Predictive Analytics 1 – Machine Learning Tools with Python

Predictive Analytics 1 – Machine Learning Tools with Python

This course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.

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

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Overview

Test YourselfIn this course you will be introduced to basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. You will cover two core paradigms that account for most business applications of predictive modeling: classification and prediction. You will also study commonly used machine learning techniques and learn how to combine models to obtain optimal results. This course includes hands-on work with Python, a free software environment with statistical computing capabilities.
ACE + CAP Credit Eligible
ACE + CAP Credit Eligible
Expert Instructors
Teacher Assistant Support

Learning Outcomes

At the conclusion of this course you will be able to visualize and explore data, provide an assessment basis for predictive models, and choose appropriate performance measures. You will become familiar with common algorithms including k-nearest-neighbor, Naive Bayes, Classification and Regression Trees, as well as ensemble models.

  • 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 Python’s scikit-learn
  • Assess the performance of these models with holdout data
  • Apply predictive models to generate predictions for new data
  • Use Python’s sci-kit learn package to implement the models in the course

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. Peter Gedeck

Dr. Peter Gedeck

Peter Gedeck is at the forefront of the use of data science in drug discovery. He is a Senior Data Scientist at Collaborative Drug Discovery, which offers the pharmaceutical industry cloud-based software to manage the huge amount of data involved in the drug discovery process. Drug discovery involves the exploration and testing of huge numbers of molecule combinations, and much of that testing takes place analytically, hence the need for robust software to handle the data and provide a framework for analyzing it. Peter's specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Prior to this, he worked for twenty y...

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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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Course Syllabus

Week 1

Preparation

  • 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 models
    • Common metrics
  • K-Nearest-Neighbors (KNN)
    • Measuring distance
    • Choosing k
    • Generating classifications and predictions

Week 3

Bayesian Classifiers; 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

2021

No classes scheduled at this time.

2022

Jan 14, 2022 to Feb 11, 2022

May 13, 2022 to Jun 10, 2022

2023

No classes scheduled at this time.

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Prerequisites

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 should be familiar with Python, as covered in our Python for Analytics course.

Python for Analytics

Python for Analytics

This course will teach you the basic Python skills and data structures – how to load data from different sources and aggregate it, and how to analyze and visualize it to create high-quality products.
Topic: Analytics, Using Python | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 11, 2022, Jul 8, 2022, Nov 11, 2022

What Our Students Say

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
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

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

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Predictive Analytics 1 – Machine Learning Tools with R

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Predictive Analytics 2 – Neural Nets and Regression with Python

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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.
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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 Python, by Shmueli, Bruce, Gedeck, and Patel. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with Python” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with Python”

Software

The course includes hands-on work with Python, a free software environment with statistical computing capabilities.

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 college credit.  For recommendation details (level, and number of credits), please see this page. 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

Whatch 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.

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Predictive Analytics 1 – Machine Learning Tools with Python
$549 | Enroll Now
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