SYST/OR 568. Applied Predictive Analytics - Mason Analytics MS

Place and Time

• Fridays 4:30 pm - 7:10pm at Robinson Hall B124 (Jan 21, 2020 - May 13, 2020)

Grades

Grade composition: No in-class examination. Grade based entirely on participation in class, homework assignments, take-home midterm and final project.

Textbooks

• Diez, Barr and Cetinkaya-Rundel OpenIntro Statistics, OpenIntro, 2015

• James, Witten, Hastie and Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2009.

• Kuhn and Johnson, Applied Predictive Modeling, Springer, 2013.

• Hyndman and Athanasopoulos, Forecasting: Principles and Practice, OTexts, 2013.

Links

• Model Interpretability

• Data Sceince Survey

• J. M. Keynes's Investment Performance: A Note

• Bayes Search

• How Y'all (NY Times)

• Monte Hall

• EPL

• Data Cleaning

• Airbnb (Random Forest)

• Facebook (Decision trees and logistic regrsssion)

• Youtube (deep learning)

• Uber (time series)

• Exploratory Data Analysis

• Overfitting

• Election 2016

Deep Learning

• Course by Jeremy Howard on coding aspects of deep learning

• Short introduction into deep learning

• Architectures

• Keras

• ConvNetJS

• Opt Visualization

• Momentum

• Backprop

Final Project Data

• Debby Kermer (data services): contact info

• Grocery Shopping

• Walmart

• Yelp

• Airbnb

• Zillow

• Uber

• FiveThirtyErigh

• UCE ML Repo (Lots of datasets along with descriptions of each)

• Knuggets (Lot of links to datasets relevant for data mining)

• Pew Research Center Survey datasets

• ExonData (The site has links to plenty of regional, state, and local economic data)

• 3stages (Searchable listing 363 Internet sites of Social Science data)

• Bureau of labor statistics

• data.gov (A repository for information collected by the federal government)

• Medicare datasets

• Bureau of Transportation Statistics

• United Nations

• Chicago (Urban Analytics)

• NYC Taxi Trips

• Virginia

Very biased and noisy course recommendations

Here are the courses that cover different aspects of data science

• Statistical modeling (STAT250, SYST664, OR719, STAT 554)

• Data management (AIT614)

• Optimization (OR604)

Notebooks (ISLR labs)

• Introduction

• Regression

• Classification

• CV

• Model Selection

• Nonlinear Models

• Trees

• SVM

• PCA

• All

Previous instances

• Fall 2016

• Spring 2017