Map of Tracks Topics

Topics Index

Python Libraries

Data Collection

Descriptive Statistics

Probability

Probability Distributions

Confidence interval

Hypothesis tests

P-Value

Linear regression and model fit

Regression x Classification, train-test split, parameter and hyperparameter, Neural Networks, Garson's Algorithm, Clustering, ROC Curve, Ensembles

Laws, Theorems, Databases

Maps and its applications

Python Libraries

Google Colab Notebook Intro

T01

Pandas and data frames

All tracks employed Pandas commands

Folium and maps applications

T02

T05

Numpy and Scipy.stats

T05

T06

T07

T08

T09

Matplotlib

All tracks employed Matplotlib commands

Sklearn

T09

Statsmodels

T09

Seaborn

T09

Data Collection

Population x Sample

Types of variables

Quantitative: Discrete x Continuous

Categorical: Binary, nominal, ordinal

T03-1.1

Data Cleansing

Missing Data

Outliers

T03-2.1

T03-1.6

T03-2.4

Descriptive Statistics

Frequency Distribution

Measures of Central Tendency

Measures of Variability

Univariate Descriptive Statistics

T03-2.2

T03-1.4

T03-1.5

T03-2.3

How to find an outlier using a box-plot?

Build a bar graph

Build and interpret a box-plot

T03-1.6

T03-2.2

T03-2.4

T03-2.5

Probability

Multiplication rule

Combinations

Permutations

Coin and Six-dice Simulator in Python

T04-2.8

T04-2.10

T04-2.1

T04-2.2

Simple or marginal probability

Conditional Probability

Bayes Theorem

Probability Independence

T04-2.5

T04-2.6

T04-2.9

T04-2.7

Probability Distributions

Bernoulli

Binomial

Geometric

Poisson

T05-1.5

T05-2.1

T05-1.6

T05-1.7

T05-2.3

T05-1.8

T05-2.2

Uniform

Normal

Student T

Mean and variance

T05-1.4

T06-1.1

T06-1.2

T06-1.3

T06-1.4

T06-2.3

T06-2.4

T06-1.5

T06-1.6

T06-2.3

Confidence interval

Central Limit Theorem

Confidence Interval and normal distribution

Confidence Interval and Student T distribution

Estimating sample size normal distribution

T07-1.2

T07-1.3

T07-1.4

T07-1.5

T07-1.6

T07-1.8

T07-1.9

T07-1.10

Estimating sample size Student T distribution

Confidence interval for proportion

Confidence Interval application

Sample size application

T07-1.11

T07-1.12

T07-2.1

T07-2.3

T07-2.2

Hypothesis tests

Test of Hypothesis

Z-Test

T-Test

Chi-squared Test

T08-1.1

T08-1.2

T08-1.3

T08-1.4

T08-1.5

T08-1.6

P-Value

P-Value and its meaning

T08-1.7

P-Value for Test of Hypothesis

T08-1.7

Statistical Power and Power Analysis

T08-1.8

Shapiro Wilk for normality test

T08-1.9

T08-1.10

T08-1.11

T08-1.12

Linear regression and model fit

Compute and interpret linear regression

T09-1.1

T09-1.2

Test of hypothesis and P-Value

T09-1.2

T09-1.5

T09-1.6

Least squares fit and its equations

T09-1.1

T09-1.2

Multiple regression numerical example

T09-1.5

T09-1.6

P-Value for multiple regression

T09-1.1

T09-1.2

T09-1.5

T09-1.6

Converting categorical varibles to dummy ones

T09-1.4

Multiple linear regression with dummy

T09-1.6

Metrics to evaluate linear regression

T09-1.1

Regression x Classification, train-test split, parameter and hyperparameter, Neural Networks, Garson's Algorithm, Clustering, ROC Curve, Ensembles

Regression x Classification

T10-1.1

T10-2.1

T10-2.2

Train-Test Split

T10-1.2

T10-2.2

T10-2.3

Parameter versus Hyperparameter

T10-1.3

Training, Validation, and Test

T10-1.4

T10-2.2

Neural Networks

T10-1.5

T10-2.3

Garson's Algorithm

T10-1.6

Clustering Algorithms

T10-1.7

T10-2.1

ROC Curve

T10-1.8

T10-2.2

T10-2.3

Ensembles

T10-1.9

T10-2.3

Laws, Theorems, Databases

Law of Large Numbers

Central Limit Theorem

WITS

Canada Immigration

T04-1.4

T07-1.2

T07-1.3

T07-1.4

T01-2.3

T02-2.3

T02-1.3

T02-1.4

Numbeo

UK importation site

INTTRA HS6 Weight

INTTRA HS6 Weight/count

T02-2.5

T02-2.6

T02-2.7

T03-1.2

T06-2.1

T06-2.6

T10-2.1

Volume of Taiwan Import Containers

T09-2.1

Risk Management score intervals

T09-2.2

World Bank - Ease of Doing Business Score

T09-2.3

Predicting Credit Card Risk of Default

T10-2.2

Maps and its applications

Choropleth

Create a GeoJSON

Data Fusion

Markers inside of a Map

T02-2.1

T02-1.2

T02-2.1

T02-2.2

Bar graph inside markers

Flow to draw a route

Maps and probabilities

Map of predicted Easy of Doing Business Score

T02-2.4

T02-2.7

T05-2.2

T09-2.3

Google Colab

Extra Bonus Material

Principal Component Analysis

Explanation

https://www.datacamp.com/tutorial/principal-component-analysis-in-python

A small numerical example

https://www.geeksforgeeks.org/principal-component-analysis-with-python/

Checking the importance of each component using PCA

https://stackoverflow.com/questions/50796024/feature-variable-importance-after-a-pca-analysis/50845697#50845697

https://plotly.com/python/pca-visualization/

Using Chi-square in several machine learning problems

https://medium.com/@dancerworld60/unlocking-the-power-of-chi-square-a-guide-to-statistical-analysis-92080ae6176b

Comparison using multi-criteria decision-making method

https://arxiv.org/abs/2110.00508

Correspondence Analysis

https://medium.com/low-code-for-advanced-data-science/understanding-and-applying-correspondence-analysis-cbd0192dec4

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