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Emmanuel Acheampong
Welcome to the “Deploying a Python data analytics web app on Heroku” guided project.
Read more
Welcome to the “Deploying a Python data analytics web app on Heroku” guided project.
Read more
Welcome to the “Deploying a Python data analytics web app on Heroku” guided project.
This project is for anyone interested in breaking or transitioning into the data science field and hopes to build a portfolio that stands out with unique projects. In this project we’re going to be building and deploying a python data analytics web application leveraging the General Social Survey data, which collects information and records of behaviours, experiences and opinions of residents of the Us and is funded by the National Science Foundation, particularly finding the correlation between education, income and happiness for US residents in 2016.
At the end of this project, learners will be able to deploy a python data analytics website using Python, Streamlit, Git and Heroku that they can show to potential hiring managers and recruiters as part of their portfolio.
Register for this course and see more details by visiting:
OpenCourser.com/course/p4pcfa/deploying
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What's inside
Syllabus
Project Overview
Deploying a Python data analytics web app on Heroku using real-world data, is an intermediate level Python project that seeks to help anyone looking to break into data science or transition into it, be able to build a data analytics web application using Python and Streamlit and deploy it online using Heroku. This in turn will facilitate building a unique data science and ML portfolio that will help them demonstrate their knowledge using real-world data and help them stand out on the job market.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Specifically made for data scientists or those seeking to transition into data science
Students will be able to build a portfolio with real-world data
Teaches students to deploy a Python web application
Integrates Python, Streamlit, Git and Heroku to provide practical skills
Creates a strong foundation for data science beginners
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deploying a Python Data Analytics web app on Heroku with these
activities:
Python Refresher
Show steps
Review fundamental concepts and syntax of Python to ensure a solid foundation for the course.
Browse courses on
Python
Show steps
-
Review basic data types, operators, and control flow.
-
Practice writing simple Python scripts.
-
Use online resources and tutorials to reinforce understanding.
-
Complete coding challenges or exercises to apply knowledge.
-
Join a discussion forum or study group for support and collaboration.
Data Science for Business
Show steps
Provide a theoretical foundation in data science concepts and real-world business applications for effective problem-solving.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read chapters on data collection, preparation, and analysis.
-
Understand statistical and machine learning concepts.
-
Apply knowledge to case studies and hands-on exercises.
-
Summarize key takeaways and connect them to the deployed web application.
-
Discuss insights and applications with peers.
Interactive Streamlit Tutorial
Show steps
Follow a step-by-step tutorial to build a simple Streamlit dashboard, providing a practical introduction to the framework.
Browse courses on
Streamlit
Show steps
-
Install Streamlit and create a project.
-
Build a basic user interface with widgets.
-
Add charts and visualizations to display data.
-
Add interactivity with user input and event handling.
-
Deploy the dashboard to share with others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Data Manipulation Exercises
Show steps
Practice data manipulation techniques in Python to improve proficiency in handling and transforming data.
Browse courses on
Data Wrangling
Show steps
-
Import necessary libraries.
-
Load and explore the dataset.
-
Perform data cleaning operations (e.g., handling missing values, removing duplicates).
-
Transform the data using operations like grouping, sorting, filtering.
-
Save the modified dataset for further analysis.
Peer Mentoring
Show steps
Support fellow students in understanding the course material, completing assignments, and preparing for the data science field.
Browse courses on
Mentorship
Show steps
-
Join or start a peer mentoring program.
-
Pair up with other students for regular check-ins.
-
Share knowledge, experiences, and resources.
-
Provide constructive feedback and encouragement.
-
Build a network of connections within the data science community.
Data Visualization Project
Show steps
Create an interactive data visualization dashboard using Tableau or Power BI to present insights from the deployed web application.
Browse courses on
Data Visualization
Show steps
-
Connect to the deployed web application as a data source.
-
Explore and analyze the data for insights.
-
Design and build interactive dashboards with charts and visualizations.
