given data to solve the problem. This project is an opportunity to demonstrate your skills and build a professional portfolio you can use to showcase your work to potential employers.
Learning Objectives
- Describe key findings for a relevant audience member
- Implement a code-based solution with Python
- Plan an approach to solving a new data science problem
- Conduct analysis on a given dataset to determine potential options for tackling a problem
- Formulate a problem statement to understand a dataset’s inputs and outputs
Apply your skills to a workplace scenario
Video: Welcome to module 5
Tiffany, from Google’s Responsible AI Program, guides you on building a Python portfolio project for your job search.
- Purpose: Showcase your coding skills, develop interview skills, and prepare for data-driven tasks.
- Project focus: Load, clean, and structure unstructured data into a tidy dataset using Python.
- Benefits:
- Demonstrate data manipulation skills.
- Explain your problem-solving and thought process to employers.
- Practice interview scenarios involving data cleaning and structuring.
- Prepare for potential requests to build a database structure.
- Instructions:
- Choose a business case from provided options.
- Follow instructions to complete your project in your PACE strategy document.
- Aim for a tidy dataset: easy to manipulate, model, and visualize.
- Document your steps for future reference and explanation.
- Outcome:
- A structured dataset for the next portfolio project.
- Enhanced data manipulation skills and portfolio piece for your job search.
This summary highlights the key elements of the project, its benefits, and your goals as you move forward.
Hi. It’s great to
be with you again. You might recognize me
from the last course. I’m Tiffany and I lead the Responsible AI Program management team here at Google. I’m back to talk
more with you about your portfolio
projects and how you can use them in your job search. Now that we’ve had some
time to explore Python, I’m excited to help
you work on a project that you could add to your
professional portfolio. As we complete this
segment of the program, you’ll have the
opportunity to begin showcasing your coding skills. This portfolio project is a really valuable opportunity to develop your
interview skills. When potential employers
assess you as a candidate, they might ask for
specific examples of how you tackled coding
challenges in the past. You could use your
portfolio as a way to discuss a real problem
you have solved. Additionally, some employers
might ask you to load, clean, and structure data during an interview to prove
your proficiency. Getting some practice creating a database structure to address data-driven projects
means you’ll be prepared for that
type of situation. You’ve already learned about experiential
learning or the idea of understanding through doing. This portfolio project is a great opportunity to really
discover how organizations manage data with Python and practice the skills you’ve
been learning in this course. To complete the
portfolio project, you’ll be presented
with details about some business cases and some
unstructured data files. Choose one business scenario
and use the instructions to complete a new entry in
your pace strategy document. Based on the scenario, your task is to load, clean, and structure the data so that your end product
is a tidy dataset. Data tidying is structuring datasets to facilitate analysis. Tidy data sets are
easy to manipulate, model and visualize, and
have a specific structure. Each variable is a column, each observation is a row, and each type of observational
unit is a table. By the time you complete
this project, you’ll have a structured dataset
that you’ll use in the next course
portfolio project. In your PACE strategy
document, you’ll also have documentation of the steps
you took along the way. Which you can use to explain
your work and thought process to future
hiring managers. At this point, you’re almost
finished with this course, which means you’ve
learned everything you need to keep advancing
as a data professional. This part of the project will focus on demonstrating
mastery of data manipulation
and understanding how data professionals use Python to explore and extract information through
custom functions. Ready? Then let’s get started.
Video: Introduction to your Course 2 end-of-course portfolio project
Building your data portfolio with Python in this exciting next step!
- Leverage previous learning: Apply your knowledge of basic Python, data structures, and communication skills to this project.
- Tidy data is key: Clean and structure unstructured data into a clear, well-organized format targeting a specific business scenario.
- Data career beyond this project: This is just the beginning! Dive deeper into data visualization, statistics, models, and machine learning in coming sections.
- Strengthening skills for future success: Gaining proficiency in Python makes you a strong candidate for data professional roles.
- Demonstrate your potential: Showcase your ability to transform data into valuable insights for business decisions, impressing potential employers.
- Iterative learning: Continuously improve your data skills as you explore new ideas and technologies.
