Giter VIP home page Giter VIP logo

columbiax-statistical-thinking-for-data-science's Introduction

Learning activities for ColumbiaX series on Data Science and Analytics

###Course I: Statistical Thinking for Data Science and Analytics

Week 2

Excel sheet for the Buffon's needle learning activity.

Week 3

The folder R Learning activities contains some R learning activities we created for week 3 of our course I "Statistical Thinking for Data Science and Analytics" for the ColumbiaX series on Data Science and Analytics on edX.

Before you start
Learning activities

These learning activities are given during week 3 of our edX course. All these RMarkdown files can be run after installing R, Rstudio, and the knitr package. We use data sets that come with standard R installation. These RMarkdown (rmd) files are intended to be downloaded locally and run on your personal computer. Download these files to your computer and save them in a designated folder (such as one created for this course). To explore the R codes, in Rstudio, use the file menu to open the saved RMarkdown file and click on "knit html".

Week 4

  • Learning activity 4: visualize the geographical distribution of participants of our edX course. This exercise is contributed by our course assistant Bob Minnich.

Week 5

The folder R Learning activities contains another R learning activity we created for week 5 of our course I "Statistical Thinking for Data Science and Analytics" for the ColumbiaX series on Data Science and Analytics on edX.

columbiax-statistical-thinking-for-data-science's People

Contributors

akin-aroge avatar cayetanobv avatar matt32106 avatar minnich49 avatar peteboucher avatar tz33cu avatar yuting27 avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

columbiax-statistical-thinking-for-data-science's Issues

Error running stan

Hi , I am doing the current edX course and am trying to compete this exercise.

My apologies, I am a relative newbie to R, so may be a basic R issue.

When I follow the code, I get the following error when I try to call stan:

define BOOST_NO_CXX11_RVALUE_REFERENCES

      ^

:6:9: note: previous definition is here
#define BOOST_NO_CXX11_RVALUE_REFERENCES 1
^
1 warning generated.
Error in .Call(Module__functions_names, xp) :
negative length vectors are not allowed

the 'fit' variable is not defined.

Any suggestions ?

Additonal info: I am running RSTAN on OS X 10.12.4, have just update Xcode.

Error While Knitting to HTML

I get the below error while knitting the "LearningActivity-1.Rmd" file to HTML.

File /manifest.json not found in resource path Error: pandoc document conversion failed with error 99 Execution halted

How could this be fixed?

Error knitting

Here's the error I'm getting:

pandoc: /opensearch.xml: openBinaryFile: does not exist (No such file or directory)
Error: pandoc document conversion failed with error 1
Execution halted

Question on LearningActivity-BDA.Rmd

Hi, I'm new to Baysian model and trying to understand this learning activity.

Under "simulate renewal behaviour and prepare the data":

@tz33cu, Can you please give some idea on How did you derive the values for the beta below?

beta0 <- 0.6 beta1 <- 0.9 beta2 <- 0.6 beta3 <- 0.01 beta4 <- -0.01 beta5 <- -0.2

I'm confused, can you please help?

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.