Predictive Analytics for Business Applications Digital Badge

Kelley School of Business

Kelley School of Business

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About the course

Fundamentals and advanced skills in data analytics

Analytics has become more important over the past few years as a source of competitive advantage for businesses as well as a tool for improved effectiveness and efficiency. Organizations around the world are looking at ways to leverage data to create value. They are turning to quantitative analysis and strategies such operations research, statistical modeling, predictive analytics, text mining, and associated IT tools.

The Kelley School of Business Executive Education Digital Badge Program introduces students to a variety of analytics techniques and software tools. The online course begins with the basics of problem definition and data preparation, including ways to address missing values, outliers, and skewed distributions. Next, students will learn to identify appropriate analytical techniques based on data structure and problem definition. Techniques to be studied include linear regression, logistic regression, discriminant analysis, principle component analysis, cluster analysis, and neural networks. These will be covered via a combination of video content and live, interactive class time with a variety of software including Excel and R.

Using data analytics skills learned in the course, students will be able to address real-life problems such as:

  • Figure out what issues impact your customers’ buying decisions and which issues are just noise.
  • Be able to predict the likelihood of success for a new product based on collected market data.
  • Using a large data set from a recent survey to figure out if there is a way to group respondents to identify specific customer markets.
  • Identifying all the statistically significant predictors for employee turnover, and streamlining them into the three to four factors that can be used to create a strategy for retention without ignoring any of the other important variables.

Learning Outcomes

At the conclusion of the course, students will be able to construct, validate, and interpret data mining and predictive analytics models using large data sets, and apply these techniques to marketing, finance, and operations problems. Students will complete weekly quizzes to demonstrate mastery of the subject matter and to qualify for the badge.

Upon completion of the program, students will receive a digital badge to share on their resume, LinkedIn profile, and other sites.

Predictive Analytics for Business Applications Digital Badge Overview

This program will consist of 10 webinars and will include both practical exercises and quizzes. After successful completion of the requirements listed below, students will be eligible to receive a digital badge signifying participation and achievement.

Outline of topics

  • Introduction to Data and Predictive Analytics
  • Introduction to R and R Studio
  • Prepping Data for Analysis
  • Data Visualization
  • Linear Regression Models
  • Logistic Regression Models
  • PCA, Factor Analysis, and Cluster Analysis
  • Neural Networks and K-nearest Neighbors

Experts

John Hill

Biography In 2007 I attained a BSc. (first class) in Economics at the University of Sheffield. During the degree I became interested in health economics and applied econometrics. I obtained an ESRC 1+3 award in 2008 which provided funding for an MSc. in Economics and Health Economics (distinction...

Predictive Analytics for Business Applications Digital Badge at Kelley School of Business

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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