PROJECTS
Tableau | Data Visualization
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In this project, we used information based on crime in Chicago to create visualizations that analyzed this information. We grouped data together based on location, type of crime, and time of crime. We utilized Tableau to represent this data through line charts, crosstabs, heat maps, etc. We cleaned the data to create the visualizations by cleaning the data in Access using SQL.
Microsoft Excel | Time Series Analysis and Forecasting
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Using Microsoft Excel we were able to create a Time Series Analysis from forecasts to determine the best model for predicting trends in gasoline sales. We created an average, naive, 3 week moving average, and 6 week moving average models for the data.
Microsoft Excel | Regression Analysis
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In this project, we utilized statistics based on Median SAT Score, type of college, college acceptance rate, and expenditures of students to relate to graduation rates. We cleaned up data, removed outliers, and used linear regression with collinearity. Through this we were able to determine which factors most likely determined graduation rates of a college.
Project | 01
Project | 02
Project | 03
SAS Visual Analytics | Cluster Analysis
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Utilizing Cluster Analysis in SAS Visual Analytics we were able to take a look at factors that show patterns in patients with coronary heart disease. We could look at factors such as smoker or non-smoker, age of CHD diagnosis, systolic, diastolic, and cholesterol to determine patterns.
Project | 04
Project | 05
SAS Visual Analytics | Decision Trees
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In the SAS Visual Analytics, we described data utilizing Decision Trees. The Decision Tree allowed us to use data determine certain aspects in smokers that are related that could relate to death. The tree split with a large cause and then split into other factors to relate the deaths for people with Coronary Heart Disease.
SAS Visual Analytics | Logistic Regression
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Using SAS Visual Analytics, we used Logistic Regression to predict who would likely to donate to the Paralyzed Veterans Administration. This software provided us with the lift chart that shows how much better our model was at predicting and targeting possible donors and shows the advantages that our model has over the random one.
Project | 07
Microsoft Access | SQL
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In Microsoft Access we used the programming code SQL before putting the data into Tableau to further clean the data. Through the SQL code, we were able to remove all crimes that were not labeled with an IUCR code so we would have clean, correct data to export into Tableau.
Project | 06
Microsoft Excel | Monte Carlo Risk Simulation
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In Microsoft Excel, we used the Analytic Solver Platform to run the Monte Carlo Risk Simulation to determine whether or not a Midwestern pharmaceutical company that discovered a potential drug breakthrough in the lab should move forward to conduct clinical trials and seek FDA approval to market the drug or not.
Project | 08
Project | 09
Microsoft Excel | Optimization
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In Microsoft Excel, we used the Analytic Solver Platform to run optimization analysis on MedEX data in order to maximize customers reached via advertising for under their $14,000 budget. We also determined the optimal budget would be at $22,000 where 40 people were reached. We discovered the trend line was not monotonically decreasing because the percent changes are not equal to each other.