Solved using VBA in Excel

Monte Carlo simulation to find the optimal portfolio.

Probabilities, expected value, variance, etc. applied to portfolios

Measures of Central Tendency and Dispersion

Examples with the FinCal package.

PostgreSQL Conditional Expressions and Procedures, Import and Export, Python

SQL commands to create databases and tables.

A summary of some advanced SQL commands.

A summary of SQL JOIN statements.

A summary of SQL GROUP BY statements.

A summary of frequently used SQL statements.

Basic Exploratory Data Analysis in R from 2019

Some basic work from 2019

Support Vector Machine, Naive Bayes, and Logistic Regression classifiers with stock data and county health data. Updated formatting.

Evaluating the S&P 500 returns, the Fed Funds rate, and valuation over time. The period ending 2020-12-31. Using Python.

Portfolio statistics, Sharpe Ratio, Portfolio optimization, Capital Asset Pricing Model

Time series analysis using Python's Statsmodels package. Exponentially-weighted moving averages, error-trend-seasonality decomposition, seasonal ARIMA models.

Stock visualization and analysis

Working with dates using Pandas. Datetime Index, Resampling, Time Shifts, Rolling and Expanding.

A few free ways to get prices and other financial information for a stock or index. Pandas Datareader, Quandl, yfinance, yahoofinancials

Solutions to Project Euler numbers 21 through 25 using R and Python

Local regression. General additive models.

Polynomial regression, step functions, splines

Solutions to Project Euler numbers 16 through 20 using R and Python

Solutions to Project Euler numbers 11 through 15 using R and Python

Solutions to Project Euler numbers 6 through 10 using R and Python

Solutions to Project Euler numbers 1 through 5 using R and Python

Data wrangling and exploration. Model selection and evaluation. Matrix calculations. Cross Validation. Python implementation.

NLP using nltk and scikit-learn

Basic recommendation system using Python and pandas.

PCA using scikit-learn

K Means Clustering using scikit-learn

Support Vector Machines using scikit-learn

Decision Trees and Random Forests using scikit-learn

K Nearest Neighbors using scikit-learn

Logistic regression using scikit-learn.

Linear regression using scikit-learn.

An exploratory data analysis of stock prices and an analysis of 911 call data from Kaggle

Plots called directly from Pandas dataframes

Interactive plotly charts - scatter, bar plots, boxplots, histogram, violin, density contour

Plotly maps, Polar Coordinates, Choropleth maps

distribution plots, categorical plots, matrix plots, grids, regression plots

Matplotlib plots and formatting.

NumPy - Arrays, Indexing and Selection, Operations. Pandas - Series, DataFrames, Grouping, Joining, and Merging, Operations, Input and Output.

Some Python basics, like If statements, loops, functions, lists, tuples, dictionaries, mapping, filtering, etc.

Pipes, Functions, Vectors, and Iteration

Creating Date/Times, Date-Time Components, Time Spans, Time Zones

Creating factors, modifying factor order, modifying factor levels

A short analysis on the impact of alternative data on sell-side earnings estimates.

String basics & matching patterns with regular expressions - anchors, repetition, grouping, detecting, extracting, replacing, splitting, locating, etc.

Pivoting Longer (Gathering), Pivoting Wider (Spreading), Separate, Unite, Missing Values, Case Study

mutating joins (inner, left, right, full), filtering joins (semi, anti), and set operations (intersect, union, setdiff)

Filtering, Arranging, Selecting, Renaming, Creating, Grouping, and Summarizing with dplyr

An overview of PCA and PLS with examples.

Summary of Lasso and Ridge Regression with examples

Linear model selection methods - best subsets, forward, backward, sequential replacement. Overview of selection metrics - Cp, AIC, BIC, adjusted R squared.

A review of resampling methods with examples using the `boot` package. Cross-validation and Bootstrapping.

This is an updated report to account for the initial impacts from COVID-19

Scatter Plots, Bar Charts, Histograms, Density Plots, Box Plots, Heat Maps, Facets grids, Frequency Polygons, Pairs, Stat Summaries, Transformation

In this report I give a brief overview of Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and K-Nearest Neighbors. I then apply these classification methods to S&P 500 data.

Regression plots with two independent variables. Includes a fitted regression plane.

Data exploration. Model selection and evaluation. Matrix calculations. Cross Validation.

Screenshots and code for a ShinyApp. Interactive Boxplots, Scatterplots, Histograms, and Regression Plots. Tukey’s HSD Test. Data Table.

Estimating RCL's volatility with a GARCH model. ACF and PACF review. Model selection. Diagnostics. Forecasting.

Using a SARIMA model to evaluate Pnemonia and Influenza data. Data transformation. ACF and PACF review. Model selection and diagnostics. Forecasting.

Support Vector Machine, Naive Bayes, and Logistic Regression classifiers with stock data and county health data.