├── .gitattributes ├── Capstone ├── BMoretz_Research.R ├── BMoretz_Research.pdf ├── Quantum-Capital-CEO-Deck.pdf ├── Quantum-Capital-Pitch-Deck.pdf ├── QuantumCapital-Dashboard.Rmd └── README.md ├── Data Cleansing ├── DataCleasing.html ├── DataCleasing.ipynb ├── DirtyData4BestDeal10000.csv └── bestdeal.db ├── Data Engineering ├── 01_SearchPerformance.ipynb ├── 02_SortPerformance.ipynb ├── 03_Recursion.ipynb ├── 04_QuickSort.ipynb ├── 05_HashTable.ipynb ├── 06_BreadthFirstSearch.ipynb ├── 07_DijkstrasAlgorithm.ipynb ├── 08_GreedyAlgorithm.ipynb ├── 09_DynamicProgramming.ipynb ├── 10_KNN.ipynb ├── README.md └── img │ └── RoadTrip_NYC_to_L.A.jpg ├── Decision Analytics ├── 01_Furniture Production Model.R ├── 01_Furniture Production Model.xlsx ├── 01_Steel Production.pdf ├── 02_Allocation Model.R ├── 02_Allocation Model.xlsx ├── 02_Marketing Campaign.pdf ├── 02_Marketing Campaign.xlsx ├── 03_Nonlinear Models.ipynb ├── 03_Nonlinear Models.xlsx ├── 03_Toy Production.pdf ├── 04_Markovian Models.R ├── 04_Markovian Models.xlsx ├── 05_Queuing & Simulation.R ├── 05_Queuing & Simulation.pdf ├── 05_Queuing & Simulation.xlsx └── README.md ├── Financial Risk Analytics ├── 01_Monte-Carlo.pdf ├── 01_Monte-Carlo.rmd ├── 02_Stock-Returns-and-Lognormal-Distributions.pdf ├── 02_Stock-Returns-and-Lognormal-Distributions.rmd ├── 03_Fitting-Bivariate-t-Distribution.pdf ├── 03_Fitting-Bivariate-t-Distribution.rmd ├── 04_Fitting-Copula-Models-Bivariate-Returns.pdf ├── 04_Fitting-Copula-Models-Bivariate-Returns.rmd ├── 05_Daily-Returns-Analysis.pdf ├── 05_Daily-Returns-Analysis.rmd ├── 06_Black-Monday.pdf ├── 06_Black-Monday.rmd ├── 07_Value-at-Risk.pdf ├── 07_Value-at-Risk.rmd ├── 08_Efficient-Equity-Portfolios.pdf ├── 08_Efficient-Equity-Portfolios.rmd ├── 09_Fama-French-Model.pdf ├── 09_Fama-French-Model.rmd └── README.md ├── Geospatial Analysis ├── Chicago Crimes │ ├── Boundaries.geojson │ ├── Chicago Crimes.docx │ ├── Chicago Crimes.pdf │ ├── Chicago_Crimes.ipynb │ ├── Chicago_Crimes_2.ipynb │ ├── README.md │ ├── furtherest_crimes.sql │ ├── gun_crimes.sql │ └── images │ │ ├── chicago_crimes.png │ │ ├── chicago_pd.png │ │ ├── crime_database.png │ │ └── trump_header.png ├── Chicago Food Inspections │ ├── Chicago_Food_Inspection.html │ └── Chicago_Food_Inspection.ipynb └── GitHub Issues │ ├── Data_Visualization_for_GitHub_issues.html │ ├── Data_Visualization_for_GitHub_issues.ipynb │ └── GitHub_issues.json ├── LICENSE ├── Machine Learning ├── Housing Sales Results_01.ipynb ├── Housing Sales Results_01.pdf ├── Housing Sales Results_02.ipynb ├── Housing Sales Results_02.pdf ├── Model Development.ipynb ├── Model Development.pdf ├── Natural Language