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Sarah Haq,
Stacey McBrine,
and
Megan Smith Branch
This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes.
Read more
This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes.
Read more
This course is designed for business professionals that want to learn how to gather results from previous stages of the data science project and present them to stakeholders. Learners will communicate the results of a model to stakeholders, be shown how to build a basic web app to demonstrate machine learning models and implement and test pipelines that automate the model training, tuning and deployment processes.
The typical student in this course will have completed previous courses in the CDSP professional certificate program, and have several years of experience with computing technology, including some aptitude in computer programming.
Register for this course and see more details by visiting:
OpenCourser.com/course/7rex9p/finalize
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What's inside
Syllabus
Communicate Results to Stakeholders
In the previous courses in this specialization, you put your data through the extract, transform, and load (ETL) process, conducted an analysis of the data, and developed statistical models from the data that cover the three major disciplines of machine learning: classification, regression, and clustering. But you're not done yet. Now it's time to gather your results and present them to stakeholders. After all, you undertook the data science project to achieve business goals, so you need to demonstrate that you were actually successful in doing so. In this first module, you'll report your findings to the project's stakeholders.
Read more
Syllabus
Communicate Results to Stakeholders
In the previous courses in this specialization, you put your data through the extract, transform, and load (ETL) process, conducted an analysis of the data, and developed statistical models from the data that cover the three major disciplines of machine learning: classification, regression, and clustering. But you're not done yet. Now it's time to gather your results and present them to stakeholders. After all, you undertook the data science project to achieve business goals, so you need to demonstrate that you were actually successful in doing so. In this first module, you'll report your findings to the project's stakeholders.
Read more
Demonstrate Models in a Web App
One way to create a robust and interesting presentation is to show off your results in a web app. In this module, you'll focus on some of the major technologies that go into creating a web app that you might want to use in a demonstration.
Implement and Test Production Pipelines
Much of what you've done throughout the data science project can be automated in some way. The goal is to spend less time performing some of the more repetitive tasks, and more time on tasks that require your own judgment. This is where pipeline automation comes into play.
Apply What You've Learned
You'll work on a project in which you'll apply your knowledge of the material in this course to practical scenarios.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills and knowledge in communicating the results of machine learning models to stakeholders, which is a key aspect of real-world data science projects
Provides hands-on experience in building web apps to demonstrate machine learning models, which is a valuable skill for data scientists
Covers implementation and testing of production pipelines for automating the machine learning training, tuning, and deployment processes, which are essential for real-world data science applications
Taught by instructors with expertise in data science and machine learning, which ensures the quality of the course content
Assumes prior experience in data science and computing technology, which may not be suitable for complete beginners
Save this course
Save Finalize a Data Science Project to your list so you can find it easily later:
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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 Finalize a Data Science Project with these
activities:
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Expand your knowledge by reading a book that provides a comprehensive overview of machine learning using popular Python libraries.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book thoroughly
-
Take notes on key concepts and techniques
-
Complete the exercises and projects provided in the book
Practice data cleaning and transformation
Show steps
Strengthen your data cleaning and transformation skills by working through practice exercises that cover real-world scenarios.
Browse courses on
Data Cleaning
Show steps
-
Find data cleaning and transformation practice problems online
-
Work through the problems step-by-step
-
Review your solutions and identify areas for improvement
Follow Tutorials on Web App Development
Show steps
Gain hands-on experience in web app development through guided tutorials provided by platforms like Coursera or Udemy.
Browse courses on
Web App Development
Show steps
-
Identify a web app development tutorial
-
Set up your development environment
-
Follow the tutorial steps
-
Deploy your web app
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review supervised learning algorithms
Show steps
Start by reviewing the basics of supervised learning and explore different classification and regression algorithms to gain a deeper understanding.
Browse courses on
Supervised Learning
Show steps
-
Read online tutorials on supervised learning
-
Watch videos on classification and regression algorithms
-
Practice implementing these algorithms using coding exercises
Create a presentation on machine learning concepts
Show steps
Solidify your understanding of machine learning concepts by creating a presentation that explains them clearly and concisely to a non-technical audience.
