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Title

The top n questions data scientists ask

Introduction

Data science doesn’t start with data, it starts with a problem…

The pipeline model is useful, but data scientists progress via a series of questions - what are those questions?

Scoping

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from elasticsearch.helpers import bulk
from elasticsearch import Elasticsearch

class ElasticIndexer(object):
    """
    Create an ElasticSearch instance, and given a list of documents, 
    index the documents into ElasticSearch.
    """
    def __init__(self):
        self.elastic_search = Elasticsearch()

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import os

from sklearn.datasets.base import Bunch
from yellowbrick.download import download_all

## The path to the test data sets
FIXTURES  = os.path.join(os.getcwd(), "data")

## Dataset loading mechanisms
datasets = {

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package main

import (
    "fmt"
    "log"

    "github.com/shirou/gopsutil/mem"
    "github.com/shirou/gopsutil/cpu"
    "github.com/shirou/gopsutil/disk"
    "github.com/shirou/gopsutil/host"

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import os 
import zipfile
import requests 
import pandas as pd 

WALKING_DATASET = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/00286/User%20Identification%20From%20Walking%20Activity.zip", 
)

def download_data(path='data', urls=WALKING_DATASET):

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# kimchi.py
# For converting Python 2 pickles to Python 3

import os
import dill
import pickle
import argparse


def convert(old_pkl):

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#!/usr/bin/python
# -*- coding: utf-8 -*-
# plot_classifier_comparison.py
"""
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.

Particularly in high-dimensional spaces, data can more easily be separated
linearly and the simplicity of classifiers such as naive Bayes and linear SVMs