AI Academy

The Data Scientist’s Toolbox

Instructor: Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD Duration: 7 hours to complete
Objective 1 Set up R, R-Studio, Github and other useful tools
Objective 2 Understand the data, problems, and tools that data analysts use
Objective 3 Explain essential study design concepts
Objective 4 Create a Github repository
Rmarkdown
Git (Version Control System)
Development Environment
GitHub
Data Science
Big Data
R Programming
Version Control
Data analysis
Integrated Development Environments
Software Installation
Statistical Programming

R Programming

Instructor: Roger D. Peng, PhD , Jeff Leek, PhD , Brian Caffo, PhD Duration: Approx. 57 hours
Objective 1 Understand critical programming language concepts
Objective 2 Configure statistical programming software
Objective 3 Make use of R loop functions and debugging tools
Objective 4 Collect detailed information using R profiler
Computer Programming Tools
Data analysis
Debugging
Data Structures
Data Import/Export
Statistical Analysis
Performance Tuning
Program Development
Simulations
R Programming
Statistical Programming

Getting and Cleaning Data

Instructor: Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD Duration: 2 weeks to complete at 10 hours a week
Objective 1 Understand common data storage systems
Objective 2 Apply data cleaning basics to make data "tidy"
Objective 3 Use R for text and date manipulation
Objective 4 Obtain usable data from the web, APIs, and databases
Data Access
Data Import/Export
R Programming
Web Scraping
Application Programming Interface (API)
MySQL
Data Quality
Data Cleansing
Data Management
Data Collection
File Management
Exploratory Data Analysis
SQL
Data Wrangling
Data Integration
Data Manipulation
Data Transformation

Exploratory Data Analysis

Instructor: Roger D. Peng, PhD , Jeff Leek, PhD , Brian Caffo, PhD Duration: 7 hours to complete
Objective 1 Understand analytic graphics and the base plotting system in R
Objective 2 Use advanced graphing systems such as the Lattice system
Objective 3 Make graphical displays of very high dimensional data
Objective 4 Apply cluster analysis techniques to locate patterns in data
Ggplot2
Histogram
Unsupervised Learning
Color Theory
Exploratory Data Analysis
Plot (Graphics)
Data Visualization Software
Statistical Analysis
Scatter Plots
Data analysis
Box Plots
Data Visualization
R Programming
Dimensionality Reduction
Graphing

Reproducible Research

Instructor: Roger D. Peng, PhD , Jeff Leek, PhD , Brian Caffo, PhD Duration: 1 week to complete at 10 hours a week
Objective 1 Organize data analysis to help make it more reproducible
Objective 2 Write up a reproducible data analysis using knitr
Objective 3 Determine the reproducibility of analysis project
Objective 4 Publish reproducible web documents using Markdown
Technical Documentation
Verification And Validation
Statistical Reporting
Data Validation
R Programming
Knitr
Data analysis
Statistical Analysis
Rmarkdown
Data Sharing