How Does Experiential Learning Help With The Goal?
Even the most highly trained data science and engineering staff require time to become familiar with each new company’s data stack and data structure. This familiarity requires months (if not years for complex ecosystems) to establish a firm data relationship.
With the BYU-I Data Science Experiential Learning program, you can shape the training process. Our program offers multiple touchpoints, allowing our students to develop familiarity with your company’s data and infrastructure. This flexibility ensures that the training aligns with your specific business needs.
How Can I Get Involved?
Students
As a BYU-Idaho student, you have a lot of great opportunities to get involved with Data Science at your fingertips. If interested, you might consider visiting the Data Science Society, and looking for ways to get involved there. As you progress through your schooling, make plans to register for the Statistical and Data Science Consulting course (MATH488) where you will get an opportunity to work hand in hand with a real-world client to help them find a solution for their organization.
Employers
If you are an employer or representative from an organization interested in partnering with BYU-Idaho’s Data Science students and faculty, we would encourage you to start by registering a project with our Experiential Learning Team. When registering your project, you will be asked which course you would like your project to be used for. You will need to select MAT 488 in order for it to be selected for our Data Science students and faculty mentors. If you need help knowing which types of projects can be registered, there is a list below the project registration link that shares types and examples of projects that have been done in the past.
Please note that there are fees associated with this type of work. Specifics of those fee amounts will be discussed prior to starting any projects.
Data Analysis
Our team can systematically apply statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. We can help you make sense of performance, survey, spatial, financial, sales data, and more, ensuring that you have the information you need to make informed decisions.
Examples:
- A Local Credit Union: Students supported the analytics team in evaluating customer retention, transition among clients’ credit offerings, credit card profitability, and customer demographics. The R programming language was used to develop final reports with guidance on preferred credit offerings among those evaluated.
- A Communications Company: Our teams have assisted in testing data science tools and using these tools to simplify reporting. Previous student consulting teams have worked on developing Trelliscope functions and dashboards.
Dashboarding
We provide a fundamental means of displaying key business information to measure progress against goals, for any aspect of any organization or business, all in a highly visual manner. We offer dashboards with different tools such as Power BI, Shiny apps, Streamlit, Quarto, and Tableu, giving you the flexibility to choose the best option.
Examples:
- A Local School District: The student consulting team connected to a Local School District’s data and worked on designing reports through Power BI to create dashboards. Data covers student performance, enrollment and attendance, human resources, management and maintenance overview.
- A Local Steel Company: Students developed sales and HR employee management dashboards using PowerBI and Shiny. BYU-I provided the client with detailed SQL statements to pull and format data for use in dashboards. Final PowerBI dashboards were then internalized into company use with a BYU-I intern the following semester.
Machine Learning
We use machine learning to automate analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Our team can help you make better decisions based on data insights, allowing you to stay ahead of the curve.
Examples:
- A construction Software Company: Our team created an economic model that predicts inflation rates in construction projects. The project included identifying economic drivers, building a web scraping algorithm to automate the data collection process, and creating a machine learning model that predicted inflation rates for the client’s areas of interest. The final product was a Streamlit dashboard.
Predictive Modeling
Our student teams are trained to use this statistical technique to predict future behavior. Predictive modeling solutions are a form of data-mining technology that analyzes historical and current data and generates a model to help predict future outcomes. We can help you predict store placement, inflation, or any other needs you may have, giving you a more accurate picture of what’s to come.
Examples:
- A construction Software Company: Our team created an economic model that predicts inflation rates in construction projects. The project included identifying economic drivers, building a web scraping algorithm to automate the data collection process, and creating a machine learning model that predicted inflation rates for the client’s areas of interest. The final product was a Streamlit dashboard.
Spatial Data Analysis
Our teams can help you study entities by examining, assessing, evaluating, and modeling spatial data features such as locations, attributes, and their relationships that reveal data’s geometric or geographic properties.
Examples:
- A Gas Station Chain: In previous semesters, student consulting teams have worked with this Gas Station Chain to develop store placement models, forecasting models to predict sales for the next quarter, analyze and report on each of the Gas Station Chain’s store locations’ quality based on customer feedback, and develop dashboards that displayed optimal locations for new Gas Station Chain stores.
Web Scraping
Our teams can extract content and data from a website using bots. Web scraping extracts underlying HTML code and, with it, data stored in a database. The scraper can then replicate entire website content elsewhere, allowing you to easily access the information you need.
Examples:
- An Education Technology Company: The student teams found, scraped, and processed data from over 2,000 US school district websites to help the client understand the performance and contact information of the school districts. The R programming language was used for web scraping and data manipulation.
- A Local University: Our teams worked with the Agricultural Economics Department at a local university to build an automated dashboard for students to use in their classes. The dashboard automatically pulls commodity data from both cash and futures market reports, creating a basis table. The data is scraped on a weekly basis to match the frequency with which the website is updated.
Natural Language Processing
Our teams use NLP to analyze different aspects of human language, including syntax, semantics, pragmatics, and morphology. By transforming this knowledge into rule-based, machine-learning algorithms, we can help you solve specific problems and perform desired tasks with accuracy and efficiency.
Examples:
- A History Foundation: Our team used our own Regex technique to match topics based on journal text. We created a dashboard of project details and a story map of an important time in his life using Streamlit. Previous teams have applied sentiment analysis to this History Foundation’s journals, correlating them to different locations and people that were written about, among other topics.
Report Automation
We provide report automation services to have business reports automatically updated and delivered through platforms on a specified schedule. It includes data being automatically extracted, visualizations being automatically updated, and reports being automatically shared without anyone having to manually do this work every time. It delivers business insights to users and top management without extra labor, allowing you to focus on more important tasks.
Examples:
- A Software Solutions Company: The team automated the reporting process to reflect changes in usage patterns and cancellations in real-time. The project’s deliverables included a functional dashboard, along with documentation outlining how to use and maintain it.
Building Python / R packages
Our team can help you build R packages to help you solve your specific needs.
Examples:
- An Equipment Company: Our teams have worked with an Equipment Company to create a tool to help farmers predict harvest time. The projects have focused on creating an R package and script that will pull weather data for the fields of interest, and compute growing degree hours for those fields.
- A Dental Solutions Company: Our team partnered with a Dental Solutions Company to build an R package that automated their initial models, which supported their data science team in predicting customer turn to help sales managers have a more informed customer interaction on software usage.
And More!
If you have a business problem or inefficiency that you want to have explored, this is an awesome opportunity to have students get involved, get excited, and go to work on things without requiring a major time commitment on your part. So if you have an idea, get in touch and we can explore the possibilities.
