Curriculum

This Executive Certificate in Data Science combines 3.5 days on the historic Notre Dame campus with 5 weeks of online live sessions. This format allows participants to develop a deeper understanding of the machine learning and its applications.

The Data Science Process

The Data Science Process

In this opening session, participants will be exposed to the basic process of working with data. This session will discuss the challenges and ideas related to those steps and lay a foundation for the remainder of the workshop.
Introduction to R

Introduction to R

The R programming language is a cornerstone of modern data science. In this hands-on session, participants will explore the basics of R and the tidyverse.
Planning for Data Quality

Planning for Data Quality

Data quality is a critical consideration. Practical tips to avoid data quality problems through better planning for data collection and structure will be covered.
Advanced Data Visualization in R

Advanced Data Visualization in R

R and the ggplot2 package have powerful data visualization capabilities. This session will equip students to create a broad range of data visualizations that can be used to explore data.
Managing Big Data

Managing Big Data

This session discusses the issues of working with big data. Participants will discuss the basics of working with SQL, Spark, and Hadoop.
Ethics of Big Data

Ethics of Big Data

Using big data has the potential to have a broad impact. In this interactive session, students will examine a framework for evaluating the impact of using big data.
Simulation and Bootstrapping

Simulation and Bootstrapping

In this session, participants will examine tools that allow them to model and study random processes. These tools will be used to help participants understand machine learning in the data science process.
Simulation with Process Playground

Simulation with Process Playground

This session will introduce participants to the powerful discrete event simulation software.
Text Mining

Text Mining
From customer comments to process documentation, text data is commonplace. In this session, participants will see the basics of transforming freeform text into data for machine learning.
Evaluating to Machine Learning Models

Evaluating to Machine Learning Models

There are many possible machine learning algorithms that can be applied to big data. This session will give an overview of supervised and unsupervised machine learning and demonstrate methods of evaluating these algorithms.
Machine Learning for Classification

Machine Learning for Classification

This session will introduce programming machine learning algorithms for classification. Students will explore the fundamentals of the decision trees and k-nearest neighbors algorithms and discuss evaluating these algorithms.
Supervised Machine Learning for Classification

Supervised Machine Learning for Classification

Machine learning models have been found to be a great solution for classification problems. In this session, students will explore random forests, boosting and bagging along with k-nearest neighbors and other algorithms.
Unsupervised Learning

Unsupervised Learning

Machine learning is adept at finding hidden groups and relationships in data. This session allows participants to explore algorithms for unsupervised learning such as principal components analysis, hierarchical clustering, and k-means clustering.
AI and Neural Networks

AI and Neural Networks

Neural networks have broad applications including much of what we think of as Artificial Intelligence (AI). In this session, participants will see the basics of training and using neural networks.