Tag Archives: capstone

WHAT ARE SOME EXAMPLES OF RARE PEDIATRIC CANCERS THAT COULD BE THE FOCUS OF A CAPSTONE PROJECT

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Rare cancers that affect children are of particular interest for capstone projects because they often receive less research funding and attention compared to more common adult cancers. Developing a deeper understanding of the molecular mechanisms, treatments, and patients’ experiences with rare pediatric cancers can help advance care for these vulnerable populations. Here are some examples of rare pediatric cancers that would be suitable topics for an in-depth senior or graduate-level capstone project:

Neuroblastoma is a rare cancer that forms in certain types of nerve tissue and most commonly appears in young children, often presenting in the adrenal glands, chest, abdomen or neck. It accounts for around 15% of all childhood cancers but less than 1% of all cancers diagnosed. Despite being rare, neuroblastoma is responsible for more deaths among children with solid tumors than any other cancer. A capstone project could explore new targeted therapies and immunotherapies in development for high-risk neuroblastoma. The student could conduct a literature review of recent clinical trials and analyze molecular markers to identify patient subgroups most likely to respond to certain treatments. Understanding the genetics and biology of neuroblastoma in more detail could help accelerate the development of personalized, precision medicine approaches.

Ewing sarcoma is the second most common bone cancer in children after osteosarcoma. It remains quite rare, accounting for less than 1% of all cancers and 3% of childhood cancers. Ewing sarcoma most often appears in bones of the pelvis, legs, chest, or spine and is characterized by translocations linking the EWS gene to an ETS family gene. A capstone project on Ewing sarcoma could comprehensively review past and current standard of care therapies, while also evaluating promising new targeted drugs and immunotherapies in preclinical and early phase clinical testing. Interviews with patients, families and clinicians could provide insights into the challenges of living with and treating this aggressive bone cancer. Identifying biomarkers for early detection and response to treatment is another important area warranting further research highlighted by such a project.

Rhabdomyosarcoma is a type of soft tissue sarcoma that develops from skeletal muscle cells or muscles in other parts of the body. It represents about 3-4% of all childhood cancers but is still considered rare. The most common locations are the head and neck region, genitourinary tract, and extremities. Subtypes include embryonal, alveolar and pleomorphic. A capstone project could focus specifically on the more aggressive alveolar subtype, analyzing its distinctive genetic mutations and exploring combination therapies to overcome resistance. The student might profile a series of alveolar rhabdomyosarcoma cases at their institution to identify clinical or molecular characteristics associated with improved outcomes. Interviews with long-term survivors could offer unique perspectives on the emotional and physical impacts as well as care needs over time.

Atypical teratoid/rhabdoid tumor (AT/RT) is an extremely rare and highly malignant type of cancerous brain tumor that primarily affects young children. It develops from cells in the central nervous system and has a very poor prognosis despite intensive multimodal therapy. AT/RT represents less than 1% of all pediatric central nervous system tumors but is the focus of considerable research efforts given its lethal nature. A project delving into the molecular hallmarks and epigenetic dysregulation characteristic of AT/RT could survey targeted agents in preclinical testing and early stage clinical trials. Collaboration with neuro-oncologists may provide access to tumor samples for exploring biomarkers of sensitivity and resistance. Investigating supportive care interventions and quality of life for patients undergoing complex treatment regimens could also yield important insights.

Wilms tumor, also known as nephroblastoma, begins in the kidneys and is the most common malignant tumor of the kidneys in children. It represents approximately 6% of all childhood cancers yet remains defined as a rare cancer. Wilms tumor is usually found in children younger than 5 years old, with 80-90% of cases arising before the age of 6. A capstone topic could extensively review protocols from cooperative clinical trials groups to analyze factors influencing event-free survival overtime. The student might conduct interviews with nursing professionals and child life specialists to gain perspective on psychosocial support needs throughout the patient journey. Exploration of genomic characterization efforts aimed at more precisely stratifying risk could also yield valuable insights for precision oncology approaches.

