sachaarbonel

http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/td_3a_spark.html#installation-de-spark-sous-linux

https://github.com/mGalarnyk/Installations_Mac_Ubuntu_Windows.git

mGalarnyk

The Data Scientist's Toolbox Project (JHU) Coursera

A image video link walkthrough of the project + answers is available at the end of this quiz.

github repo for rest of specialization: Data Science Coursera

Part 1

The Course Project for this course is pretty straightforward. Now that you’ve learned about the basic tools found in the data scientist’s toolbox, we want to make sure that you’ve assembled those tools and are ready to begin using them. This easy project is your chance to demonstrate that you’ve done the basic software setup (R, RStudio, and Github) that will get you through the rest of the Data Science Specialization.

1. Install R

mGalarnyk

P = 34689.96
term = 60

def generate_loan_table(P, term, interest_rate=0.0702):

    def calc_emi(P, n, interest_rate):
        r = interest_rate / 12
        numerator = (r *((1 + r)**(n)) )
        denominator = ((1 + r)**(n)) - 1
        emi = P * (numerator / denominator)

richardliaw

import numpy as np
import torch
import torch.optim as optim

from ray import tune
from ray.tune.examples.mnist_pytorch import get_data_loaders, train, test
import ray
import sys

if len(sys.argv) > 1:

richardliaw

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms

import ray
from ray.util.sgd.torch import TorchTrainer
from ray.util.sgd.torch import TrainingOperator
# https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
from ray.util.sgd.torch.resnet import ResNet18

stephanie-wang

import ray
from ray.util.dask import ray_dask_get

import dask
import dask.dataframe as dd 
import pandas as pd
import numpy as np

dask.config.set(scheduler=ray_dask_get) # Sets Ray as the default backend.

stephanie-wang

import ray
import numpy as np


@ray.remote
def map(data, npartitions):
    outputs = [list() for _ in range(npartitions)]
    for row in data:
        outputs[int(row * npartitions)].append(row)
    return tuple(sorted(output) for output in outputs)