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Logistic Classification & Multiple Linear Regression Models for Insurance Data
The objective is to build multiple linear regression and binary logistic regression models on the training data to predict the probability that a person will crash their car and also the amount of money it will cost if the person does crash their car.
Fundamentals of Computational Mathematics
Week 11, Regression 1
Fundamentals of Computational Mathematics
Week 10, Markov Chains / Random Walks
Fundamentals of Computational Mathematics
Week 10, Markov Chains / Random Walks
Fundamentals of Computational Mathematics
Week 9, CLT & Gen Fnct
Fundamentals of Computational Mathematics
Week 9, CLT & Gen Fnct
Fundamentals of Computational Mathematics
Week 8, Sums of RV
Fundamentals of Computational Mathematics
Week 7, Imp. Distributions / EX / VARX
Fundamentals of Computational Mathematics
Week 6, Combinatorics / Conditional Prob
Fundamentals of Computational Mathematics
Week 5, Probability Distributions
Fundamentals of Computational Mathematics
Week 5, Probability Distributions
Fundamentals of Computational Mathematics week 4
Week 4, Linear Transformations & Representations: 17-23 Sep
Fundamentals of Computational Mathematics week 4
Week 4, Linear Transformations & Representations
Knowledge and Visual Analytics Module 1
CUNY MSDS DATA 608, Fall 2018
Fundamentals of Computational Mathematics Week 1
Week 1, Vectors / Matrices / Systems of Equation: Aug 27-2 Sep
Introduction to Neural Networks Example
An example of DS context presentation:
https://docs.google.com/presentation/d/1RDhUBhbddIgeJFkbRfrHKsT1WTmOc8ysrw3gHel-N9w/edit?usp=sharing
Working with web apis
HTTP
web apis
httr
jsonlite
Web tech, HTML, XML, JSON, NOSQL and Getting data from web pages
HTML
XML
JSON
MongoDB and NoSQL databases
Getting data from web pages
rvest
SelectorGadget
Data transformation
Practice applying data tidying and data transformation operations across different datasets.
Use window functions
Working with tidy data
Transform data between wide and long formats using tidyr package
Change shapes of data frames using dplyr package
Perform data transformations to support downstream data analysis
Chess tournament cross-tables
Work with strings and dates in R
Use regular expressions
Understand how exploratory data analysis (EDA) informs data preparation work
Choose appropriate graphics for different combinations and types of variables
Use basic summary statistics
R Character Manipulation and Date Processing
Work with strings and dates in R
Use regular expressions
R and SQL
Load data into a SQL database
Create a .CSV file from a SQL Query
Combine data from disparate structured data sources.
R Data Types and Basic Operations
Subset Data
Work with Vectorized Operations
Create and Publish R Markdown documents