Michael Galarnyk 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.

Install R

	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)

	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:

	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

	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.

	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)

	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