Predictive Analytics Mastery Series

Predictive Analytics Mastery Series

In this Mastery Series you’ll learn the standard techniques in data mining, the science of learning from data and how to perform predictive modeling using either R, Python, or Excel.
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Overview

In this 3-course Mastery Series, you will learn the standard techniques for predictive modeling and unsupervised learning. Hands-on training allows you to apply data mining algorithms to real data and — using XLMiner, a data-mining add-in for Excel, R or Python — to interpret the results.

Best of all, you will be taught from leaders in this field, including the authors of Data Mining for Business Analytics, a Wiley global best-seller, and be able to apply these skills at work, in your research, or learning practicum.  

Who This Mastery Serves

Enroll in our Predictive Analytics Mastery Series if you are a student or professional looking to unlock the power of predictive analytics and data mining using Excel, Python or R programming. A Record of Mastery will elevate your career knowledge and opportunities if you are a:

  • Data Scientist
  • IT Professional
  • Programmer/Developer
  • Business Analyst
  • Researcher
  • Marketing Technologist

Learning Outcomes

When you earn your Record of Mastery in Predictive Analytics, you will gain an in-depth understanding of data mining, the science of learning from data and how to perform predictive modeling using either R, Python, or Analytic Solver Data Mining (an Excel-add in, previously known as XLMiner). You will also be able to:

  • Distinguish between profiling (explanation) tasks and prediction tasks for linear and logistic regression
  • Specify and interpret linear regression models to predict continuous outcomes
  • Specify and interpret logistic regression models for classification
  • Use discriminant analysis for classification
  • Use neural nets for prediction and classification
  • Preprocess text for text mining, and use a predictive model with the resulting matrix
  • Understand issues related to using too many predictors (the “curse of dimensionality”)
  • Use principal components analysis to reduce the number of predictors to a smaller number of “components” of correlated predictors
  • Use hierarchical clustering and k-means clustering to find and describe clusters of similar records
  • Use association rules to find patterns of “what goes with what” in transaction data
  • Combine unsupervised and supervised learning methods in a final project

Prerequisites

There are no prerequisites for this program.

Courses

The Predictive Analytics Mastery Series consists of three 4-week courses offered completely online at Statistics.com. Below find the required courses you will need to take. You will need to choose three courses based on whether you want to use XLMiner, a data-mining add-in for Excel, R or Python.

Predictive Analytics 1 – Machine Learning Tools

This course introduces the basic paradigm of predictive modeling: classification and prediction. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020, Jan 15, 2021

Predictive Analytics 1 – Machine Learning Tools with Python

This course introduces the basic paradigm for predictive modeling: classification and prediction. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020

Predictive Analytics 1 – Machine Learning Tools with R

This course introduces to the basic predictive modeling paradigm: classification and prediction. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020

Predictive Analytics 2 – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.
Topic: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 13, 2020, Jun 26, 2020, Oct 23, 2020

Predictive Analytics 2 – Neural Nets and Regression with Python

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: Mar 13, 2020, Jun 26, 2020, Oct 23, 2020

Predictive Analytics 2 – Neural Nets and Regression with R

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 13, 2020, Jun 26, 2020, Oct 23, 2020

Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

This course 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: Analytics, Prediction/Forecasting | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020

Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python

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.
Topic: Data Science, Analytics, Machine Learning, Prediction/Forecasting, Using Python | Skill: Introductory, Intermediate | Credit Options: CEU
Class Start Dates: May 15, 2020, Sep 11, 2020

Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R

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 15, 2020, Sep 11, 2020

What Our Students Say​

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Instructor gave good, understandable answers on the board, and several nice summaries of topics we were covering.

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Alisa Boehme
Statistical Analyst at Walmart Labs
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The professor was extremely helpful in the course and always willing to go above and beyond to answer questions.

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Angela Saquibal

Tuition and Fees

Save 10% on your total tuition for all required courses by choosing the “Pay-In-Advance” option for this Mastery Series. You can also purchase courses as you go along in your studies, but course fees are subject to change at any time should you choose not to lock-in your tuition through the Pay-In-Advance rate.

Plus a $65 Recording Fee.

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Course fees my change over term of your enrollment.

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Enroll With Pay-As-You-Go

Frequently Asked Questions

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 there a fee for a Record of Mastery Specialization?

Successfully complete the courses listed for Mastery Series and you’ll earn a Record of Specialization from the Institute for Statistics Education at Statistics.com.

To receive your Record of Mastery Series Specialization, there is a recording fee of $65.

Please see our knowledge center for more information.

How does the Mastery Series program work?

The Institute offers specializations in topics in several areas of focus. Successfully complete three of the courses listed for any specialization and you’ll earn a Record of Specialization from the Institute for Statistics Education at Statistics.com.

Please see our knowledge center for more information.

What types of courses are offered in Mastery Series?

Courses in the Mastery Series are standard Statistics.com courses – small classes, regular interaction with your instructor throughout the course, rapid expert feedback on your homework and projects.

Please see our knowledge center for more information.

What if I have already mastered a topic from prior experience?

If you have mastered a topic from prior experience, let us know and we’ll swap out that course for another.

Visit our knowledge base and learn more.

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Additional Information

Organization of Series

The Mastery Program consists of standard Statistics.com courses – small classes, regular interaction with your instructor throughout the course, rapid expert feedback on your homework and projects.  You can register for the courses individually, and even incorporate courses that you have recently successfully completed.

Courses takes place online at The Institute. 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 are 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 material relevant to the course, 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

There are 3 courses in this Mastery Series.

Each course is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing. You may build your own course calendar to meet your individual requirements. The Mastery Series program may be completed in less than six months to one year, depending on your personal schedule and course availability.

Course Fee & Information

Save 10% on your total tuition for all required courses by choosing the “Pay-In-Advance” option for this Mastery Series. You can also purchase courses as you go along in your studies, but course fees are subject to change at any time should you choose not to lock-in your tuition through the Pay-In-Advance rate.

Record of Mastery Documentation Fee
Upon Mastery completion, there is a $65 recording fee to receive your Record of Mastery documentation. 

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary (modest fee applies).

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.

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.

Group Rates
Contact us to get information on group rates.

Options for Credit and Recognition

Our Mastery Series credits options vary by program and courses offered. 

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

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

Certificate Program Credit
If you intend to enroll in a Certificate Program following completion of your Mastery Program, your course work may be assessed for a grade. In addition, your courses will be assessed for transferability to the Certificate Program course requirements. In most cases, if your Mastery Program courses are also included in the Certificate Program as either required courses or elective courses, your completed Mastery Program course work will be transferred to the Certificate Program.

CEU’s 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.

CAP-INFORMS
Courses in your Mastery Program that have been recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam will be will be issued a “Record of Course Completion” by The Institute upon your request, after successfully completing the course.

ACE CREDIT / College Credit
Courses in your Mastery Program that have been endorsed by the American Council on Education (ACE) will be issued a “Record of Course Completion” by The Institute upon your request, after you successfully complete the course. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Digital Badge

For courses in your Mastery Program that have been endorsed by the American Council on Education (ACE), a digital badge will be made available upon successful completion of the course.

Miscellaneous

Visit our knowledge base to learn more about our mastery services.

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