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Conduct a Choice-Based Conjoint Analysis for Netflix with Python

1h 39m 35s
English
Paid
Updated September 2026

Conduct a Choice-Based Conjoint Analysis for Netflix with Python is a 21-lesson 1 hour 39 minutes self-paced course by Zero To Mastery. This project-based course puts you in a practical scenario: using data analysis and Python to help a streaming service, framed around Netflix, find ways to reignite growth.

Course facts

Lessons
21
Duration
1 hour 39 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Zero To Mastery
Price
Premium

This project-based course puts you in a practical scenario: using data analysis and Python to help a streaming service, framed around Netflix, find ways to reignite growth.

Rather than the traditional approach of surveying users feature by feature, the course teaches Choice-Based Conjoint Analysis — a market research method grounded in science and analytics that looks at consumer preferences holistically, across combinations of features rather than one at a time. Across 21 lessons, you apply this method directly to the Netflix-style scenario, working through the analysis end to end in Python.

Instead of the traditional analytics method of surveying users based on individual features, you'll use the Choice-Based Conjoint Analysis method, a type of market research that is grounded in science and analytics and examines consumer preferences in a holistic method.

Who teaches Conduct a Choice-Based Conjoint Analysis for Netflix with Python? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Conduct a Choice-Based Conjoint Analysis for Netflix with Python?

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#1: Project Introduction
All Course Lessons (21)
#Lesson TitleDurationAccess
1
Project Introduction Demo
02:03
2
Problem Statement
03:21
3
Choice-Based Conjoint Analysis
06:34
4
Python - Directory and Libraries
03:15
5
Python - Preparing Data
02:33
6
Technical Process
01:54
7
Python - Regression Model
03:59
8
Python - Retrieving Partworths
05:56
9
Python - Customizing Visualization
06:26
10
Python - Looking at Specific Drivers
05:17
11
Python - Lollipop Chart
08:39
12
Python - Feature Coefficients
05:47
13
Python - Feature Importance
02:30
14
Python - Preparing Relative Importances
02:57
15
Python - Plotting Tree Maps
07:06
16
Project Update
03:15
17
Interaction Terms
03:33
18
Python - Interaction Terms
05:45
19
Python - Interaction Terms Results
12:45
20
Next Steps
04:36
21
Limitations of Conjoint Analysis
01:24
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Frequently asked questions

What prior knowledge do I need before enrolling in this course?
Before enrolling in this course, it's beneficial to have a foundational understanding of Python, as the course involves extensive use of Python libraries and data manipulation techniques. Familiarity with basic data analysis concepts will also be helpful since the course focuses on applying these skills to conduct a choice-based conjoint analysis.
What will I be able to build by the end of this course?
By the end of the course, you will be equipped to conduct a choice-based conjoint analysis specifically tailored for Netflix. This includes preparing data, using Python to build regression models, retrieving partworths, and visualizing results through various Python libraries and techniques.
Who is the target audience for this course?
The course is aimed at individuals interested in data analysis and market research, particularly those who wish to apply Python skills to real-world business scenarios. It is suitable for data analysts, researchers, and marketing professionals looking to enhance their abilities in conducting conjoint analyses.
How does the depth of this course compare to similar courses?
This course provides a focused exploration of choice-based conjoint analysis using Python, covering both technical and application aspects through 21 detailed lessons. It offers a unique project-based approach specific to Netflix, allowing for practical experience that may not be available in broader data science courses.
What specific tools or platforms will be used in this course?
The course makes extensive use of Python and its libraries for data preparation, visualization, and model building. Tools such as regression models, lollipop charts, and tree maps are specifically covered to analyze and visualize the conjoint analysis results.
What topics are not covered in this course?
While the course covers choice-based conjoint analysis comprehensively, it does not delve into other forms of conjoint analysis or broader statistical methods beyond those necessary for the project. It also does not cover advanced machine learning techniques unrelated to conjoint analysis.
What is the time commitment required for this course?
The course consists of 21 lessons, each focusing on different aspects of conducting a choice-based conjoint analysis. Although the runtime is not specified, students should expect to invest time in both following the lessons and applying the concepts through hands-on project work to fully grasp the material.