This summary captures the essence of the text: focusing on the current portfolio project, highlighting future learning opportunities, and emphasizing the value of applying data skills in real-world scenarios.
In this course, you’ve been
learning about the advantages and simplicity of python as well
as basic python, syntax loops, strings, data structures and
object oriented programming. Now it’s time for an exciting next
step putting all this to work for your portfolio project. In the previous course you learned about
the flexibility of a data professional career and the ways communication has
a direct impact on data driven work. You also practice thinking like a data
professional as you assess a business scenario and
recorded project considerations and your Pace strategy document. These skills will also be
applicable to this new project. In this part of the course you’ll
be presented with some unstructured data files. Your goal is to load clean and
structure their data in a tidy data set that is targeted towards
a specific business scenario. Coming up, you’ll begin to explore what it
means to be a data professional in other sections of this program, you’ll work
on developing additional skills to help you succeed in the data career space,
there’s so much more to learn about data visualization,
statistics, models and machine learning. The skills you learn and strengthen
through this program will help you be a better class later when
completing future data projects, learning how to use and navigate. Python will also make you
an ideal candidate for data professional roles
as a data professional. A large part of your job involves
engaging with data to help your team and others in your organization, develop critical insights that
ultimately drive business decisions. Often there’s so much data that tools like python are
needed to successful complete daily work. This part of the portfolio project is
a great opportunity to demonstrate to potential employers that you can do
exactly that take unstructured data and clean, organize and
manage it to achieve an actionable goal. And remember developing your skills as
a data professional is an iterative process so you can continue to improve as
you have new ideas or learn new things.
Reading: Explore your Course 2 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.
eminder: 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 2 end-of-course project.
Course 2 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 have received notice that the recently submitted New York City TLC project proposal has been approved. The Automatidata team now has access to the New York City TLC data to analyze, identify key variables, and prepare for exploratory data analysis.
Course 2 tasks:
- Load data, explore, and extract the New York City TLC data with Python
- Use custom functions to organize the information within the New York City TLC dataset
- Build a dataframe for the New York City TLC project
- Create an executive summary for Automatidata
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, you’ll help by preparing the data needed for the claims classification project. You’ll build a dataframe, organize the claims data for the process of exploratory data analysis, and update the team on your progress and insights.
Course 2 tasks:
- Build a dataframe for the TikTok dataset
- Read in data from TikTok csv file
- Display rows within dataframe
- Examine data type of each column
- Gather descriptive statistics
- Visualize the TikTok data in Python
- Report to TikTok’s data team through an executive summary
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:
Your team is in the early stages of their user churn project. Your project proposal has been approved and your team has been given access to Waze’s user data. To get clear insights, the data must first be inspected, organized, and prepared for analysis.
Course 2 tasks:
- Import data
- Create a dataframe
- Inspect data
- Identify outliers
- Create a data visualization
- Share an executive summary with 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 2, Get Started with Python, you were introduced to some basics of the Python programming language. You explored syntax, loops, strings, lists, dictionaries, object-oriented programming, and explored how data professionals use code on the job.
Course 2 skills:
- Code with Python
- Create data visualization
- Use comments to enhance code readability
- Work within a Jupyter Notebook
- Share insights and ideas with stakeholders
Course 2 end-of-course project deliverables:
- Build a dataframe
- Create an executive summary
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 2 end-of-course portfolio project overview: Automatidata
Reading
Learn about the Course 2 Automatidata workplace scenario!
The end-of-course project in Course 2 focuses on your ability to understand the data needed for a project. As a reminder, in Course 1 you developed a project proposal that outlined milestones, which progress with each of the end-of-course projects. A visual representation is provided in the graphic shown here:
Learn more about the project, your role, and expectations in this reading.
Background on the Automatidata scenario
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.
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
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.
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.
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:
- Build a dataframe for the TLC dataset
- Examine data type of each column
- Gather descriptive statistics
Your assignment
You will build a dataframe for the TLC data. After the dataframe is complete, you will organize the data for the process of exploratory data analysis, and update the team on your progress and insights.
