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Predictive Analytics – Project Capstone

Predictive Analytics – Project Capstone

A predictive modeling practicum for the predictive analytices course program.

A predictive modeling practicum for the predictive analytices course program.

$999 | Enroll Now
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  • Overview
  • Learning Outcomes
  • Instructors
  • Syllabus
  • Dates
  • Prerequisites
  • Student Stories
  • FAQS
  • Requirements
Menu
  • Overview
  • Learning Outcomes
  • Instructors
  • Syllabus
  • Dates
  • Prerequisites
  • Student Stories
  • FAQS
  • Requirements

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.

Intermediate/Advanced Level
4-Week Course
100% Online Courses
Expert Instructors
Teacher Assistant Support
Tution-Back Guarantee

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.

Instructors

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Mr. Ken Strasma

Mr. Ken Strasma

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 clien...

See Instructor Bio

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

2023

Sep 8, 2023 to Oct 6, 2023

2024

Mar 8, 2024 to Apr 5, 2024

2025

No classes scheduled at this time.

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Prerequisites

You must be familiar with predictive modeling and have sufficient programming level skills to write predictive model code in R or Python.

The courses listed below are prerequisites for enrollment in this course:

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Mapping in R Course

Predictive Analytics 1 with Python – Machine Learning Tools

This course introduces the basic paradigm for predictive modeling: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024
Mapping in R Course

Predictive Analytics 1 with R – Machine Learning Tools

This course introduces to the basic predictive modeling paradigm: classification and prediction.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024

What Our Students Say​

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Marta Fedyna

The book for this course is very good. Everything is explained in a really clear way. Videos were great too and help in understanding issues.

Marta Fedyna
Data Engineer w HSBC
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Jeff Mantel

Anthony did a great job of answering questions, adding explanations and expanding on ideas. The best teacher I have had at statistics.com so far

Jeff Mantel
Mantel Consulting
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Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

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Predictive Analytics 2 – Neural Nets and Regression with Python

Predictive Analytics 2 with Python – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, SQL, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 8, 2024
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Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 8, 2024
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Predictive Analytics 3 with Python – Dimension Reduction, Clustering, and Association Rules

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
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Predictive Analytics 3 with R – Dimension Reduction, Clustering, and Association Rules

This course, with a focus on R, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 12, 2023, Sep 8, 2023, Jan 12, 2024

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

Supplemental Information

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:

 

Chrome

  •  Color Enhancer (for colorblindness)
  • HelperBird (for colorblindness, dyslexia, and reading difficulties)

 

Firefox

  • Mobile Dyslexic
  • Color Vision Simulation (native accessibility feature)
  • Other native accessibility features instructions

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Miscellaneous

There is no additional information for this course.

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Predictive Analytics – Project Capstone
$999 | Enroll Now
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About Statistics.com

Statistics.com offers academic and professional education in statistics, analytics, and data science at beginner, intermediate, and advanced levels of instruction. Statistics.com is a part of Elder Research, a data science consultancy with 25 years of experience in data analytics.

 The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV)

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