Forked from swirl courses
This Statistics Bootcamp (SB) course is part of the Analytics and Intelligent Systems and Graduate Certificate in Business Analytics Practice series offered by NUS-ISS.
Co-Lecturer: GU Zhan (Sam)
This is a collection of interactive courses for use with the swirl R package. You'll find instructions for installing courses further down on this page. Some courses are still in development and we'd love to hear any suggestions you have as you work through them.
For more information regarding swirl, visit swirlstats.com or the swirl GitHub repository. If you'd like to write your own interactive content, please visit the Instructors page of our website.
Here are our current offerings, organized by level of difficulty:
- R Programming: The basics of programming in R
- Exploratory Data Analysis: Statistics and Data visualization in R
- Open Intro: A review to statistics, data analysis, and data visualization
Additional workshop - Mathematical Biostatistics Boot Camp: One- and two-sample t-tests, power, and sample size
Additional workshop - Regression Models: The basics of regression modeling in R
- Statistical Inference: This intermediate to advanced level course closely follows the Statistical Inference course of the Johns Hopkins Data Science Specialization on Coursera. It introduces the student to basic concepts of statistical inference including probability, hypothesis testing, confidence intervals and p-values. It concludes with an initiation to topics of particular relevance to big data, issues of multiple testing and resampling.
- Getting and Cleaning Data: dplyr, tidyr, lubridate, hardcore time consuming data pre-processing
Additional workshop
Since our users come from a variety backgrounds, it's very hard to label material as Fundamental, Intermediate, or Advanced. If you find something that is labelled Fundamental to be challenging, please don't be discouraged. The first step of learning anything is to acknowledge that you are capable of understanding it. True understanding will come with time and practice.
- Install R workshops
- [Fundamental] R Programming
R Programming 1: Basic Building Blocks
R Programming 2: Workspace and Files
R Programming 3: Sequences of Numbers
R Programming 4: Vectors
R Programming 5: Missing Values
R Programming 6: Subsetting Vectors
R Programming 7: Matrices and Data Frames
R Programming 8: Logic
R Programming 9: Functions
R Programming 10: lapply and sapply
R Programming 11: vapply and tapply
R Programming 12: Looking at Data
R Programming 13: Simulation
R Programming 14: Dates and Times
R Programming 15: Base Graphics
- [Fundamental] Exploratory Data Analysis
Exploratory Data Analysis 1: Principles of Analytic Graphs
Exploratory Data Analysis 2: Exploratory Graphs
Exploratory Data Analysis 3: Graphics Devices in R [PDF PNG SVG]
Exploratory Data Analysis 4: Plotting Systems
Exploratory Data Analysis 5: Base Plotting System
Exploratory Data Analysis 6: Lattice Plotting System
Exploratory Data Analysis 7: Working with Colors
Exploratory Data Analysis 8: GGPlot2 Part1 [qplot]
Exploratory Data Analysis 9: GGPlot2 Part2 [ggplot]
Exploratory Data Analysis 10: GGPlot2 Extras [qplot]
Exploratory Data Analysis 11: Hierarchical Clustering [Machine Learning]
Exploratory Data Analysis 12: K Means Clustering [Machine Learning]
Exploratory Data Analysis 13: Dimension Reduction [Machine Learning]
Exploratory Data Analysis 14: Clustering Example [Machine Learning]
Exploratory Data Analysis 15: CaseStudy [pm2.5 air pollution]
- [Intermediate] Open Intro
Open Intro 1: Overview of Statistics
- [Intermediate] Mathematical Biostatistics Boot Camp
Mathematical Biostatistics Boot Camp 1: One Sample t-test [nitty-gritty]
Mathematical Biostatistics Boot Camp 2: Two Sample t-test [nitty-gritty]
Mathematical Biostatistics Boot Camp 3: Errors Power and Sample Size
- [Intermediate] Regression Models
Regression Models 1: Introduction [regression to mean]
Regression Models 2: Residuals [var(data)=var(estmt)+var(resdls)]
Regression Models 3: Least Squares Estimation
Regression Models 4: Residual Variation [R^2=1-sRes/sTot=cor()^2]
Regression Models 5: Introduction to Multivariable Regression
