Kim Pierce Email and Phone Number

Learned further visualization tools for analytics- SQLite, JavaScript, D3, JSON, APIs, OpenRefine, Tableau, PySpark, Spark/Scala, Databricks, Machine Learning, Ranom Forests, Linear Regression, Classification, SVM, PCA, PageRank

Goal: Analyze current and historical housing market trends in order to create a dynamic visualization that explained intricacies to more casual users. - used Regression analysis to determine most influential factors that affect the housing market- used a SARIMA model to help predict future housing market costs for metro areas in the US

Goal: analyze what features were significant in affecting the lifespan of TV shows using various forms of regression analysis- obtained data from multiple datasets and performed data cleaning techniques in order to standardize the data to the format needed for the analysis while reducing as much bias as possible- chose features for training model using Kernel Density Estimation (KDE). Realized data needed to be normalized- used a correlation heatmap to determine correlation between all the features to select most significant features to use for training- tested performance of various regression models using Python library sci-kit learn including: Logistic, LASSO, Linear, and Ridge regression- evaluated model performance using R^2 value- did not find any of the tested features to significantly affect the longevity of the TV show

Learned foundations of statistical machine learning analysis.- Clustering, K-means, Spectral clustering, Dimensionality Reduction, PCA, ISOMAP, density estimation, Gaussian mixture model, EM Algorithm, Optimization Theory, SVM, Neural Networks, Anomaly detection, AdaBoost, Random Forests

Learned how to utilize advance statistical distrubutions of random variables in order to simulate consumer habits as they interact with various businesses.- Simulation of random variables, Probability distributions, maximum likelihood estimation, confidence intervals, Monte Carlo Integration, Single-Server queues, (s,S) inventory systems, Arena, resource failures & maintenance, Random Number Generators, Tausworthe generators, goodness-of-fit tests, Acceptance-Rejection Method, Box-Muller Normal random variables, Stochastic processes, Time Series, Brownian Motion, Steady-State analysis

Goal: Determine which blackjack playing strategy maximizes player profit- programmed a blackjack game simulation in R to fully simulate a game of blackjack- used version control (git) and cloud storage (github) to keep track of changes over time and document progress- programmed different playing strategies and ran through the blackjack simulation to determine which maximized profit for the player

Learned basics of data analytics and modeling methods for various things (time series, clustering, etc.)- Basic HTML processing & APIs, Pandas, Numpy, Seaborn, Bokeh, SQL, SciPy, graph matrices, PageRank, Classification, K-means, PCA, SVD, SVM, KNN, CUSUM, Regression, CART, Random forests, Confusion matrices, Exponential Smoothing, ARIMA, GARCH, Box-Cox, multi-armed bandits, QQ plots

In charge of managing schedules, material, and invoicing for the largest builder in the area. I have also spent dedicated time analyzing their purchase orders to assure we were paid according to the agreed contracts. In addition to this, for over a year I have been in charge of all human resources responsibilities which include weekly payroll for 40 employees and subcontractors, new hire on-boarding paperwork, and organizing employee appreciation events.

Proactively delegated tasks to peers and subordinates while maintaining a clean environment in accordance to food safety requirements. Also created a weekly schedule for 10 employees at a time.

Not only did I do my main Cashier duties, I also have experience with analyzing guest surveys. I was in a leadership team where my main duty was to see how we were rated for the last week and then make plans on how to improve our scores for the following weeks.