Analytics increasingly is a source of competitive advantage for businesses as well as a tool for improved effectiveness and efficiency. Leaders in all functional roles, including human resources/talent management, marketing, operations, product development, etc., from industries such as consumer packaged goods, logistics, healthcare, and more are looking to leverage data to create value.
Why are data and analytics important?
They are turning to quantitative analysis and strategies such as operations research, statistical modeling, predictive analytics, text mining, and associated IT tools to address real-life problems such as:
- What issues impact their customers’ buying decisions and which issues are just noise.
- What is the likelihood of success for a new product based on collected market data.
- How to identify specific customer markets using a a large data set from a survey.
- What are the statistically significant predictors for employee turnover, and how to streamline them into the three to four factors that can be used to create a retention strategy.
The online course begins with the basics of problem definition and data preparation, including ways to address missing values, outliers, and skewed distributions. Next, participants 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, principal 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.
Accreditations: Continuing Education Credits Eligible/.6 CEUs per course/3.0 CEUs per certificate
Showcase your new skills
In addition to earning a professional certificate upon completion of this program, you will also earn a digital badge to showcase your skills on platforms like LinkedIn. These badges show your network the concrete and in-demand skills you earned from taking this Kelley program.
- 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
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 certificate.
Upon completion of the program, students will receive a digital badge to share on their resume, LinkedIn profile, and other sites.
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.
Meet the instructor
John D. Hill
John D. Hill, PhD, MBA, is a Grant Thornton Scholar in the Department of Operations & Decision Technologies at the Kelley School of Business in Bloomington, Indiana. He is codirector of the Supply Chain and Digital Enterprise Academy and also codirector of the Digital Logistics and Transportation Workshop. His specialties include creating simulated environments, transportation safety and policy, and measurement of human performance.