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Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

• They are the people who get things done. Effective Engineers produce results.

Adopt the Right Mindsets

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#!/usr/bin/env python3
#-*- coding:utf8 -*-


import numpy as np
from itertools import count


class Position:
    def __init__(self, row, column):

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import boto3


KINESIS = boto3.client('kinesis')
STREAM_NAME = 'my-kinesis-stream'
SHARD_ID = 'my-shard-id'

def get_sequence_number():
    # returns max sequence number if found, None if not
    return '0123456789'

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.headers on
.mode column

.mode csv
.output data.csv

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# https://stats.idre.ucla.edu/r/library/r-library-introduction-to-bootstrapping/

# Bootsrapping is a nonparametric method which lets us
# compute estimated standard errors, confidence intervals
# and hypothesis testing.

# Basics steps:
# 1. resample a given data set a specified number of times
# 2. calculate the specific statistic from each sample
# 3. find the standard deviation of the distribution of that statistic

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# http://www.stat.wisc.edu/~larget/stat302/chap3.pdf

# also...
# https://www2.stat.duke.edu/courses/Fall12/sta101.002/Sec3-34.pdf


# Bootstrap Confidence intervals with standard errors
# using the Atlanta commute times dataset

library(Lock5Data)

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def beta_1(a, b):
    return math.gamma(a) * math.gamma(b) / math.gamma(a + b)
  

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from scipy.stats import beta
import matplotlib.pyplot as plt
import numpy as np

a = 2
b = 12

x = np.arange(0.001, 1, 0.01)
y = beta.pdf(x, a, b)

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def prob_b_winning(a_success, a_fail, b_success, b_fail):
    predict = 0
    for i in range(b_success):
        predict += math.exp(beta_l(a_success + i, b_fail + a_fail)
                            - math.log(b_fail + i)
                            - beta_l(1 + i, b_fail)
                            - beta_l(a_success, a_fail))
    return predict

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%matplotlib inline
import numpy as np
import scipy.stats as stats
from IPython.core.pylabtools import figsize
import matplotlib.pyplot as plt


figsize(12.5, 5)

params = [(2, 5), (1, 1), (0.5, 0.5), (5, 5), (20, 4), (5, 1)]