We continue Monday’s discussion of “people analytics’ with a look from the customer’s side and a call for all thinkers! (see below)

Our course spotlight is on:

Sep 6 – Oct 4: Predictive Analytics 1 – Machine Learning Tools
Sep 6 – Oct 4: Programming 1 (R or Python)

See you in class!

– Peter Bruce,
Chief Academic Officer, Author, Instructor, and Founder
The Institute for Statistics Education at Statistics.com

Industry Spotlight

Delivering customer feedback

Companies need CFM to understand the VOC and improve the UX. CFM is “customer feedback management,” VOC is “voice of the customer,” and UX is user experience. When acronyms proliferate, that’s a sure sign of an active commercial market […]

QUOTE(S) FOR THE DAY

“The best time to design an experiment is after you have done it.” R. A. Fisher (attributed by George Box)

This refers to the fact that you will find out about the design flaws in your experiment once you start looking at the results. Two other related quotes are:

“The world’s best statistical analysis cannot rescue a poorly planned experimental program.” (Gerry Hahn, GE)

“Data from a well-designed experiment will practically analyze themselves.” (Thomas P. Ryan)

Small Ball

Calling all thinkers

I was visiting New York a couple of weeks ago, transferring from Amtrak to the PATH trains at Newark. PATH takes you to Wall Street – the #1 financial center in the world – and yet the process of paying for my $2.75 PATH ticket was excruciating. When I arrived at Newark, my colleague, who had arrived 30 minutes earlier on a different train, was still in line at a PATH farecard machine. Credit card transactions were taking minutes before being mysteriously denied. The most efficient means of buying my ticket to the world’s financial center? […]

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Course Spotlight

Predictive Analytics 1 – Machine Learning Tools starts Sep 6 – Oct 4

Designed and taught by the author team for the best selling text Data Mining for Business Analytics. You will learn how to:

Visualize and explore data to better understand relationships among variables
Partition data to provide an assessment basis for predictive models
Specify, implement and assess models with the following algorithms:
k-nearest-neighbor
Naive Bayes
Classification and Regression Trees
Understand how ensemble models improve predictions

Programming 1: using R or Python starts Sep 6 – Oct 4

Our introductory courses for R and Python programming are designed and taught by experienced Institute instructors and authors. You will learn:

How to read/write data
How to use functions
How to deal with dates
How to write loops
How to leverage the power of packages contributed to these open-source languages

These courses are excellent entry points into our data science certificate programs:

Analytics for Data Science (focuses on off the shelf tools like Excel and Tableau)
Programming for Data Science (tracks for R and Python, novice and experienced programmers)

You can take one of the entry point courses before enrolling in the certificate, and apply it retroactively.

See you in class!

Contact Us To Register or Learn More

If you have any questions on our courses, certificates, and degree programs and how they can apply to you, your work, and to your career, please get in touch. We’re here to help you succeed.