Introduction to the iCademy

The Business Analytics iCademy has a variety of competencies that provide foundational and advanced resources to help ease the burden that you face when learning about data analytics. Within each competency, skills are organized based on the analytic platform that you want to learn, such as Excel, R, or Python.

While data analytics is an exciting topic, it can also be overwhelming. The skills in each competency are designed so that you can complete them in a relatively short amount of time and at your own pace, and when you’re ready to do so.

Competencies and What You'll Learn

Assembling Data

What makes data useful for decision making? You’ll learn about data formats, and the importance of giving structure to data so that it can be analyzed.


Learn foundational principles for visualizing data and how to build a wide variety of visualizations using popular data visualization software.

Getting Started with a Data Analytic Language

Become familiar with a coding environment. Get started using written commands to prepare data, analyze it, and share the results.

Machine Learning

Learn some basic analyses and take advantage of powerful machine learning techniques like time series analysis, random forests, and text analysis for extracting insights.

Chart Your Learning

Although you can complete the Skills in any order, Gies recommends the following Learning Path: Data and Its Properties; Data Visualization in Excel; Data Visualization in Tableau; Regression in Excel; Advanced Regression Topics in Excel; Introduction to Automation in Excel; Getting Started in R; Assembling Data with R; Tidying Data in R; Exploratory Data Visualization in R

Once you've completed the Skills above, you can move on to either (or both) of the Learning Paths below: 

     1) Forecasting in R; Times Series in R Part 1; Times Series in R Part 2; Algorithmic Trading in R 

     2) A/B Testing and ANOVA in R; Logistic Regression in R; Customer Satisfaction and Scaling in R; Conjoint Analysis in R