In this project-based course you'll be put into a scenario to assist Netflix in rejuvenating the streaming service's growth using Data Analysis and Python.
Conduct a Choice-Based Conjoint Analysis for Netflix with Python
1h 39m 35s
English
Paid
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. In this project-based course you'll be put into a scenario to assist Netflix in rejuvenating the streaming service's growth using Data Analysis and Python.
Course facts
- Lessons
- 21
- Duration
- 1 hour 39 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- Zero To Mastery
- Price
- Premium
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 (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?
0:00
/ #1: Project Introduction
All Course Lessons (21)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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.