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silverrain

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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.
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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
Eigenvalues / Eigenvectors
Linear algebra text, Chapter E
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
EVALUATE RECOMMENDER SYSTEM
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Introduction to Neural Networks Example
An example of DS context presentation: https://docs.google.com/presentation/d/1RDhUBhbddIgeJFkbRfrHKsT1WTmOc8ysrw3gHel-N9w/edit?usp=sharing
INFERENCE FOR CATEGORICAL DATA
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Working with web apis
HTTP web apis httr jsonlite
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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
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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
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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
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R and SQL
Load data into a SQL database Create a .CSV file from a SQL Query Combine data from disparate structured data sources.
DATA 606 lab 0
R Data Types and Basic Operations
Subset Data Work with Vectorized Operations Create and Publish R Markdown documents