| import dash | |
| import dash_core_components as dcc | |
| import dash_html_components as html | |
| import plotly.graph_objs as go | |
| import pandas as pd | |
| app = dash.Dash() | |
| df = pd.read_csv( | |
| 'https://gist.githubusercontent.com/chriddyp/' |
Free Basics in India - some links. Note these are not meant to be an exhaustive survey - just what I've come across in late Dec 2015 after Mark Zuckerberg wrote an op-ed piece for Times Of India
Times of India on Net Neutrality
Alex Howard's article on alternate methods of subsidized Internet access in the US
Indian regulators ask Facebook's Indian telco partner - Reliance - to put Free Basics on hold
| Session 1 | |
| --------- | |
| * Intro | |
| * Motivation | |
| * Installation | |
| * Overview of Material |
| Verifying myself: My Bitcoin username is +nitinb. https://onename.io/nitinb |
| I've had a good look at the new Data Science Certification program from Johns Hopkins on Coursera | |
| and it looks like the first one to crack the code on what is needed for the future. | |
| It gets many things right and they all add up to make it likely to be | |
| a huge success compared to other such programs. | |
| What does it get right? | |
| a) Makes all 9 classes available at once on Cousera | |
| b) Makes each class a uniform 4 weeks long |
| __author__ = 'nitin' | |
| # -*- coding: utf-8 -*- | |
| """ | |
| =========== | |
| pgmagic | |
| =========== | |
| Magics for interacting with Postgres database server via psycopg2. |
| # pgdb.py | |
| __author__ = 'nitin' | |
| import psycopg2 as pspg2 | |
| import sys | |
| # import pandas as pd, then use pandas.io.sql | |
| db_data = { | |
| 'host':'localhost', | |
| 'dbname': 'nitin', |
| # compute error measures for predictive model for activity (act) as dependent variable | |
| # note this is computed for all 6 activities and a total computed which is an aggregate value | |
| errormeasures <- function(orig, pred, act) { | |
| N <- length(orig) | |
| origtrue <-as.vector(NULL) | |
| origfalse <-as.vector(NULL) | |
| predtrue <-as.vector(NULL) | |
| predfalse <-as.vector(NULL) | |
| === | |
| > rs <- dbSendQuery(con, statement = paste( | |
| + "SELECT * FROM", +"MADlib.c45_train('infogain','public.golf_data','trained_tree_infogain',null,'temperature,humidity','outlook,temperature,humidity,windy','id','class',100,'explicit', 5,0.001,0.001,0)")); | |
| > df <- fetch(rs, n = -1) | |
| > df | |
| training_set_size tree_nodes tree_depth training_time split_criterion | |
| 14 8 3 00:00:00.311362 infogain |