Data Science with R: Data Analysis and Visualization

This is a paid 5-week course offered by NYC Data Science Academy (https://nycdatascience.com/). Learn more and enroll on our website: https://nycdatascience.com/courses/data-science-with-r-data-analysis/

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Overview
This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

Time and Date: July 29, 2017 - August 26, 2017 | 10:00am-5:00pm Weekends

Day 1: July 29, 2017

Day 2: August 5, 2017

Day 3: August 12, 2017

Day 4: August 19, 2017

Day 5: August 26, 2017

Instructor

David Romoff is a risk management consultant with 10 years of experience modeling market and credit risk using the latest methods and technologies. David's recent work includes serving as Manager of Risk Management at On Deck Capital, a business lending company in the FinTech space that uses machine learning models to underwrite loans. David was responsible for estimating and reporting losses on the book of loans. Previously, David worked in Enterprise Risk Management at AIG for five years where he designed and supported models on insurance risk, credit risk, and capital allocation. Before AIG, he worked at Bear Stearns in counterparty credit risk. David has an MBA from the Zicklin School of Business in New York City and a Master of Science in Actuarial Science from Columbia University. His undergraduate degree is from the State University of New York at Albany, where he studied psychology and philosophy.

Prerequisites
Basic knowledge about computer components
Basic knowledge about programming

Syllabus
Unit 1: Basic Programming with R

Introduction to R
What is R?
Why R?
How to learn R
RStudio, packages, and the workspace
Basic R language elements
Data object types
Local data import/export
Introducing functions and control statements
In-depth study of data objects
Functions
Functional Programming

Unit 2: Basic Data Elements

Data transformation
Reshape
Split
Combine
Character manipulation
String manipulation
Dates and timestamps
Web data capture
API data sources
Connecting to an external database

Unit 3: Manipulating Data with “dplyr”

Subset, transform, and reorder datasets
Join datasets
Groupwise operations on datasets

Unit 4: Data Graphics and Data Visualization

Core ideas of data graphics and data visualization
R graphics engines
Base
Grid
Lattice
ggplot2
Big data graphics with ggplot2

Unit 5: Advanced Visualization

Customized graphics with ggplot2
Titles
Coordinate systems
Scales
Themes
Axis labels
Legends
Other plotting cases
Violin Plots
Pie charts
Mosaic plots
Hierarchical tree diagrams
scatter plots with multidimensional data
Time-series visualizations
Maps
R and interactive visualizations

Final Project

After 35 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.