Ex Libris

An Introduction to Statistical Learning with Applications in R

Statistical Learning

Linear Regression

Classification

Resampling Methods

Linear Model Selection and Regularization

Moving Beyond Linearity

Tree-Based Methods

Support Vector Machines

Unsupervised Learning

R for Marketing Research and Analytics

Part I

Basics of R

Part II

Fundamentals of Data Analysis

Relationships Between Continuous Variables

Comparing Groups: Tables and Visualizations

Comparing Groups: Statistical Tests

Identifying Drivers of Outcomes: Linear Models

Part III Advanced Marketing Applications

Reducing Data Complexity

Additional Linear Modeling Topics

Confirmatory Factor Analysis and Structural Equation Modeling

Segmentation: Clustering and Classification

Association Rules for Market Basket Analysis

Choice Modeling

Data Science Using Python and R

Introduction to Data Science

The Basics of Python and R

Data Preparation

Exploratory Data Analysis

Preparing to Model the Data

Decision Trees

Model Evaluation

Naïve Bayes Classification

Neural Networks

Clustering

Regression Modeling

Dimension Reduction

Generalized Linear Models

Association Rules

Data Summarization and Visualization

Reproducible Finance with R

Returns

Asset Prices to Returns

Building a Portfolio

Risk

Standard Deviation

Skewness

Kurtosis

Portfolio Theory

Sharpe Ratio

CAPM

Fama-French Factor Model

Practice and Applications

Component Contribution to Standard Deviation

Monte Carlo Simulation

Learn Python by Building Data Science Applications

Getting Started with Python

First Steps in Coding - Variables and Data Types

Functions

Data Structures

Loops and Other Compound Statements

First Script - Geocoding with Web APIs

Scraping Data from the Web with Beautiful Soup 4

Simulation with Classes and Inheritance

Shell, Git, Conda, and More - at Your Command

Hands-On with Data

Python for Data Applications

Data Cleaning and Manipulation

Data Exploration and Visualization

Training a Machine Learning Model

Improving Your Model - Pipelines and Experiments

Moving to Production

Packaging and Testing with Poetry and PyTest

Data Pipelines with Luigi

Let's Build a Dashboard

Serving Models with a RESTful API

Serverless API using Chalice

Best Practices and Python Performance

Python 3 Object-orientated Programming Second Edition

Object-oriented Design

Objects in Python

When Objects Are Alike

Expecting the Unexpected

When to Use Object-oriented Programming

Python Data Structures

Python Object-oriented Shortcuts

Strings and Serialization

The Iterator Pattern

Python Design Patterns

Python Design Patterns II

Testing Object-oriented Programs

Concurrency

Practical Machine Learning with Python

Understanding Machine Learning

Machine Learning Basics

The Python Machine Learning Ecosystem

The Machine Learning Pipeline

Processing, Wrangling, and Visualizing Data

Feature Engineering and Selection

Building, Tuning, and Deploying Models

Real-World Case Studies

Analyzing Bike Sharing Trends

Analyzing Movie Reviews Sentiment

Customer Segmentation and Effective Cross Selling

Analyzing Wine Types and Quality

Analyzing Music Trends and Recommendations

Forecasting Stock and Commodity Prices

Deep Learning for Computer Vision

Analyzing Financial Data and Implementing Financial Models Using R

Prices

Individual Security Returns

Portfolio Returns

Risk

Factor Models

Risk-Adjusted Portfolio Performance Measures

Markowitz Mean-Variance Optimization

Fixed Income

Options

*Constructing a Hypothetical Portfolio

Data Smart

Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask

Cluster Analysis Part I: Using K-Means to Segment Your Customer Base

Naïve Bayes and the Incredible Lightness of Being an Idiot

Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself

Cluster Analysis Part II: Network Graphs and Community Detection

The Granddaddy of Supervised Artificial Intelligence--Regression

Ensemble Models: A Whole Lot of Bad Pizza

Forecasting: Breathe Easy; You Can't Win

Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant

Moving from Spreadsheets into R

SQL Cookbook

Retrieving Records

Sorting Query Results

Working with Multiple Tables

Inserting, Updating, Deleting

Metadata Queries

Working with Strings

Working with Numbers

Date Arithmetic

Date Manipulation

Working with Ranges

Advanced Searching

Reporting and Warehousing

Hierarchical Queries

Odds 'n' Ends

*Window Function Refresher

*Rozenshtein Revisited