From 50f1cad5a71844e1a5ec033496715d6d249d5c67 Mon Sep 17 00:00:00 2001 From: Anupam Mishra <66557767+anupamishra333@users.noreply.github.com> Date: Tue, 28 Sep 2021 22:34:15 +0530 Subject: [PATCH] Update README.md --- 2-Working-With-Data/08-data-preparation/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/2-Working-With-Data/08-data-preparation/README.md b/2-Working-With-Data/08-data-preparation/README.md index f3a93b8a..d6a71ea9 100644 --- a/2-Working-With-Data/08-data-preparation/README.md +++ b/2-Working-With-Data/08-data-preparation/README.md @@ -4,9 +4,9 @@ |:---:| |Data Preparation - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -## Pre-Lecture Quiz +## [Pre-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/14) + -[Pre-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/14) Depending on its source, raw data may contain some inconsistencies that will cause challenges in analysis and modeling. In other words, this data can be categorized as “dirty” and will need to be cleaned up. This lesson focuses on techniques for cleaning and transforming the data to handle challenges of missing, inaccurate, or incomplete data. Topics covered in this lesson will utilize Python and the Pandas library and will be [demonstrated in the notebook](notebook.ipynb) within this directory. @@ -33,9 +33,9 @@ Depending on its source, raw data may contain some inconsistencies that will cau Give the exercises in the [notebook](4-Data-Science-Lifecycle\15-analyzing\notebook.ipynb) a try! -## Post-Lecture Quiz +## [Post-Lecture Quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/15) + -[Post-lecture quiz](https://red-water-0103e7a0f.azurestaticapps.net/quiz/15) ## Review & Self Study @@ -48,4 +48,4 @@ There are many ways to discover and approach preparing your data for analysis an ## Assignment -[Evaluating Data from a Form](assignment.md) \ No newline at end of file +[Evaluating Data from a Form](assignment.md)