Show steps
-
Review the key concepts of machine learning
-
Choose a specific topic to focus on
-
Gather supporting materials such as examples and visualizations
-
Write a clear and engaging script
-
Practice delivering your presentation
Attend a workshop on data visualization
Show steps
Enhance your ability to communicate data insights effectively by attending a workshop on data visualization techniques.
Browse courses on
Data Visualization
Show steps
-
Find a relevant workshop
-
Attend the workshop and take notes
-
Apply the techniques learned in your own projects
Solve Machine Learning Challenges
Show steps
Sharpen your machine learning skills by solving practical challenges on platforms like Kaggle or LeetCode.
Browse courses on
Machine Learning Algorithms
Show steps
-
Select a challenge platform
-
Choose a challenge
-
Develop and implement a solution
-
Evaluate and improve your solution
Volunteer at a Data Science Organization
Show steps
Gain practical experience and support the data science community by volunteering at events, workshops, or mentoring programs.
Show steps
-
Identify a data science organization
-
Inquire about volunteer opportunities
-
Attend volunteer training
-
Participate in volunteer activities
Create a compilation of resources on machine learning
Show steps
Organize and expand your knowledge by creating a comprehensive collection of resources on machine learning.
Browse courses on
Machine Learning
Show steps
-
Gather resources such as articles, tutorials, and videos
-
Organize the resources into categories or topics
-
Create a document or website to share your compilation
Develop a Presentation on a Data Science Project
Show steps
Enhance your communication skills and showcase your data science knowledge by creating a presentation on a project you've worked on.
Show steps
-
Choose a project to present
-
Outline the presentation structure
-
Gather data and create visuals
-
Write the presentation script
-
Rehearse and deliver the presentation
Build a machine learning model for a specific business problem
Show steps
Apply your learning by working on a project that involves building a machine learning model to address a real-world business problem.
Show steps
-
Identify a business problem that can be solved with machine learning
-
Gather and clean the necessary data
-
Choose and implement an appropriate machine learning algorithm
-
Evaluate and refine your model
-
Deploy your model and monitor its performance
Build a Model Deployment Pipeline
Show steps
Build a deployment pipeline to automate the deployment process and ensure a smooth transition of models from development to production.
Browse courses on
Model Deployment
Show steps
-
Design the pipeline architecture
-
Implement the pipeline components
-
Configure monitoring and logging
-
Deploy the pipeline
-
Monitor and maintain the pipeline
Volunteer as a data science mentor
Show steps
Deepen your understanding and strengthen your communication skills by mentoring others in the field of data science.
Browse courses on
Mentoring
Show steps
-
Find a mentoring program or organization
-
Prepare materials and resources for your mentees
-
Meet with your mentees regularly
-
Provide guidance and support
-
Reflect on your experiences
Review 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Expand your knowledge by reading a book that provides a comprehensive overview of machine learning using popular Python libraries.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly
- Take notes on key concepts and techniques
- Complete the exercises and projects provided in the book
Practice data cleaning and transformation
Show steps
Strengthen your data cleaning and transformation skills by working through practice exercises that cover real-world scenarios.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find data cleaning and transformation practice problems online
- Work through the problems step-by-step
- Review your solutions and identify areas for improvement
Follow Tutorials on Web App Development
Show steps
Gain hands-on experience in web app development through guided tutorials provided by platforms like Coursera or Udemy.
Browse courses on
Web App Development
Show steps
Show steps
Show steps
- Identify a web app development tutorial
- Set up your development environment
- Follow the tutorial steps
- Deploy your web app
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Review supervised learning algorithms
Show steps
Start by reviewing the basics of supervised learning and explore different classification and regression algorithms to gain a deeper understanding.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Read online tutorials on supervised learning
- Watch videos on classification and regression algorithms
- Practice implementing these algorithms using coding exercises
Create a presentation on machine learning concepts
Show steps
Solidify your understanding of machine learning concepts by creating a presentation that explains them clearly and concisely to a non-technical audience.
