AI Academy

The Data Scientist’s Toolbox

Instructor: Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD Duration: 1 week to complete at 10 hours a week
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: 2 weeks at 10 hours a week
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: 1 week 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: 7 hours to complete 3 weeks at 2 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

Statistical Inference

Instructor: Brian Caffo, PhD , Roger D. Peng, PhD , Jeff Leek, PhD Duration: 6 hours to complete
Objective 1 Understand the process of drawing conclusions about populations or scientific truths from data
Objective 2 Describe variability, distributions, limits, and confidence intervals
Objective 3 Use p-values, confidence intervals, and permutation tests
Objective 4 Make informed data analysis decisions
Probability
Data analysis
Statistical Methods
Statistical Inference
Probability & Statistics
Bayesian Statistics
Probability Distribution
Sampling (Statistics)
Statistical Modeling
Sample Size Determination
Statistical Analysis
Statistical Hypothesis Testing

Regression Models

Instructor: Brian Caffo, PhD , Roger D. Peng, PhD , Jeff Leek, PhD Duration: 2 weeks to complete at 10 hours a week
Objective 1 Use regression analysis, least squares and inference
Objective 2 Understand ANOVA and ANCOVA model cases
Objective 3 Investigate analysis of residuals and variability
Objective 4 Describe novel uses of regression models such as scatterplot smoothing
Regression Analysis
Predictive Modeling
Data Science
Statistical Analysis
Probability & Statistics
Statistical Modeling
Statistical Inference

Practical Machine Learning

Instructor: Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD Duration: 5 hours to complete
Objective 1 Use the basic components of building and applying prediction functions
Objective 2 Understand concepts such as training and tests sets, overfitting, and error rates
Objective 3 Describe machine learning methods such as regression or classification trees
Objective 4 Explain the complete process of building prediction functions
Regression Analysis
Decision Tree Learning
Classification And Regression Tree (CART)
Statistical Machine Learning
Feature Engineering
Data Collection
Random Forest Algorithm
Applied Machine Learning
Machine Learning Algorithms
Supervised Learning
Data Processing
Predictive Modeling
Machine Learning

Developing Data Products

Instructor: Brian Caffo, PhD , Jeff Leek, PhD , Roger D. Peng, PhD Duration: 10 hours to complete 3 weeks at 3 hours a week
Objective 1 Develop basic applications and interactive graphics using GoogleVis
Objective 2 Use Leaflet to create interactive annotated maps
Objective 3 Build an R Markdown presentation that includes a data visualization
Objective 4 Create a data product that tells a story to a mass audience
Web Applications
User Interface (UI)
Plotly
GitHub
Leaflet (Software)
Interactive Data Visualization
Data Presentation
Statistical Reporting
Data Visualization Software
Data Visualization
Rmarkdown
R Programming
Package and Software Management
Hypertext Markup Language (HTML)
Shiny (R Package)

Data Science Capstone

Instructor: Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD Duration: 7 hours to complete
Objective 1 Create a useful data product for the public
Objective 2 Apply your exploratory data analysis skills
Objective 3 Build an efficient and accurate prediction model
Objective 4 Produce a presentation deck to showcase your findings
Data Collection
Statistical Analysis
Data Manipulation
Data Science
Data Presentation
Predictive Modeling
Data analysis
Exploratory Data Analysis
Data Cleansing
Data Storytelling