- 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.
Ken Strasma is a pioneer in the field of predictive analytics in high-stakes Presidential campaigns, serving as the National Targeting Director for President Obama’s historic 2008 campaign and for John Kerry’s 2004 presidential campaign. He produced the predictive analytics models used by the campaigns, and helped popularize the use of that technology.
Strasma is now the co-founder and CEO of HaystaqDNA, a firm that provides predictive analytics and strategic consulting services for corporations, non-profits and membership organizations.
Since 2008, Strasma has consulted on hundreds of political and corporate projects in the United States and internationally. HastaqDNA clients include multiple Fortune 500 companies with a combined market capitalization of more than $600 billion. Haystaq commercial clients span the worlds of entertainment, sports, consumer goods and healthcare. Haystaq has provided predictive analytics in international political campaigns in four continents.
Ken is the author of numerous articles and studies regarding targeting, marketing, demographics and social media analysis.
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
Developing Predictive Models
- Traditional vs. individual level targeting
- Deciding what to predict
- The model-building process
- Assessing models
- Controlled and Natural Experiments
- A-B tests
- Uplift – Combining A-B tests with Predictive Models
Implementation and Actions
- Deciding who to target and with what message
You must be familiar with predictive modeling and have sufficient programming level skills to write predictive model code in R or Python.
- Skill: Introductory, Intermediate
- Credit Options: CEU
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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.
Depending on topic being covered, reference materials will be provided as required.
To do the project in the course you will need access to and some familarity with R or Python.
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions