Finalize a Data Science Project

This course is part of CertNexus Certified Data Science Practitioner Professional Certificate

Instructors: Sarah Haq

Access provided by ASTRA NAVIGATION INC.

1,641 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Communicate results of a model via web apps, and implement an d test pipelines that automate the model training, tuning and deployment process.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments¹

AI Graded see disclaimer

Taught in English

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Build your Data Analysis expertise

This course is part of the CertNexus Certified Data Science Practitioner Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from CertNexus

There are 4 modules in this course

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.

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.

What's included

6 videos3 readings1 assignment1 peer review1 discussion prompt

6 videos Total 45 minutes

Course Intro: Finalize a Data Science Project 2 minutes
Audience and Insights 7 minutes
Factors That Drive Outcomes 10 minutes
Present Model Results 5 minutes
Visuals and Dashboards 12 minutes
Cumulative Gains Charts and Lift Charts 8 minutes

3 readings Total 22 minutes

Overview 2 minutes
Get help and meet other learners. Join your Community! 5 minutes
Guidelines for Communicating Results to Stakeholders 15 minutes

1 assignment Total 30 minutes

Communicating Results to Stakeholders 30 minutes

1 peer review Total 60 minutes

Communicating Results to Stakeholders 60 minutes

1 discussion prompt Total 5 minutes

Reflect on What You've Learned 5 minutes

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.

What's included

7 videos2 readings1 assignment1 discussion prompt

7 videos Total 62 minutes

Web App 6 minutes
HTML, CSS, and JavaScript 10 minutes
Web Frameworks 5 minutes
Flask and Django 7 minutes
Demonstrating Models in a Web App (Part 1 of 3) 8 minutes
Demonstrating Models in a Web App (Part 2 of 3) 13 minutes
Demonstrating Models in a Web App (Part 3 of 3) 13 minutes

2 readings Total 7 minutes

Overview 2 minutes
Guidelines for Demonstrating Models in a Web App 5 minutes

1 assignment Total 30 minutes

Demonstrating Models in a Web App 30 minutes

1 discussion prompt Total 5 minutes

Reflect on What You've Learned 5 minutes

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.