Marketing Analytics

MBA/EWMBA 263 (3-unit)

In this course, students will gain hands-on experience with data analytics for the purpose of learning about and marketing to customers. The goal is not to produce experts in statistics; the goal is to gain the competency to interact with and manage a data science team.

Tech Platform

  • Jupyter
  • Notebook +
  • Python-kernel

Business Context

  • Customer acquisition, targeting and retention
  • Experimental design of products and promotions
  • Search Engine Optimization
  • Web analytics (marketing)

Concepts

  • Descriptive Models
  • Predictive Models and Inference
  • Unsupervised Machine Learning
  • Supervised Machine Learning

Algorithms

  • Segmentation and Clustering with RFM
  • Logistic regression
  • Neural Networks/Deep learning
  • Discrete Choice models and Full-factorial designs

Marketing Analytics

MBA/EWMBA 263 (3-unit)

In this course, students will gain hands-on experience with data analytics for the purpose of learning about and marketing to customers. The goal is not to produce experts in statistics; the goal is to gain the competency to interact with and manage a data science team.

Business Context

  • Customer acquisition, targeting and retention
  • Experimental design of products and promotions
  • Search Engine Optimization
  • Web analytics (marketing)

Concepts

  • Descriptive Models
  • Predictive Models and Inference
  • Unsupervised Machine Learning
  • Supervised Machine Learning

Algorithms

  • Segmentation and Clustering with RFM
  • Logistic regression
  • Neural Networks/Deep learning
  • Discrete Choice models and Full-factorial designs

Tech Platform

  • Jupyter
  • Notebook +
  • Python-kernel

Marketing Analytics

MBA/EWMBA 263 (3-unit)

In this course, students will gain hands-on experience with data analytics for the purpose of learning about and marketing to customers. The goal is not to produce experts in statistics; the goal is to gain the competency to interact with and manage a data science team.

Business Context

  • Customer acquisition, targeting and retention
  • Experimental design of products and promotions
  • Search Engine Optimization
  • Web analytics (marketing)

Concepts

  • Descriptive Models
  • Predictive Models and Inference
  • Unsupervised Machine Learning
  • Supervised Machine Learning

Algorithms

  • Segmentation and Clustering with RFM
  • Logistic regression
  • Neural Networks/Deep learning
  • Discrete Choice models and Full-factorial designs

Tech Platform

  • Jupyter
  • Notebook +
  • Python-kernel

Marketing Analytics

MBA/EWMBA 263 (3-unit)

In this course, students will gain hands-on experience with data analytics for the purpose of learning about and marketing to customers. The goal is not to produce experts in statistics; the goal is to gain the competency to interact with and manage a data science team.

Business Context

  • Customer acquisition, targeting and retention
  • Experimental design of products and promotions
  • Search Engine Optimization
  • Web analytics (marketing)

Concepts

  • Descriptive Models
  • Predictive Models and Inference
  • Unsupervised Machine Learning
  • Supervised Machine Learning

Algorithms

  • Segmentation and Clustering with RFM
  • Logistic regression
  • Neural Networks/Deep learning
  • Discrete Choice models and Full-factorial designs

Tech Platform

  • Jupyter
  • Notebook +
  • Python-kernel

Marketing Analytics

MBA/EWMBA 263 (3-unit)

In this course, students will gain hands-on experience with data analytics for the purpose of learning about and marketing to customers. The goal is not to produce experts in statistics; the goal is to gain the competency to interact with and manage a data science team.

Business Context

  • Customer acquisition, targeting and retention
  • Experimental design of products and promotions
  • Search Engine Optimization
  • Web analytics (marketing)

Concepts

  • Descriptive Models
  • Predictive Models and Inference
  • Unsupervised Machine Learning
  • Supervised Machine Learning

Algorithms

  • Segmentation and Clustering with RFM
  • Logistic regression
  • Neural Networks/Deep learning
  • Discrete Choice models and Full-factorial designs

Tech Platform

  • Jupyter
  • Notebook +
  • Python-kernel