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Title: GitHub - lgreski/datasciencectacontent: repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera

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Description: repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera - lgreski/datasciencectacontent

Open Graph Description: repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera - lgreski/datasciencectacontent

X Description: repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera - lgreski/datasciencectacontent

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https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#a-community-mentors-guide-to-the-johns-hopkins-university-data-science-specialization
Courserahttp://coursera.org
datasciencespecialization.github.iohttp://datasciencespecialization.github.io/
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#repository-contents
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-1-data-scientists-toolbox
Course Prerequisites and Difficulty Levelshttp://bit.ly/2e6y3nn
Configuring RStudio to work with git / github - Mac OSXhttp://bit.ly/2c9Q2bf
Configuring RStudio to work with git / github - Windows 7, 8, and 10http://bit.ly/2bPxOL5
Using Editor Modes in Discussion Forum Postshttp://bit.ly/2j4f1iX
Buying a Computer for Data Sciencehttp://bit.ly/2bHgwAY
R and RStudio on Chromebookhttp://bit.ly/2eQ8ipJ
Installing R and RStudio on Chromebookhttp://bit.ly/2tHLVOo
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#issue-students-struggle-to-find-urls-in-lecture-slides
Data Science Specialization Courseshttp://bit.ly/2bJcXNW
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#miscellaneous-articles-about-data-science
The Future of Data Analysishttp://bit.ly/2uC9atX
50 Years of Data Sciencehttp://bit.ly/2tKcaQu
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-2-r-programming
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#start-here
Resources for R Programminghttp://bit.ly/2dhZ8Dy
Data Science Specialization's github.iohttp://datasciencespecialization.github.io/
References for R Programminghttp://bit.ly/2b8AxhF
Data Science Specialization: what is the value?http://bit.ly/2j3EcCn
Swirl: common problems & getting helphttp://bit.ly/2vbIZX6
R versus Pythonhttp://bit.ly/2kleU76
Commercial Statistics Packages: An Historical Perspectivehttp://bit.ly/2fPj2qN
Configuring RStudio to work with git / github - Mac OSXhttp://bit.ly/2c9Q2bf
A Data Frame is Also a Listhttp://bit.ly/2fmMRAp
Forms of the Assignment Operatorhttp://bit.ly/2dtPhM9
Forms of the Extract Operatorhttp://bit.ly/2bzLYTL
S Objects, R Objects, and Lexical Scopinghttp://bit.ly/2dtOSXi
Thinking in R versus Thinking in SAShttp://bit.ly/2cH3u8x
Strategy for the Programming Assignmentshttp://bit.ly/2ddFh9A
Why is R More Difficult than SAS?http://bit.ly/2erxk3A
R Onboarding for SAS Usershttp://bit.ly/2dr7yum
References for R Programminghttp://bit.ly/2b8AxhF
Object Oriented Programming and Rhttp://bit.ly/2f5lzyh
Scoping in C/C++ vs. Rhttp://bit.ly/2l9tTAx
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#posts-regarding-specifics-of-programming-assignments
Assignment 1: Breaking Down Pollutantmeanhttp://bit.ly/2cHyiCl
Assignment 1: Breaking Down Completehttps://bit.ly/35wUnSO
Assignment 1: Breaking Down Corrhttps://bit.ly/2UwrfFe
Assignment 1: A SAS Version of Pollutantmeanhttp://bit.ly/2d3DR4e
Assignment 1: Common Mistakes - Weighted vs. Unweighted Meanshttp://bit.ly/2cA0fSh
Assignment 1: Common Mistakes - complete("specdata",332:1) failshttp://bit.ly/2uSLhui
Assignment 1: A More Elegant Solutionhttp://bit.ly/2kwBBlK
Assignment 2: Demystifying makeVectorhttp://bit.ly/2bTXXfq
Assignment 2: makeCacheMatrix as an Objecthttp://bit.ly/2byUe4e
Assignment 2: Using Github Desktophttp://bit.ly/2udIoFf
Assignment 2: Grading the SHA-1 Hash Codehttp://bit.ly/2iUWoB6
Assignment 3: Functions to Sort Data Frameshttp://bit.ly/2dxItzw
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#miscellaneous-code-examples-and-instructions
Permanently Setting R Working Directoryhttp://www.r-bloggers.com/setting-your-working-directory-permanently-in-r/
Tutorial: Downloading Fileshttp://bit.ly/2iP2suj
Creative Use of R: Downloading Course Lectureshttp://bit.ly/2bGlI7R
How to Upgrade R without Losing Your Packageshttp://bit.ly/2ejS8ox
Common R Mistakes: Overwriting R Functions with Output Variableshttp://bit.ly/2i3gmoA
R Programming Cheat Sheethttp://bit.ly/2rwY4CD
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#interesting-r-news-and-blog-articles
R vs. Python: 2016 Survey of Software used for Data Sciencehttp://bit.ly/2b827ey
What Big Data, Data Science, Deep Learning software goes togetherhttp://bit.ly/2bFBPmW
kdnuggets.comhttp://bit.ly/2b83GcK
R and Python vs. SAS and SPSShttp://bit.ly/2mJ3l6X
r-bloggers.comhttp://r-bloggers.com
Scaling R for Data Sciencehttp://oreil.ly/2b8pQyR
