Finalize a Data Science Project (Coursera)

Offered by CertNexus,
Finalize a Data Science Project (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
Course 5 of 5 in the CertNexus Certified Data Science Practitioner Professional Certificate.
What You Will Learn

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

Syllabus

WEEK 1
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.

WEEK 2
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.

WEEK 3
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.

WEEK 4
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.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Data Science in Health Technology Assessment (Coursera) Coursera
Genentech

Data Science in Health Technology Assessment (Coursera)

This course explores key concepts and methods in Health Economics and Health Technology Assessment (HTA) and is intended for learners who have a foundation in data science, clinical science, regulatory and are new to this field and would like to understand basic principles used by payers for their reimbursement decisions.

Aug 17th 2026
3 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Aug 17th 2026
4 Weeks
Visualization for Data Journalism (Coursera) Coursera
University of Illinois at Urbana-Champaign

Visualization for Data Journalism (Coursera)

While telling stories with data has been part of the news practice since its earliest days, it is in the midst of a renaissance. Graphics desks which used to be deemed as “the art department,” a subfield outside the work of newsrooms, are becoming a core part of newsrooms’ operation. Those people (they often have various titles: data journalists, news artists, graphic reporters, developers, etc.) who design news graphics are expected to be full-fledged journalists and work closely with reporters and editors.

Aug 10th 2026
5-12 Weeks
Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Aug 3rd 2026
4 Weeks
Applied Text Mining in Python (Coursera) Coursera
University of Michigan

Applied Text Mining in Python (Coursera)

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

Aug 17th 2026
4 Weeks
Data Manipulation at Scale: Systems and Algorithms (Coursera) Coursera
University of Washington

Data Manipulation at Scale: Systems and Algorithms (Coursera)

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Aug 3rd 2026
4 Weeks
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Aug 17th 2026
3 Weeks
What is Data Science? (Coursera) Coursera
IBM

What is Data Science? (Coursera)

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Aug 3rd 2026
3 Weeks
Applied Plotting, Charting & Data Representation in Python (Coursera) Coursera
University of Michigan

Applied Plotting, Charting & Data Representation in Python (Coursera)

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework.

Aug 10th 2026
4 Weeks
VR and 360 Video Production (Coursera) Coursera
Google AR & VR

VR and 360 Video Production (Coursera)

Welcome to Daydream Impact Virtual Reality (VR) and 360 video production course! Our mission is to give you the skills needed to use VR to advocate for a cause and communicate your mission. This course will introduce you to Virtual Reality (VR) and 360 video production, guiding you through a step-by-step process to create VR content. To begin, we recommend taking a few minutes to explore the course site and review the material.

Aug 3rd 2026
4 Weeks
Scalable Machine Learning on Big Data using Apache Spark (Coursera) Coursera
IBM

Scalable Machine Learning on Big Data using Apache Spark (Coursera)

This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.

Aug 3rd 2026
4 Weeks