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CAN YOU PROVIDE SOME EXAMPLES OF CAPSTONE PROJECTS THAT PREVIOUS STUDENTS AT HUNTINGTON UNIVERSITY HAVE COMPLETED

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One popular type of capstone project for students in Huntington’s biology program is research-based projects where students design and conduct their own experiments investigating topics related to areas like cell biology, genetics, ecology or physiology. For example, one student investigated the effect of different light wavelengths on plant growth. She designed a controlled experiment growing the same type of plant under red, blue, green and full-spectrum LED lights, measuring factors like height, leaf and stem development over time. Her findings provided new insights into how specific light wavelengths can influence plant growth.

In the computer science department, many capstone projects involve developing software or web applications to solve real-world problems. One group created a web application for a local non-profit organization to better manage their volunteer roster and schedule shifts. The application allowed volunteers to sign up for tasks online, admins to track hours and see who was scheduled when. It streamlined their process and provided new analytics on volunteer participation. Another project involved developing a smartphone app for Huntington’s campus activities department. The app allowed students to view the schedule of upcoming campus events, get directions to event locations, and RSVP to attend. It enhanced communication and boosted attendance for campus activities.

In the exercise science program, many capstone projects take the form of original research studies. For example, one student investigated the effects of different post-workout nutrition options on muscle recovery following high-intensity interval training. She recruited volunteers from Huntington’s various athletic teams and had them follow specific nutrient plans after some intense workout sessions. Through metabolic analysis of muscle glycogen replenishment and questionnaires on soreness levels, she was able to determine which nutrients most effectively aided recovery. Her findings provided practical implications for athletes and trainers on optimizing recovery.

The nursing program encourages capstone projects that address real issues and needs within the local healthcare system. One group identified a gap in continuity of care for Alzheimer’s patients transitioning between facilities. They partnered with an area memory care facility and hospital to develop an electronic patient record system allowing smooth sharing of health details between locations. The tool reduced errors and stress on patients and families during transfers of care. Another nursing student conducted a community health needs assessment for Huntington’s local school corporation. Through surveys of students, families and teachers, they identified mental health and lack of nutrition education as primary concerns. Their report informed new wellness programs introduced at area schools.

The communication studies department often involves capstone projects that utilize student skills in public relations, advertising, journalism or film production. For example, one group developed a marketing campaign to increase tourism at a local state park that was facing declining visitor numbers. Their strategic campaign included promotional videos, print ads, social media content, and on-site activities they helped coordinate. Comprehensive analysis found their efforts directly contributed to a measurable increase in park visits over one summer. In another project, a student produced and directed a short documentary film profiling meaningful community organizations and volunteers in Huntington that have helped advance local welfare. The film aired on local access TV and helped spread awareness of their good work.

As these examples illustrate, capstone projects at Huntington University provide rich, hands-on experiences for students to conduct original research, develop solutions to practical problems, or generate other scholarly works that allow them to apply their classroom learning to real-world challenges. By designing their own distinctive capstone projects, students gain valuable skills in critical thinking, project management, communication and more that prepare them for workplace or graduate study success. The diversity of project types and partners with local businesses and organizations also demonstrates the commitment of Huntington students and faculty to enriching their surrounding community through engaged scholarship.

DO YOU HAVE ANY SUGGESTIONS FOR DATA ANALYTICS PROJECT IDEAS USING PYTHON

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Sentiment analysis of movie reviews: You could collect a dataset of movie reviews with sentiment ratings (positive, negative) and build a text classification model in Python using NLP techniques to predict the sentiment of new reviews. The goal would be to accurately classify reviews as positive or negative sentiment. Some popular datasets for this are the IMDB dataset or Stanford’s Large Movie Review Dataset.

Predicting housing prices: You could obtain a dataset of housing sales with features like location, number of bedrooms/bathrooms, square footage, age of home etc. and build a regression model in Python like LinearRegression or RandomForestRegressor to predict future housing prices based on property details. Popular datasets for this include King County home sales data or Boston housing data.