-
Add filters, tooltips, and other interactive features.
-
Share the dashboard for stakeholder review and feedback.
Data Hackathon
Show steps
Participate in a data hackathon to test skills, collaborate, and gain real-world experience in a competitive environment.
Browse courses on
Data Science
Show steps
-
Identify a relevant data hackathon and form a team.
-
Gather and analyze data, explore different approaches.
-
Develop and implement data science solutions.
-
Document and present findings.
-
Reflect on the experience and identify areas for improvement.
Python Refresher
Show steps
Review fundamental concepts and syntax of Python to ensure a solid foundation for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic data types, operators, and control flow.
- Practice writing simple Python scripts.
- Use online resources and tutorials to reinforce understanding.
- Complete coding challenges or exercises to apply knowledge.
- Join a discussion forum or study group for support and collaboration.
Data Science for Business
Show steps
Provide a theoretical foundation in data science concepts and real-world business applications for effective problem-solving.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read chapters on data collection, preparation, and analysis.
- Understand statistical and machine learning concepts.
- Apply knowledge to case studies and hands-on exercises.
- Summarize key takeaways and connect them to the deployed web application.
- Discuss insights and applications with peers.
Interactive Streamlit Tutorial
Show steps
Follow a step-by-step tutorial to build a simple Streamlit dashboard, providing a practical introduction to the framework.
Browse courses on
Streamlit
Show steps
Show steps
Show steps
- Install Streamlit and create a project.
- Build a basic user interface with widgets.
- Add charts and visualizations to display data.
- Add interactivity with user input and event handling.
- Deploy the dashboard to share with others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Data Manipulation Exercises
Show steps
Practice data manipulation techniques in Python to improve proficiency in handling and transforming data.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Import necessary libraries.
- Load and explore the dataset.
- Perform data cleaning operations (e.g., handling missing values, removing duplicates).
- Transform the data using operations like grouping, sorting, filtering.
- Save the modified dataset for further analysis.
Peer Mentoring
Show steps
Support fellow students in understanding the course material, completing assignments, and preparing for the data science field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Join or start a peer mentoring program.
- Pair up with other students for regular check-ins.
- Share knowledge, experiences, and resources.
- Provide constructive feedback and encouragement.
- Build a network of connections within the data science community.
Data Visualization Project
Show steps
Create an interactive data visualization dashboard using Tableau or Power BI to present insights from the deployed web application.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Connect to the deployed web application as a data source.
- Explore and analyze the data for insights.
- Design and build interactive dashboards with charts and visualizations.
- Add filters, tooltips, and other interactive features.
- Share the dashboard for stakeholder review and feedback.
Data Hackathon
Show steps
Participate in a data hackathon to test skills, collaborate, and gain real-world experience in a competitive environment.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify a relevant data hackathon and form a team.
- Gather and analyze data, explore different approaches.
- Develop and implement data science solutions.
- Document and present findings.
- Reflect on the experience and identify areas for improvement.
Career center
Learners who complete Deploying a Python Data Analytics web app on Heroku will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts analyze data to extract meaningful insights and trends that can help organizations make better decisions. This course can help you develop the skills needed to become a Data Analyst, including data cleaning, wrangling, and visualization. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use data to build predictive models and solve complex problems. This course can help you develop the skills needed to become a Data Scientist, including data analysis, machine learning, and statistical modeling. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course can help you develop the skills needed to become a Machine Learning Engineer, including machine learning algorithms, data engineering, and model deployment. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Machine Learning Engineers.
Business Analyst
Business Analyst
Business Analysts use data to understand business needs and improve decision-making. This course can help you develop the skills needed to become a Business Analyst, including data analysis, data visualization, and communication skills. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Business Analysts.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines and infrastructure. This course can help you develop the skills needed to become a Data Engineer, including data management, data integration, and data quality. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Engineers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to become a Software Engineer, including programming, data structures, and algorithms. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Software Engineers.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. This course can help you develop the skills needed to become a Data Journalist, including data analysis, data visualization, and writing. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Journalists.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. This course can help you develop the skills needed to become a Product Manager, including market research, product planning, and product launch. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Product Managers.
