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A landing page for an imaginary landscaping business that contains a hyperlink to a calculator for the business’s services in order to receive a quote.

Website Redesign and Prototype Development: Yale School of Art

The objective of this project was to select a company and enhance its digital presence through a redesigned or newly created website. The focus was on improving user experience by identifying and addressing specific user needs, emphasizing usability, functionality, and aesthetic appeal. This work was completed as part of the MIS3506 course, which emphasizes user-centric design principles.

For this project, I chose the Yale School of Art website, a critical resource for prospective students, current students, faculty, alumni, and art enthusiasts. While the existing website provides valuable information, it suffers from outdated design, unintuitive navigation, and difficulty locating key content. These challenges underscored the need for a redesign to enhance usability, accessibility, and overall user experience.

Requirement Analysis

I conducted a comprehensive requirement analysis to understand the primary users and their goals:

• Prospective Students: Seek information on academic programs, admissions, and examples of student work.
• Current Students: Need access to resources, event updates, and tools for booking equipment.
• Faculty: Require a platform to share course materials, communicate with students, and promote their work.
• Alumni: Want to stay connected, access career resources, and showcase their achievements.
• Public Visitors: Look for information on exhibitions, events, and general school details.

The redesigned website needed to feature intuitive navigation, dynamic galleries for artwork, responsive design for accessibility, and tools for event updates and secure portals. Additionally, the site had to maintain the artistic identity of the school while integrating modern usability features.

User Persona: Emma Carter

To guide the design process, I developed a persona named Emma Carter:

• Background: A 32-year-old freelance graphic designer and art enthusiast from Brooklyn, NY, with a Bachelor’s degree in Fine Arts.
• Goals: Stay informed about exhibitions, browse artwork galleries, and learn more about the school’s history and programs.
• Pain Points: Finds academic sites outdated or cluttered, struggles with complex navigation, and dislikes slow-loading pages.
• Needs: A user-friendly event calendar, high-quality online galleries, responsive design, and engaging multimedia content.

Figma Prototype

Using Figma, I developed a high-fidelity prototype for the redesigned website. The prototype reflects a modern, clean aesthetic aligned with the school’s artistic brand. Key features include:

• Navigation Menu: Simple, intuitive design for seamless exploration.
• Five Core Pages: Home, About, Apply, Exhibitions, and Log In.
• Call-to-Action: A prominent “Apply Now” button to streamline the application process.
• Visual Enhancements: High-quality galleries, a responsive layout, and engaging multimedia elements.

Key Takeaways

This project provided valuable hands-on experience in user experience design and prototyping. I gained skills in:

• Conducting requirement analyses and creating user personas.
• Identifying and addressing usability challenges.
• Using Figma to create professional, user-friendly prototypes.

This project reflects my ability to integrate creativity and analytical thinking to deliver visually compelling and functional digital solutions. It also demonstrates my proficiency in applying user-centric design principles to real-world scenarios.

Link to my prototype website: https://www.figma.com/proto/4DlI5Vgdf4bXPTuxXa5Oug/Untitled?node-id=1-2&node-type=frame&t=It6jbWBpAqvMVSQh-0&scaling=min-zoom&content-scaling=fixed&page-id=0%3A1

Using a decision tree algorithm, I examined a group of used automobiles and forecasted their asking prices based on brand, model, age, mileage, fuel type, and transmission type. To improve the model’s performance, I determined the best value for the minimum split, determined the nodes with the highest and lowest probabilities, and described how they relate to the dataset’s attributes. To demonstrate the decision tree’s value for pricing analysis, I also used it to predict the outcomes of a few sample data points. I completed all the work in Python using a Jupyter Notebook.

The goal of this project was to use decision tree algorithms to analyze data on student’s academic and professional qualifications and predict job placement outcomes. The dataset included features such as secondary school percentage, degree percentage, MBA percentage, and employment test scores. This project helped me improve my skills in data preprocessing, decision tree analysis, and model optimization using tools like Python, scikit-learn, and pandas. This also provided me with valuable insights into applying data analysis techniques to real-world problems.

For this project I had to create a decision tree diagram with python that can be used to predict the likelihood of a person having diabetes depending on various health variables such as age, smoking history, presence of heart disease, and BMI. I used this decision tree to determine which types of patients have the most and least likelihood of diabetes, as well as forecasting the likelihood of diabetes in specific scenarios.

DataAnalyticsProPoints

diabetes_prediction_dataset

During this project I analyzed a dataset related to car purchases using a decision tree model. The goal of my project is to predict whether a user will purchase a car based on financial and age features. My dataset contains 5 columns including UserID, Gender, Age, AnnualSalery, and Purchased. I was able to train my decision tree model to get 90% of validation accuracy and 90.14% training accuracy.

I completed a decision tree analysis for MIS 2502 with Konstantin Bauman. I selected a data set detailing information about car sales, including the age, gender and income of prospective buyers and the outcome of their decision. By building the decision tree, patterns in the data were identified, allowing the prediction of whether a person will buy a car or not based on identifying features. While creating the decision tree, I tested various combinations of minimus split and depth settings to provide the most effective results and reduce the effects of over and underfitting.

Schoen_2502_PRO_points

car_data

This project involved analyzing a loan approval dataset using a decision tree algorithm to predict the approval status of loan applicants based on their financial features such as income, credit score, and loan amount. This project was part of my MIS2502 coursework. My role was to clean and analyze data, build a decision tree model, and interpret the results to gain insights into the approval process. Through this project, I identified the optimal minimum split for the decision tree, achieving high training and validation accuracy rates. I also explored nodes with extreme probabilities to uncover patterns in approved and rejected applicants. Additionally, I applied the model to real-world scenarios by predicting outcomes for individual applicants. This experience enhanced my understanding of decision tree algorithms, feature importance, and the role of predictive analytics in financial decision-making. It also improved my technical skills in data processing and model evaluation, as well as my ability to communicate complex findings clearly and effectively. Overall, this project demonstrated my ability to apply analytical tools to solve real-world scenarios.

The goal of this project is to provide MIS students with additional hands-on experience in data analysis and reinforce the concepts and methods covered in class.

For this project, students should find a new suitable dataset on the internet (that was never used in the class before) and apply Decision Tree analysis to build the prediction of the outcome variable. The process should be very similar to the regular assignment on Decision Trees (i.e., start with the same Jupyter Notebook) but applied to a new dataset.

annotated-diabetes_dataset.csv

annotated-Propointsproject.docx (1)

To complete this assignment, I sourced a dataset detailing patients and their health attributes that may or may not be correlated with having diabetes. This dataset proved more difficult for me to find a fitting minimum split than past ones have, but I was able to settle on using 50 because it maximized visibility as well as accuracy. I created the four scenarios with differing attributes to try and get an idea of both sides of the spectrum as far as likelihood of having diabetes. These scenarios were based on glucose levels, age, BMI, Diabetes pedigree function, and blood pressure. After analysis of the data, it became clear that certain attributes, like high blood pressure, BMI, and glucose levels, could help indicate a patient’s chance of having diabetes.