Movie recommendation system: Collect a movie rating dataset where users have rated movies. Build collaborative filtering models in Python like Matrix Factorization to predict movie ratings for users and recommend unseen movies. Popular datasets include the MovieLens dataset. You could create a web app for users to log in and see personalized movie recommendations.

Stock market prediction: Obtain stock price data for companies over time along with other financial data. Engineer features and build classification or regression models in Python to predict stock price movements or trends. For example, predict if the stock price will be up or down on the next day. Popular datasets include Yahoo Finance stock data.

Credit card fraud detection: Obtain a credit card transaction dataset with labels indicating fraudulent or legitimate transactions. Engineer relevant features from the raw data and build classification models in Python to detect potentially fraudulent transactions. The goal is to accurately detect fraud while minimizing false positives. Popular datasets are the Kaggle credit card fraud detection datasets.

Customer churn prediction: Get customer data from a telecom or other subscription-based company including customer details, services used, payment history etc. Engineer relevant features and build classification models in Python to predict the likelihood of a customer churning i.e. cancelling their service. The goal is to target high-risk customers for retention programs.

Employee attrition prediction: Obtain employee records data from an HR department including demographics, job details, salary, performance ratings etc. Build classification models to predict the probability of an employee leaving the company. Insights can help focus retention efforts for at-risk employees.

E-commerce product recommendations: Collect e-commerce customer purchase histories and product metadata. Build recommendation models to suggest additional products customers might be interested in based on their purchase history and similar customers’ purchases. Popular datasets include Amazon product co-purchases data.

Travel destination recommendation: Get a dataset with customer travel histories, destination details, reviews etc. Engineer features around interests, demographics, past destinations visited to build recommendation models to suggest new destinations tailored for each customer.

Image classification: Obtain a dataset of labeled images for a classification task like recognizing common objects, animals etc. Build convolutional neural network models in Python using frameworks like Keras/TensorFlow to build very accurate image classifiers. Popular datasets include CIFAR-10, CIFAR-100 for objects, MS COCO for objects in context.

Natural language processing tasks like sentiment analysis, topic modeling, named entity recognition etc. can also be applied to various text corpora like news articles, social media posts, product reviews and more to gain useful insights.

These are some ideas that could be implemented as data analytics projects using Python and freely available public datasets. The goal is to apply machine learning techniques with an understandable business problem or use case in mind. With projects like these, students can gain hands-on experience in the entire workflow from data collection/wrangling to model building, evaluation and potentially basic deployment.

CAN YOU PROVIDE EXAMPLES OF REAL WORLD DATASETS THAT STUDENTS HAVE USED FOR THE CAPSTONE PROJECT

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One of the most common types of datasets used is health/medical data, as it allows students to analyze topics that can have real-world impact. For example, one group of students obtained de-identified medical claim records from a large insurance provider covering several years. They analyzed the data to identify predictors of high medical costs and develop risk profiles that could help the insurance company better manage patient care. Some features they examined included diagnoses, procedures, prescriptions, demographics, and lifestyle factors. They built machine learning models to predict which patients were most at risk of future high costs based on their histories.

Another popular source of data is urban/transportation planning datasets. One project looked at public transit ridership patterns in a major city using anonymized tap-in/tap-out records from the city’s subway and bus systems. Students analyzed rider origins and destinations to identify the most traveled routes and times of day. They also examined how ridership changed on different days of the week and during major events. Their findings helped the city transportation authority understand demand and make recommendations on where to focus service improvements.

Education data is another rich area for capstone work. A group worked with a large statewide standardized test scores database containing student performance dating back over 10 years. They performed longitudinal analysis to determine what factors most strongly correlated with improvements or declines in test scores over time. Features they considered included school characteristics, class sizes, teacher experience levels, as well as student demographics. Their statistical models provided insight into what policies had the biggest impacts on student outcomes.

Some students obtain datasets directly from private companies or non-profits. For example, a retail company provided anonymous customer transactions records from their loyalty program. Students analyzed purchasing patterns and developed segments of customer groups with similar behaviors. They also built predictive models to identify good prospects for targeted marketing campaigns. Another project partnered with a medical research non-profit. Students analyzed their database of published clinical trials to determine what therapies were most promising based on completed studies. They also examined factors correlated with trials receiving funding or being terminated early. Their analyses could help guide the non-profit’s future research investment strategies.

While restricted real-world datasets aren’t always possible to work with, many students supplement private data projects with publicly available benchmark datasets. For example, the Iris flowers dataset, Wine quality dataset and Breast cancer dataset from the UCI Machine Learning Repository have all been used in student capstones. Projects analyze these and apply modern techniques like deep learning or make comparisons to historical analyses. Students then discuss potential applications and limitations if the models were used on similar real problem domains.

Some larger capstone projects involve collecting original datasets. For instance, education students designed questionnaires and conducted surveys of K-12 teachers and administrators in their state. They gathered input on professional development needs and challenges in teaching certain subjects. After analyzing the survey results, students presented strategic recommendations to the state department of education. In another example, engineering students gathered sensor readings from their own Internet-of-Things devices deployed on a university campus, collecting data on factors like noise levels, foot traffic and weather over several months. They used this to develop predictive maintenance models for campus facilities.

Real-world datasets enable capstone students to gain experience analyzing significant problems and generating potentially impactful insights, while also meeting the goals of demonstrating technical and analytical skills. The ability to link those findings back to an applied context or decision making scenario adds relevancy and value for the organizations involved. While privacy and consent challenges exist, appropriate partnerships and data access have allowed many successful student projects.

DO YOU HAVE ANY ADVICE ON HOW TO SELECT A TOPIC FOR A CAPSTONE PROJECT THAT ALIGNS WITH MY INTERESTS

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Choosing a topic for your capstone project that aligns with your personal interests and passions is key to remaining motivated throughout the intense research and writing process. With so many potential options it can be difficult to narrow down exactly the right focus. The following steps can help guide you to select a topic that you will find truly engaging and fulfilling.

Start by brainstorming a wide list of potential topics without censoring any ideas no matter how broad or obscure they may seem. Write down everything even remotely related to your major field of study or other academic interests that spark your curiosity. Reviewing hobbies, extracurriculars, jobs/internships, areas of the world, people, or time periods that fascinate you can spark new topic ideas. At this stage, go for quantity of ideas rather than worrying if each one is too big or narrow in scope.

Once you have an extensive list, analyze it for common themes or connections between certain topics. Group related ideas together into overarching categories to help narrow the focus. For example, if you enjoy history and have interests in both ancient Rome and Victorian London, you could potentially design a comparative study of those two time periods. Identifying logical similarities between broad topics can lead to more defined potential research areas or questions.

Assess each general topic area for feasibility within the parameters of a capstone project. Consider if there is enough available research on the subject to support extensive analysis within the required timeframe and page limit. You’ll want a topic that has depth and breadth of existing literature without being too vast. Checking with your academic advisor, librarians, and even reviewing bibliographies of previous students’ projects can help gauge feasibility. Very novel topics with little previous work carry more risk of not having adequate research to draw meaningful conclusions.

Evaluate your topic ideas based on how intrinsically engaging and inspiring you find the subject matter. A compelling personal passion will sustain the long hours of research required. Consider which topics continue interesting you the more you think about them versus ones that seem exciting in the moment but hold less fascination over time. Reflect on topics that make you want to keep learning more versus feeling like checking them off a list. Intrinsic excitement, not extrinsic goals or expectations, should drive topic selection.

Review your list of narrowed topics and consider real-world applications or ways any of the potential areas could create positive change. Having a sense of purpose behind your work can make the process even more valuable and rewarding. For example, a healthcare administration student passionate about mental health may choose to analyze ways to improve access to counseling services on college campuses. The potential for applied research outcomes to benefit society can further distinguish inspiring options.

As appropriate for your field of study, evaluate topics that may have career relevance in the future. While interest should be the primary driver, considering long-term implications can add practical value to your work. For example, an engineering student aiming for machine learning roles post-graduation may opt for a capstone proposal related to predictive data analytics applications. Career direction need not define the topic, but relevance can enhance your professional portfolio.

