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Viasat - Art of Space Warfare: Orbital Modeling of Cascading Effects from Debris based on Space Consumption
Open for Registration- Lecture time: Tuesday: 13:30:00 - 14:20:00 ET
- Lab time: Thursday: 13:30:00 - 15:20:00 ET
- Domain: Research and Development
- Keywords: Orbital Modeling, Satellite, Space
- Tools: Data Modeling, GitHub, Machine Learning, Python, R
- Citizenship: Open to all students
Summary
This project seeks to understand (via modeling) the thresholds of over population in space to assess a sustainable carrying capacity (considerate of risk/impact) that is driven by multiple threat scenarios.
Microsoft - Social Analysis Platform
Open for Registration- Lecture time: Tuesday: 15:30:00 - 16:20:00 ET
- Lab time: Thursday: 15:30:00 - 17:20:00 ET
- Domain: Game
- Keywords: Analytics, Data Engineering, Data Science, Games, Minecraft, Mojang Studio, Python
- Tools: Azure, Databricks, ETL, Machine Learning, NLP, Python, Spark
- Citizenship: Open to all students
Summary
This year’s project will explore using AI tools to generate insights from text, image and video content on social platforms to develop efficient pipelines to process and enrich multi-modal content. Create tools to improve how processed data and insights can be analyzed.
BAE Systems - Customer Support and Solutions
Open for Registration- Lecture time: Thursday: 12:30:00 - 13:20:00 ET
- Lab time: Tuesday: 11:30:00 - 13:20:00 ET
- Domain: Manufacturing
- Keywords: Aerospace, Analytics, Business Intelligence, Data Cleaning, data mining, Data Visualization, Machine Learning, Predictive Analysis, Sales Forecasting
- Tools: Data Analytics, Machine Learning, Power BI, Python, R, Tableau
- Citizenship: Open to all students
Summary
Develop a predictive model by leveraging advanced analytics and machine learning techniques, the project aims to provide an accurate sales forecast that in turn enhances internal inventory management, optimizes supply chain operations, and improves sales strategies.
Syngenta - Predictive Product Placement using Machine Learning
Open for Registration- Lecture time: Monday: 09:30:00 - 10:20:00 ET
- Lab time: Friday: 09:30:00 - 11:20:00 ET
- Domain: Agriculture
- Keywords: Predictive Modeling
- Tools: Predictive Models, Python, Statistics
- Citizenship: Open to all students
Summary
Strategically positioning our vegetable products at Syngenta in the most suitable markets is crucial for product success. Product performance is influenced by the combination of genetics, environment, and management practices.
Purdue Student Life - Recreation and Wellness Data
Open for Registration- Lecture time: Friday: 13:30:00 - 14:20:00 ET
- Lab time: Monday: 13:30:00 - 15:20:00 ET
- Domain: University
- Keywords: Belongingness, Factor Analysis, Python, Student Experience, Student Life
- Tools: Data Cleansing, Data Exploration, R-Studio, Statistical Modeling, Technical Writing
- Citizenship: Open to all students
Summary
In 2023-24 RecWell had close to 1.7 million entries. What does all of this data say about our students? What is the impact of engaging with the Recreation and Wellness Center on students? Does it impact their grades? Their feeling of belonging? This is what we want you to help us
Wabash - E-Commerce Website Analytics & Support
Open for Registration- Lecture time: Monday: 09:30:00 - 10:20:00 ET
- Lab time: Friday: 09:30:00 - 11:20:00 ET
- Domain: Supply Chain
- Keywords: AI, Azure, Behavioral Analytics, Web Browser Extension, Website
- Tools: AI forecast generation, Azure, Azure IoT, Data Analytics
- Citizenship: Open to all students
Summary
Using the website data, this project will explore customer behaviors that will give clear insights into Marketing Decisions, Sales Discussions, and Inventory analysis. What results in a sale, and what prohibits customers from purchasing?
Purdue Athletics - Purdue Athletics- Ticketing
Open for Registration- Lecture time: Monday: 13:30:00 - 14:20:00 ET
- Lab time: Friday: 13:30:00 - 15:20:00 ET
- Domain: Athletics
- Keywords: Pricing, Secondary Market Data, Stadium Mapping, Ticket Sales, Ticket Usage
- Tools: Dash, Plotly, Python
- Citizenship: Open to all students
Summary
Use ticket sales data and game day scan data from static files and auto generated .igy files. The map will provide an on-demand color coded visual of ticket type combinations and purchasing patterns. Scan data visual mapping and look at secondary market ticket pricing.
MISO - Data-Driven Solutions for the Reliable Operation of the Changing Electric Grid
Open for Registration- Lecture time: Tuesday: 15:30:00 - 16:20:00 ET
- Lab time: Thursday: 15:30:00 - 17:20:00 ET
- Domain: Energy
- Keywords: AI, Anomaly Identification, Electrification, Energy Management, Energy & Renewables, Forecasting, Machine Learning
- Tools: Azure, Python, R
- Citizenship: U.S. citizens and permanent residents preferred
Summary
The Reliability Imperative is the term MISO uses to describe the shared responsibility that MISO, its members, and states have to keep electricity moving. This involves analyzing large amounts of data to identify trends and patterns that impact the electric grid's reliability.
Elevance Health - Identifying Changes in Provider Billing Practices after Reimbursement Contract Changes
Open for Registration NDMN/IDM- Lecture time: Thursday: 11:30:00 - 12:20:00 ET
- Lab time: Tuesday: 11:30:00 - 13:20:00 ET
- Domain: Insurance
- Keywords: Machine Learning, Python
- Tools: Pattern Identification
- Citizenship: Open to all students
Summary
When healthcare providers change reimbursement contracts, billed procedure codes may change in an attempt to maximize the provider’s revenue. Early identification through claims data analytics will help keep healthcare costs affordable for our members.
Elevance Health - Security Use Cases Surrounding Abnormalities and Outliers
Open for Registration INDY- Lecture time: Tuesday: 09:30:00 - 10:20:00 ET
- Lab time: Thursday: 09:30:00 - 11:20:00 ET
- Domain: Insurance
- Keywords: abnormality, Security, Unsupervised, Visualizations
- Tools: Machine Learning, Pandas, Python, SQL
- Citizenship: Open to all students
Summary
In the normal course of business, the security programs amount a staggering sum of data. Sifting through this data to discover irregular issues for devices, users, and logs is difficult and unwieldy. Author a framework capable of making these discoveries and detections.