Classification.ipynb ├── Natural Language Classification.pdf ├── Optical Recognition.ipynb ├── Optical Recognition.pdf ├── Program Objectives.ipynb ├── Program Objectives.pdf ├── README.md ├── Term Deposit Participation.ipynb ├── Term Deposit Participation.pdf ├── User-Content Identification.ipynb └── User-Content Identification.pdf ├── Misc └── Python │ ├── CQLAppDer6.py │ ├── CQLAppDer8.py │ ├── area_between_curves.py │ ├── blood_concentration.py │ ├── deminishing_returns.py │ ├── drug_reaction.py │ ├── drug_reaction_approx.py │ ├── machine_cost.py │ ├── plant_food.py │ ├── polution.py │ ├── rain_fall.py │ ├── simpsons_rule.py │ ├── standard_normal.py │ ├── study_results.py │ ├── supply_demand.py │ ├── trapezoidal_rule.py │ ├── weed_killer.py │ └── wire_cutting.py ├── README.md ├── Statistical Analysis ├── Analysis Background.pdf ├── Analysis_01.Rmd ├── Analysis_01.md ├── Analysis_02.Rmd ├── Analysis_02.md ├── Computations │ ├── 01_into │ │ ├── Introduction_to_Statistics.pdf │ │ └── home_sales.Rmd │ ├── 02_charts │ │ ├── Charts_and_Graphs.pdf │ │ └── charts_graphs.rmd │ ├── 03_descriptive_stats │ │ ├── Descriptive_Statistics.pdf │ │ └── descriptive_statistics.rmd │ ├── 04_probability │ │ ├── Probability.pdf │ │ └── probability.rmd │ ├── 05_discrete_distributions │ │ ├── Discrete_Distributions.pdf │ │ └── Discrete_Distributions.rmd │ ├── 06_continious_distributions │ │ └── Continuous_Distributions.rmd │ ├── 07_sampling_and_sampling_distributions │ │ ├── Sampling_and_Sampling_Distributions.pdf │ │ └── Sampling_and_Sampling_Distributions.rmd │ ├── 08_statistical_inference_estimation_for_single_populations │ │ ├── Statistical_Inference_Estimation_for_Single_Populations.pdf │ │ └── Statistical_Inference_Estimation_for_Single_Populations.rmd │ ├── 09_statistical_inference_hypothesis_testing_for_single_poulations │ │ ├── Statistical_Inference_Hypothesis_Testing_for_Single_Populations.pdf │ │ └── Statistical_Inference_Hypothesis_for_Single_Populations.rmd │ ├── 10_Statistical_Inferences_About_Two_Populations │ │ ├── Statistical_Inferences_About_Two_Populations.pdf │ │ └── Statistical_Inferences_About_Two_Populations.rmd │ ├── 11_analysis_of_variance │ │ ├── Analysis_of_Variance.pdf │ │ ├── Analysis_of_Variance.rmd │ │ └── data │ │ │ ├── hot_dogs.csv │ │ │ └── tableware.csv │ ├── 12_simple_regression_analysis_and_correlation │ │ ├── Simple_Regression_Analysis_and_Correlation.pdf │ │ └── simple_regression_analysis_and_correlation.rmd │ └── _Data Files │ │ ├── diet.csv │ │ ├── education.csv │ │ ├── geyser.csv │ │ ├── home_prices.csv │ │ ├── hot_dogs.csv │ │ ├── judges.csv │ │ ├── mileage.csv │ │ ├── newspapers.csv │ │ ├── noise.csv │ │ ├── nsalary.csv │ │ ├── pontus.csv │ │ ├── psalaries.csv │ │ ├── salaries.csv │ │ ├── schools.csv │ │ ├── shoppers.csv │ │ ├── tableware.csv │ │ └── tires.csv ├── R-Development_01.Rmd ├── R-Development_01.md ├── R-Development_02.Rmd ├── R-Development_02.md ├── README.md ├── data │ ├── abalones.csv │ └── mydata.csv └── figs │ ├── DA1-Part_1b-1.png │ ├── DA1-Part_1c-1.png │ ├── DA1-Part_2a-1.png │ ├── DA1-Part_2b-1.png │ ├── DA1-Part_3a_hist-1.png │ ├── DA1-Part_3b-1.png │ ├── DA1-Part_4a-1.png │ ├── DA1-Part_4a-2.png │ ├── DA1-Part_5b-1.png │ ├── DA1-Part_5c-1.png │ ├── DA1-question2b_support-1.png │ ├── DA1-question_1a_vis-1.png │ ├── DA1-question_1a_vis-2.png │ ├── DA2-Part_1a-1.png │ ├── DA2-Part_1b-1.png │ ├── DA2-Part_1b_lratiobyclass-1.png │ ├── DA2-Part_1c-1.png │ ├── DA2-Part_1c-2.png │ ├── DA2-Part_3a-1.png │ ├── DA2-Part_3b-1.png │ ├── DA2-Part_3b-2.png │ ├── DA2-Part_5a-1.png │ ├── DA2-Part_5b-1.png │ ├── DA2-Part_5b-2.png │ ├── DA2-Part_6b-1.png │ ├── DA2-Part_7a-1.png │ ├── DA2-Part_7b-1.png │ ├── DA2-Part_7c-1.png │ ├── DA2-Part_9-1.png │ ├── DA2-question_2a_support-1.png │ ├── DA2-question_2a_support-2.png │ ├── DA2-question_2a_support-3.png │ ├── RP1-test2c-1.png │ ├── RP1-test3-1.png │ ├── RP1-test3b-1.png │ ├── RP1-test3b-2.png │ ├── RP1-test3c-1.png │ ├── RP1-test3d-1.png │ ├── RP1-test5b-1.png │ ├── RP1-test5c-1.png │ ├── RP1-test5c-2.png │ ├── RP2-bootstrapCorrelation-1.png │ ├── RP2-bootstrapLinearRegression-1.png │ ├── RP2-bootstrapLinearRegression-2.png │ ├── RP2-bootstrapLinearRegression-3.png │ ├── RP2-bootstrapVariance-1.png │ ├── RP2-boundaries-1.png │ ├── RP2-ecp_kurtosis-1.png │ ├── RP2-ecp_kurtosis-2.png │ ├── RP2-ecp_kurtosis-3.png │ ├── RP2-ecp_skewness-1.png │ ├── RP2-ecp_skewness-2.png │ ├── RP2-ecp_skewness-3.png │ ├── RP2-mag-1.png │ ├── RP2-mag-2.png │ ├── RP2-plot-1.png │ ├── RP2-plot-2.png │ └── RP2-testFile-1.png ├── Supervised Learning ├── Comp_01_OLS_Linear_Regression.R ├── Comp_01_OLS_Linear_Regression.pdf ├── Comp_02_Statistical_Inference_lr.R ├── Comp_02_Statistical_Inference_lr.pdf ├── Comp_03_OLS_Dummy_Coded.R ├── Comp_03_OLS_Dummy_Coded.pdf ├── Comp_04_Interpreting_ANCOVA.R ├── Comp_04_Interpreting_ANCOVA.pdf ├── Comp_05_Logistic_Regression.R ├── Comp_05_Logistic_Regression.pdf ├── Comp_06_Poisson_Zip_Regression.R ├── Comp_06_Poisson_Zip_Regression.pdf ├── Modeling Assignment_01.R ├── Modeling Assignment_01.pdf ├── Modeling Assignment_02.R ├── Modeling Assignment_02.pdf ├── Modeling Assignment_03.R ├── Modeling Assignment_03.pdf ├── Modeling Assignment_04.R ├── Modeling Assignment_04.pdf └── README.md ├── 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