Data analytics curriculum for the classroom

Each module contains multiple lessons and was designed by faculty at University of Illinois, Gies College of Business. The course consists of 60 hours of curriculum broken up into eight modules that can be used on their own or integrated into existing curriculum:

• Module #1: Foundations (6 hrs, 30 min)

• Module #2: Introduction to Python Programming (8 hrs, 30 min)

• Module #3: Introduction to Data Analysis (6 hrs, 30 min)

• Module #4: Statistical Data Analysis (8 hrs, 30 min)

• Module #5: Introduction to Visualization (6 hrs, 30 min)

• Module #6: Introduction to Probability (7 hrs, 30 min)

• Module #7: Exploring Two-Dimensional Data (8 hrs, 30 min)

• Module #8: Introduction to Kernel Density Estimation (7 hrs, 30 min)

Students will learn basic tools and techniques that can scale to large computational systems and massive data sets. Topics include algorithms, overfitting and regularization, clustering, anomaly detection, and more:

• Module #1: Introduction to Machine Learning
• Module #2: Fundamental Algorithms

• Module #3: Practical Concepts in Machine Learning
• Module #4: Overfitting and Regularization
• Module #5: Fundamental Probabilistic Algorithms
• Module #6: Feature Engineering

• Module #7: Introduction to Clustering

• Module #8: Introduction to Anomaly Detection

Students will learn to develop data analytic scripts by using the Python programming language and the standard data analytic Python modules, including Pandas, NumPy, SciPy, Matplotlib, and Seaborn:

• Module #1: Foundations
• Module #2: Introduction to Python

• Module #3: Introduction to Python Programming
• Module #4: Python Programming

• Module #5: Introduction to Data Persistence
• Module #6: Introduction to Data Analysis

• Module #7: Introduction to Visualization

• Module #8: Exploring Two-Dimensional Data

Students will first learn how to explore and analyze multi-dimensional data sets, before learning how to obtain text data embedded within websites and how to analyze text data by using standard Python techniques and regular expressions:

• Module #1: Applied Data Analytics

• Module #2: Introduction to Text Analytics

• Module #3: Introduction to Data Persistence
• Module #4: Introduction to Python and Databases
• Module #5: Introduction to Probability

• Module #6: Introduction to Time Series Data
• Module #7: Introduction to Time Series Analysis
• Module #8: Introduction to Density Estimation

These TNPs are required to routinely report information on vehicles, drivers, and trips to the city of Chicago. This case uses Python to visualize the ridesharing trip patterns before and after COVID from 2019 to 2020.

Students will understand how histograms and plots of the cumulative distribution function can summarize customer behavior as well as create and interpret the graphs to provide insights for a business manager or executive without using technical jargon.

Students will play the role of a financial statement preparer and create a database for a fictitious company, including tables for customers, orders, and products. They will then retrieve and combine the data from these tables to calculate key accounting metrics such as revenue and cost of goods sold.