Choice Modeling for Marketing in R

Starting at USD 39.00
4 hours duration

Learn to analyze and model customer choice data in R.

Every day, individuals are faced with a multitude of choices, ranging from selecting a product like orange juice or a car, determining who to vote for, to deciding on the most efficient mode of transportation for their daily commute. Professionals in various fields, such as marketers, retailers, product designers, political scientists, transportation planners, and sociologists, are keen on comprehending the underlying factors that drive these choices. To this end, choice models serve as valuable tools in predicting individuals' preferences based on the characteristics of the available options. Consequently, these models play a crucial role in informing significant product design decisions. This course aims to equip participants with the necessary skills to effectively analyze choice data, estimate choice models using the statistical programming language R, and effectively communicate their findings. The curriculum encompasses the examination of real-world choices, as well as the application of conjoint analysis, a survey-based approach, to understand decision-making processes. By the end of this course, students will possess the knowledge and expertise to organize choice data, employ statistical techniques to estimate choice models, and effectively present their research outcomes.

About Provider

DataCamp
DataCamp
Online Education Provider ยท 410 courses
Description
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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