Predictive Analytics 1 – Machine Learning Tools

Predictive Analytics 1 – Machine Learning Tools

This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

This course introduces the basic paradigm of predictive modeling: classification and prediction.

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Overview

In this course, you will be introduced to the basic concepts in predictive analytics, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. This course is useful for marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters and professionals who want to understand what predictive modeling might do for their organization, undertake pilots with minimum setup costs, or manage predictive modeling projects or ongoing predictive modeling deployments.

Consider our trial course, Predictive Analytics Preview. This is a one-week review of the Predictive Analytics 1 – Machine Learning Tools course and introduces the basic concepts in predictive analytics as the most prevalent form of data mining.

We also offer Predictive Analytics using R or Python.

Learning Outcomes

Students will learn how to explore and vizualize the data, to get a preliminary idea of what variables are important, and how they relate to one another. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. Then you will learn how to combine different models to obtain results that are better than any of the individual models produce on their own. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model).

  • Visualize and explore data to better understand relationships among variables
  • Organize the predictive modeling task and data flow
  • Develop machine learning models with the KNN, Naive Bayes and CART algorithms using Excel tools
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Partition data to provide an assessment basis for predictive models
  • Choose and implement appropriate performance measures for predictive models
  • Specify and implement models with the following algorithms:
    • k-nearest-neighbor
    • Naive Bayes
    • Classification and Regression Trees
  • Understand how ensemble models improve predictions

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

dr-anthony-babinec

Mr. Anthony Babinec

Anthony Babinec is the President of AB Analytics. For over two decades, Tony Babinec has specialized in the application of statistical and data mining methods to the solution of business problems. Before forming AB Analytics, Babinec was Director of Advanced Products Marketing at SPSS; he worked on the marketing of Clementine and introduced CHAID, neural nets and other advanced technologies to SPSS users. He is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has held various offices including President.
See Instructor Bio
dr-galit-shmueli

Dr. Galit Shmueli

Dr. Galit Shmueli is a Distinguished Professor of the Institute of Service Science, College of Technology Management at National Tsing Hua University, Taiwan.  Previous academic appointments include the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad, and Associate Professor of Statistics in the Department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland.  Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature.
See Instructor Bio

Course Syllabus

Week 1

Preparation

  • Required text
  • What is supervised learning
  • Data partitioning and holdout samples
  • Choosing variables (features)
  • Handling missing data
  • Visualization and exploration

Week 2

Classification and Prediction

  • Assessing classification models
    • Confusion matrix
    • Misclassification costs
    • Lift
  • Assessing prediction modelsCommon metrics
    • Common metrics
  • K-Nearest-Neighbors (KNN)
    • Measuring distance
    • Choosing k
    • Generating classifications and predictions

Week 3

Bayesian Classifiers and CART

  • Full Bayes classifier
  • Naive Bayes classifier
  • Classification and Regression Trees (CART)
    • Growing the tree
    • Avoiding overfit - pruning
    • Using trees for classifications and predictions

Week 4

Ensembles

  • Combine multiple algorithms
  • Improve results

Class Dates

2020

Jan 10, 2020 to Feb 7, 2020

May 15, 2020 to Jun 12, 2020

Sep 11, 2020 to Oct 9, 2020

2021

Jan 15, 2021 to Feb 12, 2021

2022

No classes scheduled at this time.

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Prerequisites

Required text and other info here.

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

You will benefit from some familiarity with regression, which is covered in Statistics 2 – Inference and Association.

 

 

Statistics 2 – Inference and Association

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CEU
Class Start Dates: Feb 7, 2020

What Our Students Say​

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

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

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: 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 1 – Machine Learning Tools with Python

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 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.
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 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

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

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project. Note: There will be a mid-week discussion exercise in the first week of the course.

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 Microsoft Office Excel with XLMiner, 3rd Edition, by Shmueli, Patel and Bruce.

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. 

This course uses Analytic Solver Data Mining (previously called XLMiner), a data-mining add-in for Excel. We also offer a course using R or Python.

Course participants will receive a license for Analytic Solver Data Mining (previously XLMiner) for nominal cost – this is a special version, for this course.  

IMPORTANT:  Do NOT download the free trial version available at solver.com.

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

Take a 10-question quiz on analytics: Test Yourself

Watch our preview of this course:

 

Watch this video by Dr. Shmueli on “Data Mining in a Nutshell”.

Miscellaneous

There is no additional information for this course.

Register for This Course​

Predictive Analytics 1 – Machine Learning Tools
549
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