mikekiwa,github

d3-js-step-by-step

http://zeroviscosity.com/category/d3-js-step-by-step

d6tstack

Quickly ingest messy CSV and XLS files. Export to clean pandas, SQL, parquet

dat8

General Assembly's 2015 Data Science course in Washington, DC

data

Some common datasets with headers added and properly setup for Pandas/others

data-1

Data and code behind the articles and graphics at FiveThirtyEight

data-analysis

Analyse Los Angeles Crime rate between 2012 - 2016 using python pandas, numpy and matplot libraries

data-analysis-and-machine-learning-projects

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

data-analysis-on-hospital-readmission-data

Analysis on hospital readmission data for diabetic patients using R and Tableau

data-analysis-using-python

Based on the LinkedIn course "Python Data Analysis" by Michele Vallisneri

The objective of this project is to collect a dataset from one or more open web APIs of your choice, and use Python to pre-process and analyse the collected data. 1. Choose one or more open web APIs as your source of data. If you decide to use more than one API, the APIs should be related in some way. 2. Collect data from your chosen API(s) using Python. Your dataset should contain at least 100 records/items in total. Depending on the API(s), you may need to repeat the collection process multiple times to download sufficient data. 3. Parse the collected data, and store it in an appropriate file format for subsequent analysis (e.g. plain text, JSON, XML, CSV). 4. Load and represent the data using an appropriate data structure (i.e. records/ items as rows, described by features as columns). Apply any pre-processing steps that might be required to clean/filter/combine the data before analysis. 5. Analyse and summarize the cleaned dataset, using tables and visualizations where appropriate.