Specific project deliverables
With this end-of-course project, you will gain valuable practice and apply your new skills as you complete the following:
- Complete the questions in the Course 2 PACE strategy document
- Answer the questions in the Jupyter notebook project file
- Complete coding prep work on project’s Jupyter notebook
- Summarize the column Dtypes
- Communicate important findings to DeShawn and Luana in the form of an executive summary
Good luck with this project! Automatidata looks forward to seeing how you communicate your creative work and approach problem-solving!
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 2 Automatidata project
Reading: Activity Exemplar: Create your Course 2 Automatidata project
Reading
Completed Exemplars
To review the exemplar for the Course 2 executive summary, click the following link and select Use Template.
Assessment of ExemplarCourse 2 Automatidata project lab
Compare the exemplar to the Python notebook you completed. Your responses might 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the Python notebook. Yours might differ in certain ways, such as your specific code input or responses to questions. What’s important is that you have an overall understanding of the purpose and functionality of a Python notebook for data analysis.
Your Python notebook should:
- Include the correct code for inspecting and organizing your data
- Clearly communicate your responses to questions about code input and results
Course 2 executive summary
Compare the exemplar to your completed executive summary. Your responses might 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the executive summary. Yours might differ in certain ways, such as your specific language, answers to questions or the layout you selected from the template offerings. What’s important is that you have an overall understanding of the purpose and organization of executive summaries for data projects.
Your executive summary should:
- Include key information that you want to share with teammates and/or stakeholders
- Use clear and concise language to effectively communicate your results
Lab: Exemplar: Course 2 Automatidata project lab
TikTok scenario
Reading: Course 2 end-of-course portfolio project overview: TikTok
Reading
Learn about the Course 2 TikTok workplace scenario!
The end-of-course project in Course 2 focuses on your ability to understand the data needed for a project. As a reminder, in Course 1 you developed a project proposal that outlined milestones, which progress with each of the end-of-course projects. A visual representation is provided in the graphic shown here:
Learn more about the project, your role, and expectations in this reading.
Background on the TikTok scenario
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 submit reports that identify videos and comments that contain user claims. These reports identify content that needs to be reviewed by moderators. The process generates a large number of user reports that are challenging to consider in a timely manner.
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:
- Build a dataframe for the TikTok dataset
- Examine data type of each column
- Gather descriptive statistics
Your assignment
You will build a dataframe for the claims classification data. After the dataframe is complete, you will organize the claims data for the process of exploratory data analysis, and update the team on your progress and insights.
Team members at TikTok
Data team roles
- Willow Jaffey- Data Science Lead
- Rosie Mae Bradshaw- Data Science Manager
- Orion Rainier- Data Scientist
The members of the data team at TikTok are well versed in data analysis and data science. Messages to these more technical coworkers should be concise and specific.
Cross-functional team members
- Mary Joanna Rodgers- Project Management Officer
- Margery Adebowale- Finance Lead, Americas
- Maika Abadi- Operations Lead
Your TikTok team includes several managers, who oversee operations. It is important to adjust your general correspondence appropriately to their roles, given that their responsibilities are less technical in nature.
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.
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 2 PACE Strategy Document to plan your project while considering your audience members, teammates, key milestones, and overall project goal.
- Answer the questions in the Jupyter notebook project file
- Complete coding prep work on project’s Jupyter notebook
- Summarize the column Dtypes
- Communicate important findings in the form of an executive summary
TikTok’s data team needs you to problem-solve and communicate your findings. Good luck on your tasks!
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 2 TikTok project
Reading: Activity Exemplar: Create your Course 2 TikTok project
Reading
Completed Exemplars
To review the exemplar for the Course 2 executive summary, click the following link and select Use Template.
Assessment of Exemplars
Course 2 TikTok project lab
Compare the exemplar to the Python notebook you completed. 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the Python notebook. Yours may differ in certain ways, such as your specific code input or responses to questions. What’s important is that you have an overall understanding of the purpose and functionality of a Python notebook for data analysis.
Your Python notebook should:
- Include the correct code for inspecting and organizing your data
- Clearly communicate your responses to questions about code input and results
Course 2 executive summary
Compare the exemplar to your completed executive summary. 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the executive summary. Yours might differ in certain ways, such as your specific language, answers to questions or the layout you selected from the template offerings. What’s important is that you have an overall understanding of the purpose and organization of executive summaries for data projects.
