Skip To Content
CADS Foundations of Data Science is a Course

CADS Foundations of Data Science

Time limit: 60 days

Full course description

Enter the world of data science. This eight-week course is designed for you to learn how data can be used to enhance business. Gain both quantitative literacy and in-demand skills to empower the work you do.

Use data to transform your business

Learn how to explore and analyze data using fundamental data science techniques and discover how data science can transform your business. This course follows the same structure as the first, so you can focus on mastering the content.

Get expert support

Even though this course is entirely self-paced, we've got your back. Learners will have the opportunity to ask questions virtually and receive feedback and help from the course facilitator.

Expand your opportunities

Data analytics continues to reshape all industries. Miami University's Center for Analytics and Data Science offers several opportunities to gain expertise in this exciting field. Earn the Analytics Institute Certificate by completing both Foundations of Statistics and Foundations of Data Science, or go deep into big data with Miami's Master of Science in Business Analytics.

Curriculum

In this 7-week course, you will learn about a variety of data science techniques and models; concepts covered include:

Analytics Maturity Curves

Supervised vs. Unsupervised Learning

Regression

Logistic Regression

Random Forest

Simulation

Forecasting

Clustering

Decision Trees

Optimization

Feature Engineering

A/B Testing

Upon completion, participants will be able to:

  • Develop an appreciation for using data to make business decisions
  • Explain the stages of the data science and analytics pipeline
  • Explain the main modeling purposes of data science, including classification, prediction, clustering, and forecasting
  • Understand when to use different machine learning algorithms
  • Interpret results/output of machine learning algorithms
  • Compare different machine learning algorithms