Predictive Analytics 2 – Neural Nets and Regression with R

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.

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.

549
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Overview

In this course you, will continue work from Predictive Analytics 1 and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The course includes hands-on work with R, a free software environment for statistical computing. This course is especially useful for analysts and managers who want to undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Learning Outcomes

Upon completing this course students will be able to distinguish between profiling and prediction tasks for linear and logistic regression.  They will be able to specify and interpret linear and logistics regression models, use various analytical tools for prediction and classification, and preprocess text for text mining.

  • Distinguishing between profiling (explanation) tasks and prediction tasks for linear and logistic regression
  • Specifying and interpreting linear regression models to predict continuous outcomes
  • Specifying and interpreting logistic regression models for classification
  • Using discriminant analysis for classification
  • Using neural nets for prediction and classification
  • Preprocessing text for text mining, and using a predictive model with the resulting matrix

Who Should Take This Course

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters.  This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Instructors

mr-kuber-deokar

Mr. Kuber Deokar

Mr. Kuber Deokar holds a Masters degree in Statistics from University of Pune, India, where he also taught undergraduate statistics. Mr. Deokar holds the position of Instructional Operations Supervisor at Statistics.com. He is responsible for coordination of Statistics.com online courses, and ensures seamless interactions between the management team, course instructors, teaching assistants, and students. He also serves as the senior teaching assistant and shares instructional responsibilities for several courses, and handles consultancy assignments, working from our office in Pune, India.
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dr-inbal-yahav

Dr. Inbal Yahav

Dr. Inbal Yahav is a faculty member at the Graduate School of Business Administration, Bar-Ilan University, Israel.  Her research interests lie in the areas of statistical modeling and social media, with a focus on users' behavior in social networks, interactions and dynamics among users, and statistical modeling of heterogeneous behaviors.  Dr. Yahav's research to-date focuses on two domains. The first domain is cyber security and privacy, and in specific privacy unawareness and unintentional information leakage in social networks. The second domain is statistical modeling of sub-populations in big data. Dr. Yahav has presented her work at multiple Information Systems conf...
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Course Syllabus

Week 1

Linear and Logistic Regression

  • Review Predictive Analytics 1
  • Linear regression for descriptive modeling
    • Fitting the model Assessing the fit
    • Inference
  • Linear regression for predictive modeling
    • Choosing predictor variables
    • Generating predictions
    • Assessing predictive performance
  • Logistic regression for descriptive modeling
    • Odds and logit
    • Fitting the model
    • Interpreting output
  • Logistic regression for classification
    • Choosing predictor variables
    • Generating classifications and probabilities
    • Assessing classification performance

Week 2

Discriminant Analysis and Neural Nets

  • Discriminant analysis for classification
    • Statistical (Mahalanobis) distance
    • Linear classification functions
    • Generating classifications
  • Rare cases and asymmetric costsIntegrating class ratios and misclassification costs
    • Integrating class ratios and misclassification costs
  • Neural network structure
    • Input layer
    • Hidden layer
    • Output layer
  • Back propagation and iterative learning

Week 3

Text Mining

  • Representing text in a table
  • Term-document matrix
  • Bag of words
  • Preprocessing of text
    • Tokenization
    • Text reduction
    • Term Frequency - Inverse Document Frequency (TF-IDF)
    • Tokenization
  • Fitting a predictive model

Week 4

Additional Topics: Looking Ahead

  • Multiclass classification
  • Network analytics
  • Project

Class Dates

2020

Mar 13, 2020 to Apr 10, 2020

Jun 26, 2020 to Jul 24, 2020

Oct 23, 2020 to Nov 20, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

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

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

What Our Students Say​

The videos are excellent! The information is presented in a very clear and effective way.

Ciprian Alexandru Naghiu

Thank you so much Professors for this useful course. I very much liked XLMiner, it is indeed a very simple yet powerful tool

Nikhil Gupta
VP, Market Data Engineering at Goldman Sachs

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page 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: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

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 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

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.

In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.

Course Text

The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 1 – Machine Learning Tools – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R”.

Please order a copy of your course textbook prior to course start date.

Software

This is a hands-on course, and participants will apply data mining algorithms to real data.  The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

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.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Predictive Analytics 2 – Neural Nets and Regression with R
549
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