Once you have a shortlist of two to three options that meet criteria for feasibility, engagement, and application, discuss them candidly with your capstone instructor and academic advisor. Unbiased experts can provide insightful perspectives on logistics, literature quality, and strengths or limitations of each idea. Their questions may also uncover new angles to consider that had not yet occurred to you. Incorporating this critical feedback before deciding allows refining potentially promising concepts into the optimal research focus.

With open communication between yourself, instructors, and librarians, as well as an unwavering commitment to personal interests as the driver, following this exploratory process will lead to selecting a capstone topic that aligns passion and purpose. Choosing the right focus anchored intrinsically by what inspires your curiosity establishes an engaged mindset crucial to completing a compelling and impactful final research project. With this guidance, you are empowered to craft impactful work through diligent pursuit of your genuine academic passions.

DO YOU HAVE ANY TIPS FOR EFFECTIVELY PRESENTING A CAPSTONE PROJECT TO FACULTY AND STAKEHOLDERS

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First, you’ll want to prepare well in advance. Make sure you have a clear outline of the key points you want to cover so you stay organized and on track during your presentation. Spend time rehearsing your presentation out loud so you feel comfortable speaking about your project. Aim to have your presentation polished and refined after several practice runs.

Come up with a compelling opening that will grab your audience’s attention right away. You only have a limited amount of time, so an engaging introduction is crucial to set the right tone. Consider starting with an interesting fact, statistic, or scenario that establishes the relevance and importance of the work you did. This opening sets the stage for the rest of your presentation.

Be sure to clearly state the purpose and goals of your capstone project upfront. Define what problem or issue you sought to address and the objectives you established. Making your objectives explicit allows your audience to follow along and understand how and why you approached your project the way you did.

Provide some background context on the topic before delving into the key components of your work. Give your audience the necessary framework to comprehend the significance and complexity of the issue. You can discuss previous research, trends in the field, and why further exploration was needed. Painting this picture helps non-experts get up to speed.

Use visual aids judiciously and effectively. Include graphs, charts, images, or videos as appropriate – but only if they enhance comprehension rather than distract or overload the viewer. Well-designed visuals can help illustrate patterns and communicate messages more powerfully than words alone. Make sure any visual elements are readable from a distance.

Touch on your research methodology with just enough detail. Discuss the methods, tools, and processes you used while keeping explanations concise. Faculty need to know your work was rigorous and aligned with best practices, but stakeholders mainly care about the outcomes. Stick primarily to the most salient methodological aspects.

highlight your key findings and results through clear, compelling presentation of data. Analyze and interpret the most important and interesting outcomes of your work. Connect the dots from your objectives, through the approach and analysis, to the conclusions. Illustrate how the results addressed the issue at hand.

Tie your conclusions back to the big picture by discussing how your findings fit within the broader context and literature. Relate the implications and significance of your discoveries for both theory and practice. Consider directions for future research and applications stemming from your work. This level of synthesis and insight shows a deep understanding of the topic.

Leave ample time for questions by keeping your presentation timed appropriately. Most capstone advisors recommend limiting it to 15-20 minutes with another 5-10 minutes for Q&A. Practice keeping it on schedule. Field questions confidently by restating them concisely and linking responses back to your work. Ask for clarification if needed.

In your closing, summarize the key takeaways clearly and concisely while thanking your audience for their time and interest. Restate the importance of your work and its contributions. Provide a brief “call to action” if relevant for next steps. A polished conclusion leaves a strong lasting impression.

Practice good delivery techniques to engage your audience through your presentation. Make eye contact, vary your tone, and use dynamic body language and gestures judiciously. Smile, appear relaxed and confident, and exude passion for your topic to keep people’s attention. Rehearsal will help you deliver your capstone project presentation with impact and aplomb to faculty and stakeholders.

With thorough preparation, clear and compelling structure, appropriate use of visuals, strong data analysis and conclusions, engaging delivery techniques, and ability to field questions, you’ll be able to effectively communicate the value, insights and significance of your capstone project. Showcasing your excellent work in this impactful format is an excellent way to conclude your academic experience on a high note. I hope these tips provide helpful guidance as you prepare your capstone presentation.