Predictive Analytics – Project Capstone
A predictive modeling practicum for the predictive analytices course program.
Overview
In this course, students will apply data mining techniques in a real world case study. The case study concerns microtargeting in political campaigns, but the principles apply equally to any marketing campaign involving individual-level messaging. This course is really a “lab” for practically testing your skills in a real world context. You should have some facility with R or Python, and some familiarity with predictive modeling, before taking this course.
Note: This course is also listed as Persuasion Analytics, the study of micro-targeting and uplift modeling. The data in the course are sizeable and complex, and the domain (political targeting) is relatively new and unlikely to be familiar to most students, hence the course is ideal as a real-world case study for analytics students who need to be prepared to apply their analytical skills to new situations. Students who sign up for Persuasion Analytics will use curated, reduced data sets and an Excel add-in; students who are taking Predictive Analytics Project Capstone as part of their certificate program must use the full dataset and either R or Python.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
- Prepare data for a predictive modeling task
- Develop predictive models Integrate the results of A-B tests for an uplift model
- Assess model performance
- Improve single model performance using ensembles
- Implement models for a real decison scenario
Who Should Take This Course
The course will also be useful as a guided project for anyone who has learned predictive modeling methods using prepared and curated data, and wants to gain experience implementing them in a real-world context with messy data.
Student in our Programming for Data Science Certificate program will take this course as their capstone project.
Our Instructors
Mr. Ken Strasma
Course Syllabus
Week 1
Setting the Scene
- Why political campaigns need to target
- Phases of a campaign
- Finding the right targets for the right phase
- Getting to know the data
- Understanding and engineering features
- Transformations
Week 2
Developing Predictive Models
- Traditional vs. individual level targeting
- Deciding what to predict
- The model-building process
- Assessing models
Week 3
Combining Models
- Ensembles
- Controlled and Natural Experiments
- A-B tests
- Uplift – Combining A-B tests with Predictive Models
Week 4
Implementation and Actions
- Deciding who to target and with what message
Class Dates
2022
Instructors:
2023
Instructors:
Instructors:
2024
Instructors:
Prerequisites
You must be familiar with predictive modeling and have sufficient programming level skills to write predictive model code in R or Python.
Predictive Analytics 1 with Python – Machine Learning Tools
- Skill: Introductory, Intermediate
- Credit Options: CEU
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Additional Information
Homework
The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.
This course also has example software codes, supplemental readings available online.
Course Text
Depending on topic being covered, reference materials will be provided as required.
Software
To do the project in the course you will need access to and some familarity with R or Python.
Supplemental Information
Literacy, Accessibility, and Dyslexia
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