Rare pediatric cancers like neuroblastoma, Ewing sarcoma, rhabdomyosarcoma, AT/RT and Wilms tumor present opportunities for in-depth capstone study. Delving into disease biology, therapeutic developments, clinical research challenges, and patient/family experiences could advance understanding and care for these underserved populations. With a comprehensive literature review augmented by primary data collection, a student could produce an original research project meaningfully contributing to progress against devastating pediatric cancers.

WHAT ARE THE PREREQUISITES FOR ENROLLING IN THE WHARTON BUSINESS ANALYTICS CAPSTONE COURSE

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The Wharton Business Analytics Capstone course at the University of Pennsylvania is typically taken during a student’s final semester before graduating with their Bachelor of Science in Economics degree from Wharton. As the culminating course in Wharton’s Business Analytics concentration, the capstone aims to provide students hands-on experience in integrating the various business analytics skills and techniques they have learned throughout their prior coursework.

Given its advanced role in the business analytics curriculum, several prerequisites must be fulfilled before a student can enroll in the capstone course. Chief among these is the completion of the introductory and core business analytics classes. Students are required to have successfully finished the following four courses:

STAT 101 – Introduction to Statistics and Data Analysis
This entry-level course introduces students to core statistical concepts and methods used for business analytics. Key topics covered include probability distributions, statistical inference, regression analysis, and experimental design. Successful completion of STAT 101 demonstrates a student has obtained foundational statistical literacy.

OPIM 210 – Introduction to Marketing and Supply Chain Analytics
As a follow-up to STAT 101, OPIM 210 provides an overview of marketing and supply chain analytics applications. Students learn how to synthesize and analyze customer data, optimize inventory levels, and predict product demand using statistical techniques. Completing this course verifies students can apply statistics in business contexts.

OPIM 303 – Introduction to Analytics Modeling
OPIM 303 delves into predictive modeling methodologies central to business analytics such as logistic regression, decision trees, and time series forecasting. Students gain hands-on experience building models in R and interpreting results. Passing this class confirms a student’s proficiency with analytics modeling workflows.

OPIM 475 – Data Analysis and Prediction
The capstone’s direct prerequisite, OPIM 475 explores advanced analytics topics like unsupervised learning, recommender systems, and machine learning algorithms. Students apply their knowledge to a major semester-long business case requiring data wrangling, exploratory analysis, and model development. Passing this course demonstrates a student’s readiness for the capstone.

In addition to the core analytics course prerequisites, students must also have completed the associated lab sections that accompany STAT 101, OPIM 210, and OPIM 303. These half-credit labs give students supplementary practice implementing analytic methods in software like R, Python, SQL, and Tableau. Completing the labs ensures students have experience using analytics tools that will be heavily relied upon in the capstone.

To gain the full benefit of the project-focused capstone experience, students are recommended to have completed additional courses from Wharton’s business curriculum covering functions like finance, accounting, marketing, and operations. Exposure to these business domains helps students apply their analytics skills to solving real-world management problems. While no specific business courses beyond the core are mandatory, exposure is encouraged.

The culminating capstone course challenges students to integrate their business analytics training through a large team-based consulting project with a corporate partner. Students must also have senior standing, meaning they need to have accumulated at least 90 credits, to ensure sufficient time remains after the capstone to complete their degree. This senior standing prerequisite not only guarantees students’ availability to devote significant effort to the semester-long project but also verifies their general readiness to transition into industry upon graduation.

Once all the prerequisite coursework and senior standing are confirmed, student admission into the capstone is still not guaranteed, as spots are limited each semester to facilitate close faculty supervision of projects. Students must apply during the preceding semester by submitting their academic transcripts, resumes, and statements of interests. Admission is competitive based on prior academic performance in the core analytics classes. A minimum cumulative 3.3 GPA is also usually required to ensure students have demonstrated excellent analytical skills and problem-solving abilities.

