| from datetime import datetime | |
| from sqlalchemy import Column, Integer, DateTime, ForeignKey | |
| from sqlalchemy.orm import relationship | |
| from sqlalchemy.ext.declarative import declared_attr | |
| from flask_security import current_user | |
| class AuditMixin(object): | |
| created_at = Column(DateTime, default=datetime.now) | |
| updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now) |
| #!/usr/bin/env python26 | |
| import logging | |
| import pika | |
| logging.basicConfig() | |
| class Consumer(object): | |
| """ | |
| A RabbitMQ topic exchange consumer that will call the specified function |
| # coding=UTF8 | |
| ######################################################################### | |
| # This class is to help sklearn to handle statistical process # | |
| # Author: Joon Lim from Master of Science in Analytics at Northwestern # | |
| # Date: 04.23.2013 # | |
| ######################################################################### | |
| ''' this Module is built on top of numpy and sklearn. ''' | |
| brew install qt # should already be done | |
| brew install qwt # should already be done | |
| brew install portaudio # should already be done | |
| brew install wget # makes some downloading easier | |
| # set up your virtualenv (`workon friture` if you've already created it) | |
| mkvirtualenv friture | |
| cd /tmp |
| import logging | |
| import multiprocessing | |
| import time | |
| import mplog | |
| FORMAT = '%(asctime)s - %(processName)s - %(levelname)s - %(message)s' | |
| logging.basicConfig(level=logging.DEBUG, format=FORMAT) | |
| existing_logger = logging.getLogger('x') |
| import multiprocessing | |
| import pandas as pd | |
| import numpy as np | |
| def _apply_df(args): | |
| df, func, kwargs = args | |
| return df.apply(func, **kwargs) | |
| def apply_by_multiprocessing(df, func, **kwargs): | |
| workers = kwargs.pop('workers') |
| curl --header 'Authorization: token INSERTACCESSTOKENHERE' \ | |
| --header 'Accept: application/vnd.github.v3.raw' \ | |
| --remote-name \ | |
| --location https://api.github.com/repos/owner/repo/contents/path | |
| # Example... | |
| TOKEN="INSERTACCESSTOKENHERE" | |
| OWNER="BBC-News" | |
| REPO="responsive-news" |
| package org.apache.spark.graphx | |
| import org.apache.spark.SparkContext | |
| import org.apache.spark.SparkContext._ | |
| import org.apache.spark.SparkConf | |
| import org.apache.spark.rdd.RDD | |
| import org.apache.spark._ | |
| object repl { | |
| val sc = new SparkContext("local", "test") //> sc : org.apache.spark.SparkContext = org.apache.spark.SparkContext@3724af13 |
If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?
I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:
