Natural Language Processing (NLP)

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:

• Wilford Woodruff Papers 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 Wilford Woodruff’s journals throughout his life, correlating them to different locations and people he wrote about, among other topics.

Sentiment Analysis

Our teams can help you analyze digital text to determine if the emotional tone of the message is positive, negative, or neutral. With large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews, we can help you confidently make sense of and respond to your customers’ feedback.

Examples:

• Wilford Woodruff Papers 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 Wilford Woodruff’s journals throughout his life, correlating them to different locations and people he wrote about, among other topics.

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:

• MOCA Systems: 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:

• MOCA Systems: 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.
• Good2Go: In previous semesters, student consulting teams have worked with Good2Go to develop store placement models, forecasting models to predict sales for the next quarter, analyze and report on each of the Good2Go store locations’ quality based on customer feedback, and develop dashboards that displayed optimal locations for new Good2Go stores.

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:

• Good2Go: In previous semesters, student consulting teams have worked with Good2Go to develop store placement models, forecasting models to predict sales for the next quarter, analyze and report on each of the Good2Go store locations’ quality based on customer feedback, and develop dashboards that displayed optimal locations for new Good2Go 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:

• Imagine Learning: The student teams found, scrapped, and processed data from over 2000 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.
• Agricultural Economics Department: Our teams worked with the Agricultural Economics Department at BYU-I 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.

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:

• Idaho Falls School District: This semester, the student consulting team has connected to the Idaho Falls School District data and is working on designing reports through Power BI to create dashboards. Data covers student performance, enrollment and attendance, human resources, management and maintenance overview.
• Idaho Steel: 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.

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:

• iWorQ: Our student teams worked with iWorQ, a leading provider of software solutions to local government agencies, to develop a dashboard. 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.

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:

• Westmark 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.
• PW Communications: 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.

Building R Packages

Our team can help you build R packages to help you solve your specific needs.

Examples:

• Stotz Equipment: Our teams have worked with Stotz 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.