- 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.
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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This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
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This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
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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