Show steps
Show steps
Show steps
- Review the key concepts of machine learning
- Choose a specific topic to focus on
- Gather supporting materials such as examples and visualizations
- Write a clear and engaging script
- Practice delivering your presentation
Attend a workshop on data visualization
Show steps
Enhance your ability to communicate data insights effectively by attending a workshop on data visualization techniques.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a relevant workshop
- Attend the workshop and take notes
- Apply the techniques learned in your own projects
Solve Machine Learning Challenges
Show steps
Sharpen your machine learning skills by solving practical challenges on platforms like Kaggle or LeetCode.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Select a challenge platform
- Choose a challenge
- Develop and implement a solution
- Evaluate and improve your solution
Volunteer at a Data Science Organization
Show steps
Gain practical experience and support the data science community by volunteering at events, workshops, or mentoring programs.
Show steps
Show steps
Show steps
- Identify a data science organization
- Inquire about volunteer opportunities
- Attend volunteer training
- Participate in volunteer activities
Create a compilation of resources on machine learning
Show steps
Organize and expand your knowledge by creating a comprehensive collection of resources on machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather resources such as articles, tutorials, and videos
- Organize the resources into categories or topics
- Create a document or website to share your compilation
Develop a Presentation on a Data Science Project
Show steps
Enhance your communication skills and showcase your data science knowledge by creating a presentation on a project you've worked on.
Show steps
Show steps
Show steps
- Choose a project to present
- Outline the presentation structure
- Gather data and create visuals
- Write the presentation script
- Rehearse and deliver the presentation
Build a machine learning model for a specific business problem
Show steps
Apply your learning by working on a project that involves building a machine learning model to address a real-world business problem.
Show steps
Show steps
Show steps
- Identify a business problem that can be solved with machine learning
- Gather and clean the necessary data
- Choose and implement an appropriate machine learning algorithm
- Evaluate and refine your model
- Deploy your model and monitor its performance
Build a Model Deployment Pipeline
Show steps
Build a deployment pipeline to automate the deployment process and ensure a smooth transition of models from development to production.
Browse courses on
Model Deployment
Show steps
Show steps
Show steps
- Design the pipeline architecture
- Implement the pipeline components
- Configure monitoring and logging
- Deploy the pipeline
- Monitor and maintain the pipeline
Volunteer as a data science mentor
Show steps
Deepen your understanding and strengthen your communication skills by mentoring others in the field of data science.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Find a mentoring program or organization
- Prepare materials and resources for your mentees
- Meet with your mentees regularly
- Provide guidance and support
- Reflect on your experiences
Career center
Learners who complete Finalize a Data Science Project will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for developing and applying statistical and machine learning models to large datasets. They use their expertise in data analysis and modeling to extract insights from data and communicate their findings to stakeholders. This course would be helpful for aspiring Data Scientists as it provides a comprehensive overview of the data science project lifecycle, from data gathering and analysis to model development and deployment. Learners will gain hands-on experience with machine learning techniques and learn how to communicate their findings effectively to stakeholders.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. They work closely with Data Scientists to develop and implement machine learning solutions that meet business needs. This course would be helpful for aspiring Machine Learning Engineers as it provides a strong foundation in machine learning techniques and model deployment. Learners will gain hands-on experience with machine learning algorithms and learn how to build and deploy models in a production environment.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to provide insights to businesses and help them make informed decisions. This course would be helpful for aspiring Data Analysts as it provides a comprehensive overview of the data analysis process, from data gathering and cleaning to data visualization and communication. Learners will gain hands-on experience with data analysis tools and techniques.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding business needs and translating them into technical requirements. They work closely with stakeholders to gather requirements, analyze data, and develop solutions that meet business objectives. This course would be helpful for aspiring Business Analysts as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work closely with stakeholders to gather requirements, design software solutions, and implement and test code. This course would be helpful for aspiring Software Engineers as it provides a strong foundation in software development principles and practices. Learners will gain hands-on experience with software development tools and techniques.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work closely with stakeholders to define product requirements, design product features, and launch and market products. This course would be helpful for aspiring Product Managers as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Consultant
Consultant
Consultants help businesses improve their performance by providing advice and expertise. They work closely with clients to identify problems, develop solutions, and implement change. This course would be may be helpful for aspiring Consultants as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Researcher