To enroll in Wharton’s Business Analytics Capstone course, students must fulfill several prerequisites demonstrating their extensive training and high proficiency in the business analytics concentration. The core coursework requirements in statistics, predictive modeling, and data analysis provide theoretical foundations. Additional labs and business exposure offer practical tools and contexts. And senior standing verifies availability to fully engage in the multifaceted capstone consulting project experience. These comprehensive prerequisites ensure students enter the capstone well-equipped to excel and gain tremendous hands-on value from applying their analytics skills to solve real business problems.

CAN YOU PROVIDE MORE EXAMPLES OF CAPSTONE PROJECT IDEAS FOR A MASTER’S IN NURSING

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One idea would be to conduct a quality improvement project at the medical facility where you work. For example, you could focus on improving patient outcomes for a particular diagnosis or medical condition. You would research best practices and develop an evidence-based intervention aimed at enhancing care processes or the standard of care. Some options may include implementing a new screening or assessment tool, developing an education program for patients or staff, creating a standardized treatment protocol, or utilizing technology like telehealth in a new way.

As part of your project, you would need to gather baseline data on the current outcomes and develop measurable goals for improvement. Then you would implement your intervention and evaluate the impact over a designated time period, analyzing post-intervention data to determine if your goals were met. The project should utilize nursing theory and leadership skills to strategically plan and execute the change. Your final paper would thoroughly document the evidence and steps taken, and reflect on the successes and limitations experienced. If successful, the quality improvement could potentially be sustained in your organization.

Another strong option would be to explore a topic related to nursing education through a program evaluation or curriculum development project. For instance, you may analyze the effectiveness of teaching methods or clinical placements in your nursing program by developing surveys for students and faculty. Based on the feedback and research, you could then design revisions to strengthen areas identified as opportunities. Alternatively, you could create an entirely new continuing education module, online course, or simulation experience for practicing nurses on an emerging healthcare issue.

The proposed changes would need to be supported by relevant literature and align with accreditation standards. Your role would be obtaining necessary approvals, implementing the educational intervention, and assessing outcomes such as knowledge gained, skill enhancement, or perceived impact on nursing practice. Besides reporting the evaluation results, your completed capstone would provide recommendations for integrating lessons learned on a longer-term basis. By addressing a real need in your university or health system, the project has potential to positively influence nursing education.

Nursing research is another broad category that lends itself well to capstone topics. You may choose to perform a quantitative, qualitative, or mixed methods study related to your specialty area. Some examples could be exploring nurses’ perceptions of a workplace issue through surveys and interviews, evaluating a relationship between nursing interventions and patient outcomes over time, or pilot testing an innovative care model to manage a health condition.

The research process would involve developing a well-articulated purpose statement and aims, creating a thorough literature review, obtaining necessary approvals from your Institutional Review Board, implementing planned recruitment strategies and data collection methods, analyzing quantitative and qualitative findings, and interpreting results within the scope of current evidence. Your final report would discuss how the new knowledge can advance nursing practice or be built upon in future scholarship. Conducting an original research study allows for making a scholarly contribution while strengthening critical inquiry skills.

A policy analysis could also serve as a relevant capstone project. You might examine an existing law, regulation, clinical practice guideline or position statement influencing nursing and healthcare delivery. Through legislative records review, evaluating stakeholder perspectives, and comparing to supportive research, you would aim to understand both intended and unintended consequences of the policy since implementation. Based on gaps identified, the analysis could then inform recommendations for revisions or areas requiring further monitoring and evaluation.

Besides implications at the organizational level, well-designed policy work sheds light on real world issues impacting patient outcomes and the nursing profession as a whole. Your policy paper would need to utilize an approved framework and have potential to influence future decision making if shared with stakeholders. Tackling a current clinical or systemic problem through policy change aligns well with nursing leadership and systems-based competencies.

