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Finalize a Data Science Project

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.

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.

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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.

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

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

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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'

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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

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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

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Strengthen your data cleaning and transformation skills by working through practice exercises that cover real-world scenarios.

Browse courses on Data Cleaning

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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

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Gain hands-on experience in web app development through guided tutorials provided by platforms like Coursera or Udemy.

Browse courses on Web App Development

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Identify a web app development tutorial
Set up your development environment
Follow the tutorial steps
Deploy your web app

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Review supervised learning algorithms

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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

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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

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Solidify your understanding of machine learning concepts by creating a presentation that explains them clearly and concisely to a non-technical audience.

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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

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Enhance your ability to communicate data insights effectively by attending a workshop on data visualization techniques.

Browse courses on Data Visualization

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Find a relevant workshop
Attend the workshop and take notes
Apply the techniques learned in your own projects

Solve Machine Learning Challenges

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Sharpen your machine learning skills by solving practical challenges on platforms like Kaggle or LeetCode.

Browse courses on Machine Learning Algorithms

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Select a challenge platform
Choose a challenge
Develop and implement a solution
Evaluate and improve your solution

Volunteer at a Data Science Organization

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Gain practical experience and support the data science community by volunteering at events, workshops, or mentoring programs.

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Identify a data science organization
Inquire about volunteer opportunities
Attend volunteer training
Participate in volunteer activities

Create a compilation of resources on machine learning

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Organize and expand your knowledge by creating a comprehensive collection of resources on machine learning.

Browse courses on Machine Learning

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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

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Enhance your communication skills and showcase your data science knowledge by creating a presentation on a project you've worked on.

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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

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Apply your learning by working on a project that involves building a machine learning model to address a real-world business problem.

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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

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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

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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

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Deepen your understanding and strengthen your communication skills by mentoring others in the field of data science.

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Consultant

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.

See salaries and explore the career path for Researcher

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.

See salaries and explore the career path for Teacher

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.

See salaries and explore the career path for Journalist

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.

See salaries and explore the career path for Librarian

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.

See salaries and explore the career path for Archivist

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.

See salaries and explore the career path for Curator

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.

See salaries and explore the career path for Historian

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.

See salaries and explore the career path for Anthropologist

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.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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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.

Finalize a Data Science Project

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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