Lexical Scoping and Statistical Computinghttp://bit.ly/2cmqAPy
Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behindhttp://bit.ly/2oCHulX
Redmonk Programming Language Rankings: February 2020https://bit.ly/3ceFUM4
Githubhttps://github.com
Stackoverflowhttps://stackoverflow.com
IEEE 2018 Language Rankingshttp://bit.ly/2KKwgTa
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-3-getting-and-cleaning-data
Week 1: Demystifying HTML Parsing: Baltimore Ravens Game Scoreshttp://bit.ly/2gv12SS
Week 1: HTML Parsing - Gotta scrape 'em allhttps://bit.ly/3lXXPvD
Real World Example: Importance of Getting and Cleaning Datahttp://bit.ly/2xEBTga
Real World Example: Reading American Community Survey datahttp://bit.ly/2bAdLE9
Common Problems: Quiz 1 - Missing Java Runtimehttp://bit.ly/2jjtyXM
Strategy for Reading Files & APIs / Quiz 2http://bit.ly/2e4L5oF
Common Problems: Quiz 2 - sqldf() driver fails to connecthttp://bit.ly/2kD2KTY
Definitive Guide to Week 2 Question 5http://bit.ly/2wVyR6K
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#articles-and-content-related-to-getting-and-cleaning-data
Tidy Datahttp://bit.ly/2nyw5Ci
data.table Github Wikihttp://bit.ly/2og7rDK
Tutorial: Downloading Fileshttp://bit.ly/2iP2suj
Cheatsheet: R Syntax Comparisonhttps://bit.ly/3cxWabo
$ form of the extract operatorhttp://bit.ly/2bzLYTL
pipe operatorhttps://bit.ly/2Luu8BT
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-4-exploratory-data-analysis
Assignment 1: Reading a Subset of Raw Datahttp://bit.ly/2oAHoqI
CONCEPTS: Strategies for Imputing Missing Valueshttp://bit.ly/2w1b0FN
The Fundamentals of Data Visualizationhttps://bit.ly/35WjP2E
Data Visualization: a practical introductionhttps://bit.ly/2YSWL3A
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-5-reproducible-research
Assignment 2 Checklisthttp://bit.ly/2c6XFhQ
Configuring knitr to Retain Markdown Outputhttp://bit.ly/2nITVJH
Assignment 2: Improving Runtime Performance of Initial Data Loadhttp://bit.ly/2q0ieCO
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-6-statistical-inference
Reference Materials for Statistical Inferencehttp://bit.ly/2c50sKo
Using MathJax with Discussion Forums, R Markdown, and Github Pageshttp://bit.ly/2doF4OT
CONCEPTS: Calculating Area for a Point on the Normal Curvehttp://bit.ly/2hw5AMF
CONCEPTS: Variance of a Binomial Distributionhttp://bit.ly/2wA22yU
Poisson Confidence Interval Explainedhttp://bit.ly/2m8qQ8y
Power Calculations: Optimal Sample sizehttp://bit.ly/2i1RDGa
Permutation Tests Explainedhttp://bit.ly/2iV2HEx
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#articles-related-to-the-course-project
Exponential Distribution / Central Limit Theorem - Assignment Checklisthttp://bit.ly/2i5v81c
ToothGrowth Analysis - Assignment Checklisthttp://bit.ly/2iP39Ur
Exploratory Data Analysis in ToothGrowth Assignmenthttp://bit.ly/2iQEfBV
Accessing R Code from an Appendix in Knitrhttp://bit.ly/2i1I0XW
Theoretical Variance of Sampling Distribution of the Meanhttp://bit.ly/2iPa2oz
Kable Tables with Data Frameshttp://bit.ly/2j40Wlw
Installing MiKTeX on Windows 10 / Generating a PDF with knitrhttp://bit.ly/2iiGNbu
Commentary on Factorial Design in Toothgrowth Analysishttp://bit.ly/2eEVRhw
Knitr Referencehttp://bit.ly/2yOg4vv
Yihui Xehttp://bit.ly/2kh2tK3
knitrhttp://bit.ly/2g1m1xr
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-7-regression-models
Regression Models Referenceshttp://bit.ly/2IlDR9U
Why does sum of errors * X equal 0?http://bit.ly/2ipyoSN
Using MathJax with Discussion Forums, R Markdown, and Github Pageshttp://bit.ly/2doF4OT
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-8-practical-machine-learning
Week 3: Installing Rgtk2 and Rattle on OS Xhttp://bit.ly/2w13R8B
Week 3: Accessing files from ElemStatLearn packagehttps://bit.ly/3ewl6Rg
Week 4: Combining Predictors Math Explainedhttp://bit.ly/2i5z0Q4
Course Project - gh-pages Setup with RStudiohttp://bit.ly/2bTvGqr
Course Project - Improving Runtime Performance of Random Forest Models with caret::train()http://bit.ly/2bYtutG
Course Project - Predicting Test Scores based on Training Model Accuracyhttp://bit.ly/2i5A3zx
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-9-developing-data-products
Configuring shinyapps.io Application Timeouthttp://bit.ly/2dEq8O8
Mastering Shinyhttps://bit.ly/3bD6f6c
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#course-10-capstone
Speech and Language Processing, 3rd Editionhttps://web.stanford.edu/~jurafsky/slp3/
n-gram Computations and Computer Capacityhttp://bit.ly/2couvxh
Capstone Strategyhttp://bit.ly/2rGcgc6
Choosing a Text Analysis Packagehttp://bit.ly/2qagsPa
Common Problems: Running out of Memory Converting Document Feature Matrix to Regular Matrixhttp://bit.ly/2hZQf8b
https://patch-diff.githubusercontent.com/lgreski/datasciencectacontent#content-for-community-mentors
Tips for New Community Mentorshttp://bit.ly/2bZJniM
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