Regression Models 6: MultiVar Examples
Regression Models 7: MultiVar Examples2
Regression Models 8: MultiVar Examples3 [MultipleLinearRegression]
Regression Models 9: Residuals Diagnostics and Variation
Regression Models 10: Variance Inflation Factors [VIF]
Regression Models 11: Overfitting and Underfitting [ANOVA, F-test]
Regression Models 12: Binary Outcomes
Regression Models 13: Count Outcomes
- [Advanced] Statistical Inference
Statistical Inference 1: Introduction
Statistical Inference 2: Probability1 [dice, playing cards]
Statistical Inference 3: Probability2 [PMF PDF CDF]
Statistical Inference 4: ConditionalProbability [medic test]
Statistical Inference 5: Expectations [E(X), CLT]
Statistical Inference 6: Variance [Var E(X^2)-(E(X))^2]
Statistical Inference 7: CommonDistros [Bernoulli Normal Poisson]
Statistical Inference 8: Asymptotics [central limit theorem, CI]
Statistical Inference 9: T Confidence Intervals [nitty-gritty]
Statistical Inference 10: Hypothesis Testing [t-test, z score]
Statistical Inference 11: P Values [nitty-gritty]
Statistical Inference 12: Power [false negative, Type II error]
Statistical Inference 13: Multiple Testing [confusion matrix]
Statistical Inference 14: Resampling [bootstrap]
- [Advanced] Getting and Cleaning Data
Getting and Cleaning Data 1: Manipulating Data with dplyr
Getting and Cleaning Data 2: Grouping and Chaining with dplyr
Getting and Cleaning Data 3: Tidying Data with tidyr
Getting and Cleaning Data 4: Dates and Times with lubridate
It automates the process by allowing you to do everything right from the R console.
- Make sure you have a recent version version of swirl:
install.packages("swirl")
- Enter the following from the R console to install:
library("swirl")
# Fundamental
install_course("R Programming E") # or: install_course("R Programming")
install_course("Exploratory Data Analysis")
# Intermediate
install_course("Regression Models")
install_course("The R Programming Environment") # Optional
# Advanced
install_course("Statistical Inference")
install_course("Getting and Cleaning Data")
install_course("Advanced R Programming") # Optional
swirl()
For example, install_course("R Programming") will install the R Programming course. Please note that course names are case sensitive!
If the automatic course installation method outlined above does not work for you, then there's a simple alternative.
-
Click here to download the file
S-SB-Workshop-master.zip. -
Install all all available courses:
library("swirl")
install_course_zip("~/Downloads/S-SB-Workshop-master.zip", multi=TRUE)
swirl()
Or, to install one course at a time, enter the following from the R console, substituting the correct file path to your downloaded file and the name of your desired course:
install_course_zip("path/to/file/here/S-SB-Workshop-master.zip", multi=TRUE,
which_course="Course Name Here")
For example, if you download the zip file to ~/Downloads/S-SB-Workshop-master.zip, then the following command will install the R Programming course.
install_course_zip("~/Downloads/S-SB-Workshop-master.zip", multi=TRUE, which_course="R Programming")
Please note that course names are case sensitive!
If you'd like to remove a course at any time, you can use uninstall_course("Course Name Here").
If you'd like to remove all courses at any time, you can use uninstall_all_courses(force = FALSE).
If you'd like to delete a user's workshop progress, you can use delete_progress("User Name Here").
Instructors around the world are using swirl in their classrooms. We think this is awesome. If you're an instructor, please feel free to do the same -- free of charge. While your students may be paying to take your course or attend your institution, we simply ask that you don't charge people directly for the use of our software or instructional content.
If you are not sure about a particular use case, don't hesitate to send us an email at info@swirlstats.com.
Data Science Wars: R versus Python:
https://www.datasciencecentral.com/profiles/blogs/data-science-wars-r-versus-python
Data Science Road Map created by Swami Chandrasekaran:
http://nirvacana.com/thoughts/wp-content/uploads/2018/01/RoadToDataScientist1.png
