lOMoARcPSD|56800462
Week 1
Why Analytics? 6
Data Vocabulary 7
Classification 8
Support Vector Machines 11
Scaling and Standardization 13
k-Nearest Neighbor (KNN) 13
Week 2
Model Validation 16
Validation and Test Sets 17
Splitting the Data 18
Cross-Validation 20
Clustering 21
Supervised vs. Unsupervised Learning 22
Week 3
Data Preparation 25
Introduction to Outliers 25
Change Detection 27
Week 4
Time Series Data 31
AutoRegressive Integrated Moving Average (ARIMA) 34
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) 34
Week 5
Regression 37
Regression Coefficients 39
Causation vs. Correlation 39
Important Indicators in the Output 40
Week 6
De-Trending 43
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Principal Component Analysis (PCA) 44
Week 7
Intro to CART 47
How to Branch 48
Random Forests 48
Logistic Regression 49
Confusion Matrices 51
Week 8
Intro to Variable Selection 53
Models for Variable Selection 53
Choosing a Variable Selection Model 56
Week 9
Intro to Design of Experiments 59
Factorial Design 60
Multi-Armed Bandits 61
Intro to Advanced Probability Distributions 62
Bernoulli, Binomial, and Geometric Distributions 62
Poisson, Exponential and Weibull Distributions 63
Q-Q Plot 65
Queuing 66
Simulation Basics 66
Prescriptive Simulation 68
Markov Chains 68
Week 10
Intro to Missing Data 71
Dealing with Missing Data 71
Imputation Methods 72
Intro to Optimization 73
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Elements of Optimization Models 74
Modeling with Binary Variables 74
Week 11
Optimization for Statistical Models 76
Classification of Optimization Models 79
Stochastic Optimization 81
Basic Optimization Algorithms 82
Non-Parametric Models 82
Bayesian Modeling 83
Communities in Graphs 83
Neural Networks and Deep Learning 84
Competitive Models 86
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, lOMoARcPSD|56800462
Life is full of mysteries.
Although that can feel a bit overwhelming at times, the interesting thing is that we can use math
to explain a lot of what we see as the "unknown."
In fact, that's the goal of the field of analytics.
Rather than looking at our businesses or organizations and wondering what will work and what
won't, we can use analytics to sift through our data to explain why something happened or why
one idea will work while another won't.
If you're interested in learning more about how that works, you're in the right place.
In this post, I go through the content in week 1 of ISYE 6501 to make sense of what analytics is
and how we can use simple machine learning models to make better decisions.
Why Analytics?
We can use analytics to answer important questions, and we can break those questions down
into three types:
● Descriptive Questions: What happened? What effect does spin rate have on how hard
someone hits the ball? Which teachers in the school produce the best exam results?
● Predictive Questions: What will happen? How much will the global temperature
increase in the next 100 years? Which product will be most popular?
● Prescriptive Questions: What action(s) would be best? When and where should
firefighters be placed? How many delivery drivers should the pizza shop have on hand
on certain days and times?
In short, we can use analytics to make sense of the world around us and to make better
decisions in a complex world.
And we do this through something called modeling.
Downloaded by Guy Cube ()
Week 1
Why Analytics? 6
Data Vocabulary 7
Classification 8
Support Vector Machines 11
Scaling and Standardization 13
k-Nearest Neighbor (KNN) 13
Week 2
Model Validation 16
Validation and Test Sets 17
Splitting the Data 18
Cross-Validation 20
Clustering 21
Supervised vs. Unsupervised Learning 22
Week 3
Data Preparation 25
Introduction to Outliers 25
Change Detection 27
Week 4
Time Series Data 31
AutoRegressive Integrated Moving Average (ARIMA) 34
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) 34
Week 5
Regression 37
Regression Coefficients 39
Causation vs. Correlation 39
Important Indicators in the Output 40
Week 6
De-Trending 43
Downloaded by Guy Cube ()
, lOMoARcPSD|56800462
Principal Component Analysis (PCA) 44
Week 7
Intro to CART 47
How to Branch 48
Random Forests 48
Logistic Regression 49
Confusion Matrices 51
Week 8
Intro to Variable Selection 53
Models for Variable Selection 53
Choosing a Variable Selection Model 56
Week 9
Intro to Design of Experiments 59
Factorial Design 60
Multi-Armed Bandits 61
Intro to Advanced Probability Distributions 62
Bernoulli, Binomial, and Geometric Distributions 62
Poisson, Exponential and Weibull Distributions 63
Q-Q Plot 65
Queuing 66
Simulation Basics 66
Prescriptive Simulation 68
Markov Chains 68
Week 10
Intro to Missing Data 71
Dealing with Missing Data 71
Imputation Methods 72
Intro to Optimization 73
Downloaded by Guy Cube ()
, lOMoARcPSD|56800462
Elements of Optimization Models 74
Modeling with Binary Variables 74
Week 11
Optimization for Statistical Models 76
Classification of Optimization Models 79
Stochastic Optimization 81
Basic Optimization Algorithms 82
Non-Parametric Models 82
Bayesian Modeling 83
Communities in Graphs 83
Neural Networks and Deep Learning 84
Competitive Models 86
Downloaded by Guy Cube ()
, lOMoARcPSD|56800462
Life is full of mysteries.
Although that can feel a bit overwhelming at times, the interesting thing is that we can use math
to explain a lot of what we see as the "unknown."
In fact, that's the goal of the field of analytics.
Rather than looking at our businesses or organizations and wondering what will work and what
won't, we can use analytics to sift through our data to explain why something happened or why
one idea will work while another won't.
If you're interested in learning more about how that works, you're in the right place.
In this post, I go through the content in week 1 of ISYE 6501 to make sense of what analytics is
and how we can use simple machine learning models to make better decisions.
Why Analytics?
We can use analytics to answer important questions, and we can break those questions down
into three types:
● Descriptive Questions: What happened? What effect does spin rate have on how hard
someone hits the ball? Which teachers in the school produce the best exam results?
● Predictive Questions: What will happen? How much will the global temperature
increase in the next 100 years? Which product will be most popular?
● Prescriptive Questions: What action(s) would be best? When and where should
firefighters be placed? How many delivery drivers should the pizza shop have on hand
on certain days and times?
In short, we can use analytics to make sense of the world around us and to make better
decisions in a complex world.
And we do this through something called modeling.
Downloaded by Guy Cube ()
