Finalize a Data Science Project

CertNexus via Coursera

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Overview

Finalize a Data Science Project is a focused course designed to guide data scientists and analysts through the crucial final stages of completing a data science project. This course emphasizes best practices for refining, validating, documenting, and deploying data science solutions to ensure they deliver reliable and actionable insights. Participants learn how to perform comprehensive model evaluation, validation, and tuning to maximize accuracy and performance. The course covers techniques for effective data visualization and storytelling to clearly communicate results to...

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.