summary presentation slides for final report for Data622. We explored dimensionality reduction for the Fashion-MNIST dataset

my PCA dimensionality reduction of Fashion MNIST. Section for DATA622 final report, a group project

Mental Health Survey

Group homework assignment

Principles of Data Visualization and Introduction to ggplot2

another loess smoothing application. smoothed responses on LGN neurons to stimuli with different wavelengths of light

a loess smoothing application. smoothed response histograms to different orientations are visualized to demonstrate tuning properties for V1 neurons

using linear regression to demonstrate a physiological difference between retinal gagnlion cell classes using a classic data set

Notes from Google UX Design: Course 3

Modelling the time course of dark adaptation with a classic psychophysics dataset.

autoregression, OLS (lm) and GLS methods in R

Notes from Google UX Design: Course 2

Notes from Google UX Design: Course 1

Linear Relationships in Psychophysics and how to Model them with `R` in the tidyverse

Notes from videos and exercises for DataCamp's Shiny Fundamentals with R. This is a casual follow along to help me remember all the things and to serve as future reference.

notes from readings in ‘Extending the Linear Model with R’ by Julian Faraway for the course DATA621, ‘Business Analystics and Data Mining’ as part of the Masters of Science in Data Science program at CUNY SPS.

an quick introduction to cost curves as an alternative to ROC curves. for data621 discussion.

notes for applied predictive modeling

a short discussion post to facilitate dealing with missing values, imputation and understanding why imputation with regression is better than to the mean

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Group miniproject for Business Analytics & Data Mining class.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

a short discussion post to demonstrate Bonferroni Correction

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

using simple sample code from LMR section 4.3 (Autoregression) to capture the weekly variation in US COVID deaths

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

useful table with reading & suggested problem assignments for DATA621: Business Analystics and Data Mining

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes and some finished problems fro 'A Modern Approach to Regression with R' by Simon Sheather

Notes and some finished problems fro 'Linear Model with R' by Julian Faraway

presentation to accompany prosem lecture w/ jupyter lab notebook

pre to accompany prosem lecture w/ jupyter lab notebook

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Notes from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Note from videos and exercises for DataCamp's Statistics with R track. This is a casual follow along to help me remember all the things and to serve as future reference.

Probability problem set

kaggle housing dataset

Taylor Series Expansions

Partial DiffyQs

Taylor Series Expansion

Simple calculus: find area bounded by two functions

messin around with some calculus problems

linear multiple regression to estimate fine particulates by pollutants concentrations and meteorological data.

linear regressions homework tasks with WHO data

uses the dataset: "Estimation of obesity levels based on eating habits and physical condition Data Set" to find eating habits and other parameters predictive of BMI

simply simple linear regression + diagnostics

just a simple linear regression using the lego set datast from kaggle

Absorbing Markov Chain

Markov Chain

central limit theorem etc.

9.3.2

Chebyshev's Inequality 8.2.10

conditional and joint probabilities

Combinatronics & Probabilities

Introduction to Probability: Continuous Density Functions exercise

Uniformly distributed random numbers

Discussion Week4: Compute the matrix representation of T relative to the bases B and C

Inverse of a Matrix & Single Value Decomposition

Discussion Week3 for Eigenvalues & Eigenvetors

Eigenvalues & Eigenvectors

Trace, Determinant & Factorization

Vectors, Matrices & Systems of Equations

A simple python simulation of a game of chance

The FEMA Disaster Declaration Summary dataset holds a record for every declaration of natural emergency since the early 1950. This analysis tours the dataset and models the duration of declaration over the years.

Slides for project presentation

a look at COVID-19 deaths by county and comparison with mortality rates for other leading causes of death in the US

my extension of Nilsa Bermudez' assignment. Part of a group project to practice version control with group collaboration. Here, I just added some text and a brief chunk of code to use tidyr & dplyr methods

A brief overview and UX glance at MovieLens

follows chapter2: Sentiment Analysis with Tidy Data in Text Mining with R using the My Little Pony transcript data set from kaggle.com. Here the nlp R libraries tidytext and syuzhet are used to weight positive and negative sentiments with respect to narrative duration and character.

Tidyverse vignette to demonstrate purrr list filtering function: pluck(), compact(), keep() & more

Project proposal and preliminary descriptive stats for an analysis of turn-around time duration for different classifications of disasters.

R code to directly access information from the New York Times Books API, read in the JSON data to an R DataFrame, 'tidy' the data with tidyverse methods and visualize the results

fork a github repository, commit contributions & send a pull request

Some vary basic web scraping of reddit comments using the redditExtractoR library and text mining with the 'tm' library to create a wordcloud of most frequent words.

This demo will focus on reading and writing web data formats (.html, .xml & .json) in R. However, we will start by scraping data on several books from the MIT Press website.

This demo walks through reading & writing a .csv file before 'tidying' a dataset using methods from dplyr and tidyr libraries.

Video Game Sales 2019 Dataset from Kaggle is tidied w/ dplyr & tidyr. ggplot is used to visualize the data

The UN International Migration Dataset is tidied w/ dplyr & tidyr. circlize is then used to visualize the data as a chord diagram

beginning with a small but really quite messy .csv file, use methods from tidyr & dplyr to clean the data and then perform some brief analysis & visualizations

Given a text file with chess tournament results where the information has some structure, this R Markdown file generates a .CSV file that summarizes information from each player.

working with character strings and dates in R.

loading information from a SQL database into an R dataframe

Loading Data Into a Dataframe

Introduction to R and RStudio for DATA606 Spring Semester 2020

Demonstrating a Poisson distribution with a classic data set: the von Bortkeiwicz Prussian horse-kicking data

This R markdown document completes the homework assignment for week 2 of the R Bridge tutorial for the MSDS2020 Winter Bridge program

This R markdown document completes the homework assignment for week 1 of the R Bridge tutorial for the MSDS2020 Winter Bridge program.