Graduate Students

Curriculum Vitae

Course (Algorithms For Data Science):

Data

Here we introduce several blogs related to data and data handling and also some resources of datasets.

Blogs:

How to Share Data with a Statistician by Jeff Leek
10 Statistics Tips (and Why You Should Use Them!) by Jeff Leek

Improve Your Data Literacy Skills and Make the Most of Data BY Geckoboard Company

Common	Data	Mistakes	to	Avoid
Cherry Picking	Data Dredging	Survivorship Bias	Cobra Effect	False Causality
Gerrymandering	Sampling Bias	Gambler’s Fallacy	Hawthorne Effect	Regression Toward the Mean
Simpson’s Paradox	McNamara Fallacy	Overfitting	Publication Bias	Danger of Summary Metrics

Datasets:

The following resources may be helpful for those still undecided about their course projects.

DataHub has a lot of structured data in formats such as RDF and CSV.
Awesome Public Datasets
UC Irvine Machine Learning Repository
CrowdFlower Data for Everyone library
Stanford Large Network Dataset Collection
Data Science Weekly
Awesome Data Science
Kaggle
Cafebazaar

To know more datasets, refer to the following webpage of KDnuggets:

Datasets for Data Mining and Data Science

Dealing with Data:

Data Preprocessing
5 Ways To Handle Missing Values In Machine Learning Datasets
Handling Missing Data
How to Handle Missing Data
7 Techniques to Handle Imbalanced Data
How to Handle Imbalanced Data: An Overview
Visualize Missing Data with VIM Package
Ultimate Guide to Handle Big Datasets for Machine Learning Using Dask (in Python)
Slide: Data Cleaning and Data Preprocessing by Nguyen Hung Son
Slide: Data Preprocessing by Taehyung Wang

Jupyter NoteBooks:

5-Day Challenge: Data Cleaning by Rachael Tatman
HousePrice - Data Cleaning & Visualization by Katsuhisa Kawaguchi