Actuary
Actuary
Actuaries use data to assess risk and make financial decisions. This course can help you develop the skills needed to become an Actuary, including data analysis, financial modeling, and statistical modeling. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Actuaries.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course can help you develop the skills needed to become a Quantitative Analyst, including financial modeling, data analysis, and statistical modeling. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Quantitative Analysts.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course can help you develop the skills needed to become a Statistician, including data analysis, statistical modeling, and data visualization. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Statisticians.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of organizations. This course can help you develop the skills needed to become an Operations Research Analyst, including data analysis, optimization, and simulation. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Operations Research Analysts.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data to create visual representations of data. This course can help you develop the skills needed to become a Data Visualization Specialist, including data visualization techniques, data analysis, and design principles. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Visualization Specialists.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. This course can help you develop the skills needed to become a Market Researcher, including data analysis, market research methods, and data visualization. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Market Researchers.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and oversee data science projects. This course can help you develop the skills needed to become a Data Science Manager, including data management, project management, and communication skills. Additionally, the course will teach you how to use Python and Streamlit to deploy a data analytics web application, which is a valuable skill for Data Science Managers.
For more career information including salaries, visit:
OpenCourser.com/course/p4pcfa/deploying
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deploying a Python Data Analytics web app on Heroku .
Save
Provides a comprehensive introduction to Python data analytics, covering topics such as data cleaning, data wrangling, data visualization, and machine learning. It valuable resource for those who are new to Python data analytics or who want to learn more about the field.
Provides a comprehensive introduction to data science for business. It covers topics such as data mining, machine learning, and data visualization. It valuable resource for those who are new to data science or who want to learn more about the field.
Provides a comprehensive introduction to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for those who are new to machine learning or who want to learn more about the field.
Provides a comprehensive introduction to data analysis with Pandas and Python. It covers topics such as data cleaning, data wrangling, and data visualization. It valuable resource for those who are new to data analysis or who want to learn more about the field.
Provides a comprehensive overview of the General Social Survey (GSS), a large-scale survey of American society. It covers topics such as the history of the GSS, the methodology of the GSS, and the data collected by the GSS. It valuable resource for those who are interested in learning more about the GSS or who want to use the GSS data for research.
Save
Provides a comprehensive overview of happiness and how to achieve it. It covers topics such as the nature of happiness, the causes of happiness, and the practices that can lead to happiness. It valuable resource for those who are interested in learning more about happiness or who want to improve their own happiness.
Save
Provides a personal account of one woman's year-long journey to happiness. It covers topics such as the importance of gratitude, the power of social connections, and the benefits of mindfulness. It valuable resource for those who are interested in learning more about happiness or who want to improve their own happiness.
Save
Provides a scientific approach to happiness. It covers topics such as the nature of happiness, the causes of happiness, and the practices that can lead to happiness. It valuable resource for those who are interested in learning more about happiness or who want to improve their own happiness.
Save
Provides a comprehensive overview of authentic happiness. It covers topics such as the nature of authentic happiness, the causes of authentic happiness, and the practices that can lead to authentic happiness. It valuable resource for those who are interested in learning more about happiness or who want to improve their own happiness.
Provides a thought-provoking exploration of happiness. It covers topics such as the nature of happiness, the illusions we have about happiness, and the ways we can find happiness. It valuable resource for those who are interested in learning more about happiness or who want to improve their own happiness.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/p4pcfa/deploying
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Emmanuel Acheampong
Language
English
Good to know
Specifically made for data scientists or those seeking to transition into data science
Students will be able to build a portfolio with real-world data
Teaches students to deploy a Python web application
Integrates Python, Streamlit, Git and Heroku to provide practical skills
Creates a strong foundation for data science beginners
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