Your executive summary should:
- Include key information that you want to share with teammates and/or stakeholders
- Use clear and concise language to effectively communicate your results
Lab: Exemplar: Course 2 TikTok project lab
Waze scenario
Course 2 end-of-course portfolio project overview: Waze
Reading
Learn about the Course 2 Waze workplace scenario!
The end-of-course project in Course 2 focuses on your ability to understand the data needed for a project. As a reminder, in Course 1 you developed a project proposal that outlined milestones, which progress with each of the end-of-course projects. A visual representation is provided in the graphic shown here:
Learn more about the project, your role, and expectations in this reading.
Background on the Waze scenario
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.
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 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:
- Build a dataframe for the churn dataset
- Examine data type of each column
- Gather descriptive statistics
- Your assignment
Your assignment
You will build a dataframe for the churn data. After the dataframe is complete, you will organize the data for the process of exploratory data analysis, and update the team on your progress and insights.
Team members at Waze
Data team roles
- Harriet Hadzic – Director of Data Analysis
- May Santner – Data Analysis Manager
- Chidi Ga – Senior Data Analyst
- Sylvester Esperanza – Senior Project Manager
Data team members 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.
Cross-functional team members
- Emrick Larson – Finance and Administration Department Head
- Ursula Sayo – Operations Manager
Your Waze team includes several managers overseeing operations. It is important to adapt your communication to their roles since their responsibilities are less technical.
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.
Specific project deliverables
With this end-of-course project, you will gain valuable practice and apply your new skills as you complete the following:
- Complete the questions in the Course 2 PACE strategy document
- Answer the questions in the Jupyter notebook project file
- Complete coding prep work on project’s Jupyter notebook
- Summarize the column Dtypes
- Communicate important findings in the form of an executive summary
Good luck with this project! Your Waze team members are looking forward to seeing how you communicate your creative work and approach problem-solving!
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 2 Waze project
Reading: Activity Exemplars: Create your Course 2 Waze project
Reading
Completed Exemplars
To review the exemplar for the Course 2 executive summary, click the following link and select Use Template.
Assessment of Exemplar
Course 2 Waze project lab
Compare the exemplar to the Python notebook you completed. Your responses might 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the Python notebook. Yours might differ in certain ways, such as your specific language, answers to questions or the layout you selected from the template offerings. What’s important is that you have an overall understanding of the purpose and functionality of a Python notebook for data analysis.
Your Python notebook should:
- Include the correct code for inspecting and organizing your data
- Clearly communicate your responses to questions about code input and results
Course 2 executive summary
Compare the exemplar to your completed executive summary. Your responses might 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 end-of-course projects in the certificate.
Note: The exemplar represents one possible way to complete the executive summary. Yours might differ in certain ways, such as your specific language and or layout selected from the template offerings. What’s important is that you have an overall understanding of the purpose and organization of executive summaries for data projects.
Your executive summary should:
- Include key information that you want to share with teammates and/or stakeholders
- Use clear and concise language to effectively communicate your results
Lab: Exemplar: Course 2 Waze project lab
End-of-course portfolio project wrap-up
Video: End-of-course project wrap-up and tips for ongoing career success
Progress & Portfolio:
- You’ve accomplished a lot, including building a tidy database in Python for a business scenario.
- Highlight transferable skills in your portfolio, like problem-solving, communication, and data management, regardless of specific tools used.
- Showcase your thought process and decision-making in the project to demonstrate your approach.
Communication & Audience:
- Adjust your technical explanations to match the audience’s knowledge level (stakeholders, interviewers).
- Consider interviewers’ business needs and tailor your information to help them fill the open position.
Next Steps:
- Learn data storytelling and exploratory data analysis.
- Create data visualizations for your portfolio.
- Build a strong portfolio showcasing your skills and potential.
Overall Message:
- Focus on transferable skills and effective communication while highlighting your project’s value to various audiences.
- Utilize the upcoming lessons and data analysis practice to strengthen your portfolio and prepare for your career in data.
