• Computer Science is more than just programming
• Statistics is more than just estimators and tests
• Mathematics is more than just proofs and abstraction
• Data Science is the interplay between computer science, statistics, and mathematics along with domain-specific knowledge to extract meaning and make predictions from data.

To support this, we use

• Active and collaborative explorations
• Real data
• Open ended projects

• Students are actively coding during class
• Students develop the algorithms organically
• Students work collaboratively to build ideas, find bugs, and ask questions
• Students are producing their own math/stats/cs

Task: Build an algorithm to separate the three species of iris that are shown in this image? (Audience Participation)

Step 1: randomly assign a species to every point

Step 2: find the centroids of the data

Step 3: reassign all species based on distance to centroids

Step 4: find centroids again and iterate

Step N: Clusters are revealed after several iterations.

• Develop a criterion for selecting variables in multiple regression
• Write code to demonstrate that a neural network can approximate any function
• Develop a way to classify univariate data like this:

Small / simulated data can be useful:

• Simple enough calculations to see what is happening
• Develop first intuition about material
• Creating simulated data with specific features is challenging

Small/simulated data is simultaneously ridiculous:

• Not realistic
• Can’t demonstrate the power of techniques
• Can’t demonstrate the challenge of real problems
• Real data often requires cleaning and visualization before anything else should be done
• Real data is often contains surprises

Pulse of the Nation Data: thepulseofthenation.com/

• Blunt random telephone surveys (be careful)
• Good for exploratory data analysis

Wisconsin Breast Cancer Diagnostic Data: archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

• Descriptive statistics of cells from fine needle apiriate
• Good for classification, clustering, and PCA

Wine Quality Data: archive.ics.uci.edu/ml/datasets/wine+quality

• Chemical compositions of wines and a taster's quality measurement
• Good for classification, variable selection, and PCA

Wine Quality Data: archive.ics.uci.edu/ml/datasets/wine+quality

• Chemical compositions of wines and a taster's quality measurement
• Good for classification, variable selection, and PCA

Web Hosted Data Sets:

• Carroll College's Active Statistics: http://mathquest.carroll.edu/activestats/
• University of California Irvine Machine Learning Archive: https://archive.ics.uci.edu/ml/datasets.html
• Kaggle: https://www.kaggle.com/
• Pulse of the Nation: https://thepulseofthenation.com/
• The World Health Organization: http://www.who.int/gho/database/en/

Textbooks:

• Introduction to Statistical Learning with R: https://www-bcf.usc.edu/~gareth/ISL/
• Modern Data Science with R: https://mdsr-book.github.io/

• Mock consulting scenarios
  • Technical report or presentation
  • Non-technical summary for a "client"
• Open ended
  • if there is one answer key then it isn't a good project
  • every student / group should come to different conclusions
• Students follow the entire data science cycle

• Build a classification scheme from the Wisconsin Breast Cancer data set and present your findings to a (ficticious) group of doctors.
• Build an interactive app that allows users to explore a data set related to Parkinsons disease. Your app needs to allow for EDA, classification, and clustering.
• Choose your own adventure final exam: Find a non-trivial data set and demonstrate what you've learned