Researcher
Researchers conduct scientific research to advance knowledge and understanding. They use a variety of methods to collect and analyze data, and disseminate their findings through publications and presentations. This course would be may be helpful for aspiring Researchers as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Teacher
Teacher
Teachers educate students in a variety of subjects. They develop lesson plans, deliver instruction, and assess student learning. This course would be may be helpful for aspiring Teachers as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Journalist
Journalist
Journalists gather and report news and information to the public. They conduct interviews, research stories, and write articles, broadcasts, or online content. This course would be may be helpful for aspiring Journalists as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Librarian
Librarian
Librarians help people find and use information. They work in a variety of settings, including public libraries, school libraries, and corporate libraries. This course would be may be helpful for aspiring Librarians as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Archivist
Archivist
Archivists preserve and manage historical records. They work in a variety of settings, including libraries, museums, and government agencies. This course would be may be helpful for aspiring Archivists as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Curator
Curator
Curators manage and display museum collections. They work closely with other museum staff to develop exhibits, conduct research, and provide educational programs. This course would be may be helpful for aspiring Curators as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Historian
Historian
Historians research and write about the past. They work in a variety of settings, including universities, libraries, and museums. This course would be may be helpful for aspiring Historians as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
Anthropologist
Anthropologist
Anthropologists study human societies and cultures. They work in a variety of settings, including universities, research institutions, and government agencies. This course would be may be helpful for aspiring Anthropologists as it provides a strong foundation in data analysis and communication. Learners will gain hands-on experience with data analysis tools and techniques, and learn how to communicate their findings effectively to stakeholders.
For more career information including salaries, visit:
OpenCourser.com/course/7rex9p/finalize
Reading list
We've selected 11 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
Finalize a Data Science Project.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for learners who want to learn how to build and deploy machine learning models using Python.
Provides a comprehensive overview of natural language processing (NLP) using Python. It valuable resource for learners who want to learn how to use Python for NLP tasks such as text classification, sentiment analysis, and machine translation.
Save
Provides a comprehensive overview of big data and its impact on society. It valuable resource for learners who want to understand the potential and challenges of big data.
Provides a comprehensive overview of deep learning, with a focus on using Python libraries such as Keras and TensorFlow. It valuable resource for learners who want to learn how to build and deploy deep learning models using Python.
Provides a comprehensive overview of Python libraries and tools for data science. It valuable resource for learners who want to learn how to use Python for data analysis and machine learning.
Provides a comprehensive overview of the data science process, from data collection and cleaning to model building and evaluation. It valuable resource for learners who want to gain a foundational understanding of data science and its applications in business.
Focuses on data analysis using Python libraries such as Pandas, NumPy, and Matplotlib. It good choice for learners who want to learn how to use Python for data exploration and visualization.
Provides a hands-on introduction to data science using Python. It good choice for learners who want to learn the basics of data science and how to use Python for data analysis and machine learning.
Provides a comprehensive overview of data science for beginners. It good choice for learners who want to learn the basics of data science and how it can be used in various fields.
Provides a comprehensive overview of machine learning for beginners. It good choice for learners who want to learn the basics of machine learning and how it can be used in various fields.
Provides a comprehensive overview of TensorFlow for beginners. It good choice for learners who want to learn the basics of TensorFlow and how it can be used for deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7rex9p/finalize
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Effort
4 weeks of study, 1–3 hours per week
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructors
Sarah Haq
Stacey McBrine
Megan Smith Branch
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
CertNexus Certified Data Science Practitioner. It's comprised of five courses:
- Extract, Transform, and Load Data
- Train Machine Learning Models
- Address Business Issues with Data Science
- Analyze Data
- Finalize a Data Science Project (viewing)
Good to know
Develops skills and knowledge in communicating the results of machine learning models to stakeholders, which is a key aspect of real-world data science projects
Provides hands-on experience in building web apps to demonstrate machine learning models, which is a valuable skill for data scientists
Covers implementation and testing of production pipelines for automating the machine learning training, tuning, and deployment processes, which are essential for real-world data science applications
Taught by instructors with expertise in data science and machine learning, which ensures the quality of the course content
Assumes prior experience in data science and computing technology, which may not be suitable for complete beginners
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