Hi there. Let’s take a moment to appreciate how far you’ve come at this point in the program. You’ve done a lot
of work already. You completed two entries in
your pace strategy document and began writing your own code. As you continue to work on
your portfolio projects, you’ll want to consider
how you can document your process and
explain what you’ve done to potential employers and hiring managers in
future interviews. First, it’s important to recognize that as a
data professional, you may be asked to learn
and adapt to new tools. There are a lot of great
options out there and different businesses have
their own preference depending on their needs. As you apply for the jobs, keep in mind that you
have learned a lot of transferable skills
that can be applied across organizations
and industries. For example, in the part of your portfolio of the project
you’ve just completed, you used Python to
build a tidy database focused on solving a
data-focused business scenario. Python is a great tool, and knowing how to use it
is a tremendous skill. But even more importantly, you’ve learned to consider how a data professionals work, contributes to business decisions,
and strategic insights. You’ve learned the
importance of communication, the value of tools
available to you, and how to use Python to
manage large data sets. These are skills worth
highlighting in job interviews, no matter what tools
the position requires. This portfolio project is
a great way to showcase these transferable
skills and give interviewers insight on
your approach to problems, your thought processes, and why you made certain decisions. In addition to making
sure you’re highlighting transferable skills when talking about your portfolio project, you’ll also want to make sure you’re considering
your audience. As you have been learning
throughout these courses, you will often work
with different kinds of stakeholders who have different levels of
technical knowledge. When you’re communicating with them about technical processes, you’ll want to keep in
mind who your audience is, what their goals are, what they already know, and what they need to know. This is just as true
when you’re discussing your portfolio project
with interviewers. Often, there will be
people conducting or joining your interview who aren’t necessarily
data professionals. For example, hiring
managers may not have the same detailed
understanding of data processes as you do. In order to keep your
presentation relevant to them, try to remember those key
questions about your audience. Your interviewers have
a business challenge, just like stakeholders
and data projects. They have an open job
position they need to fill. Think about what they
need to know about you to make a decision that
solves their challenge. Coming up, you’re going to learn all about how to tell
stories with data. Then you’ll have an
opportunity to perform some exploratory data analysis and create data visualizations. By the end of the program, you will have a
strong portfolio.
Course review: Get Started with Python
Reading: Course 2 glossary
Reading
Video: Course wrap-up
- Achievement unlocked: Completion of the end-of-course project, showcasing your Python proficiency through a tangible product for employers.
- Python skills mastered: Variable usage, data type conversion, function calls, operators, conditional statements, clean code writing, loops, string manipulation, data structures (lists, tuples, dictionaries, sets, arrays), and fundamental data analysis tools (NumPy and pandas).
- Next steps: Dive into data presentation and communication to transform data into meaningful insights for decision-making.
- Future outlook: A strong foundation in Python skills ready to be built upon in your data professional career.
This summary captures the essential elements of your progress and future focus, highlighting your accomplishments while leaving the audience excited for the next learning stage.
Congratulations! You have completed the
end-of-course project! You now have a tangible
product you can present to future employers that demonstrates
your Python proficiency. Wow, you’ve learned so
many new Python skills! First, you learned how
to use variables to store and label your data, and how to convert and
combine different data types such as integers and floats. Next, you learned how to call functions to perform useful actions on your data, and use operators to compare values. You also learned how to
write conditional statements to tell the computer how to make decisions based on your instructions. And you practiced writing
clean code that can be easily understood and reused
by other data professionals. Then you discovered how to use loops to automate repetitive tasks. You also learned how to manipulate strings by slicing, indexing, and formatting them. After that, you explored
fundamental data structures such as lists, tuples,
dictionaries, sets, and arrays. Lastly, you learned about
two of the most widely used and important Python tools for advanced data
analysis: NumPy and pandas. Coming up, you have even more
exciting discoveries to make. Now that you understand
how to create systems to prepare data for stakeholders, it’s time to start thinking
about how to present that data and make it useful for decision-making. You now have a strong
foundation of Python skills that you can continue to build on in your future career
as a data professional. So go get ready and continue
your learning journey.