Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks
Instructor365 CareersThe curriculum covers specialized modeling including Binomial and Multinomial Logistic Regression for brand choice, alongside Linear Regression for quantity elasticity.
The path combines traditional segmentation via K-Means and PCA with modern deep learning workflows for conversion prediction.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
An instructor presents a visual curriculum map highlighting the progression from theoretical foundations to K-means clustering and Principal Component Analysis for dimensionality reduction.
The instructor presents a menu of course resources including exercises, homework assignments, downloadable materials, and quizzes to support practical learning.
The image displays a presenter standing behind a graphic banner that connects the concept of Deep Learning, represented by a robot icon, to the business application of Predicting Customer Conversion.
An instructor introduces the topic of descriptive statistics, highlighted by a teal banner featuring a bar chart icon and clear text overlay.
Free
Instructional signals indicate a rapid coding style that may feel overwhelming without significant prior experience in Python and its associated libraries.
One sampled review suggests that some learners report that the 'why' behind specific statistical methods and code implementations is not sufficiently detailed.
The curriculum aligns well with the target of marketing-focused data science, provided prerequisites are met.