Second Course in Foundations of Data Analytics
The Second Course in Foundations of Data Analytics will build a practical foundation for machine learning by teaching students 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
Each of eight modules consists of multiple lessons, which each contain a video explaining the lesson content, external reading(s), and included course Jupyter notebooks. Each module also includes a quiz (or assessment) that tests basic mastery of the lesson contents, and a programming assignment that tests synthesis of the lesson contents. Click here or complete the brief registration form to access the Second Course in Foundations of Data Analytics.
The Center is also releasing five mini-case studies, which are designed to build up students’ understanding of a management control system (MCS) and their data analytics skills in investigating MCS-related issues. These cases can be inserted directly into your data analytics curriculum and offer students the opportunity to leverage and interpret data in important and practical ways.
The five mini-case studies, which were created by Fei Du, assistant professor of accounting at the University of Illinois’ Gies College of Business, focus on the following areas:
1. Holding people accountable for results – “Results control”
2. Identifying, selecting, hiring, promoting the right employees – “Personnel control”
3. Promoting the right type of cultures and norms – “Cultural control”
Data Analytics Skills
1. Operationalizing a big control issue and disaggregating into specific data questions
2. Properly asking managers or employees to answer these specific questions
3. Designing a data structure to collect archival records to answer specific questions
4. Critically evaluating the meaning of the data pattern
5. Clearly communicating your results to decision makers in the organization
If you would like to access the mini-case studies, please complete the brief registration form. Access will be emailed to you soon after you complete the registration form. If you have additional questions or would like to provide feedback, email us.