# Statistical Computing and Intro to Data Science

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Prerequisites: STAT GU4204 and GU4205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, parallelizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

## Description

This is my notes for the course, Statistical Computing and Intro to Data Science, at Columbia University. After payment is received, user is able to download full sets of notes along with a zip file of course materials which include (1) textbooks, (2) slides, (3) R scripts, (4) Labs, and (5) Homework.

Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, parallelizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

Topics include:

– Introduction to R

– Exploratory Data Analysis

– EDA: Iris Dataset

– Bootstrap

– Bootstrap: Simple Linear Regression

– Bootstrap: Confidence Interval

– Gradient Descent

– K-Nearest Neighborhood (KNN)

– Logistic and Newton’s Method

– Project: Gross Metropolitan Product Per Capita (GMP) Analysis

– Project: Gradient Descent Algorithm

– Project: GDP Growth and Debt