The key aspects of a strong capstone project involve systematically planning and executing a scholarly work that addresses a relevant nursing practice or healthcare delivery issue. While topic ideas may vary, components such as a literature review, application of theory, development or evaluation of an intervention, data collection and analysis, discussion of results and conclusions all help demonstrate mastery of MSN program outcomes. Regardless of specific focus area, the depth, rigor and applicability of your final written report is what ultimately signifies preparedness for advanced nursing practice at the graduate level. With sufficient preparation and faculty guidance, the preceding examples provide a starting point for selecting a meaningful capstone experience.

CAN YOU PROVIDE MORE EXAMPLES OF CAPSTONE PROJECTS IN THE FIELD OF DATA SCIENCE AND ANALYTICS?

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Customer churn prediction model.

One common capstone project is building a predictive model to identify customers who are likely to churn, or stop doing business with a company. For this project, you would work with a large dataset of customer transactions, demographics, service records, surveys, etc. from a company. Your goal would be to analyze this data to develop a machine learning model that can accurately predict which existing customers are most at risk of churning in the next 6-12 months.

Some key steps would include: exploring and cleaning the data, performing EDA to understand customer profiles and behaviors of churners vs non-churners, engineering relevant features, selecting and training various classification algorithms (logistic regression, decision trees, random forest, neural networks etc.), performing model validation and hyperparameter tuning, selecting the best model based on metrics like AUC, accuracy etc. You would then discuss optimizations like targeting customers identified as high risk with customized retention offers. Additional analysis could involve determining common reasons for churn by examining comments in surveys. A polished report would document the full end to end process, conclusions and business recommendations.

Customer segmentation analysis.

In this capstone, you would analyze customer data for a retail company to develop meaningful customer segments that can help optimize marketing strategies. The dataset may contain thousands of customer profiles with demographics, purchase history, channel usage, response to past campaigns etc. Initial work would involve data cleaning, feature engineering and EDA to understand natural clustering of customers. Unsupervised learning techniques like K-means clustering, hierarchical clustering and latent semantic analysis could be applied and evaluated.

The optimal number of clusters would be selected using metrics like silhouette coefficient. You would then profile each cluster based on attributes, labeling them meaningfully based on behaviors. Associations between cluster membership and other variables would also be examined. The final deliverable would be a report detailing 3-5 distinct and actionable customer personas along with recommendations on how to better target/personalize offerings and messaging for each group. Additional analysis of churn patterns within clusters could provide further revenue optimization strategies.

Fraud detection in insurance claims.

Insurance fraud costs companies billions annually. Here the goal would be to develop a model that can accurately detect fraudulent insurance claims from a historical claims dataset. Features like claimant demographics, details of incident, repair costs, eyewitness accounts, past claim history etc. would be included after appropriate cleaning and normalization. Sampling techniques may be used to address class imbalance inherent to fraud datasets.

Various supervised algorithms like logistic regression, random forest, gradient boosting and deep neural networks would be trained and evaluated on metrics like recall, precision and AUC. Techniques like SMOTE for improving model performance on minority classes may also be explored. A GUI dashboard visualizing model performance metrics and top fraud indicators could be developed to simplify model interpretation. Deploying the optimal model as a fraud risk scoring API was also suggested to aid frontline processing of new claims. The final report would discuss model evaluation process as well as limitations and compliance considerations around model use in a sensitive domain like insurance fraud detection.

Drug discovery and molecular modeling.

With advances in biotech, data science is playing a key role in accelerating drug discovery processes. For this capstone, publicly available gene expression datasets as well as molecular structure datasets could be analyzed to aid target discovery and virtual screening of potential drug candidates. Unsupervised methods like principal component analysis and hierarchical clustering may help identify novel targets and biomarkers.

Techniques in natural language processing could be applied to biomedical literature to extract relationships between genes/proteins and diseases. Cheminformatics approaches involving property prediction, molecular fingerprinting and substructure searching could aid in virtual screening of candidate molecules from database collections. Molecular docking simulations may further refine candidates by predicting binding affinity to protein targets of interest. Lead optimization may involve generating structural analogs of prioritized molecules and predicting properties like ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles.

The final report would summarize key findings and ranked drug candidates along with discussion on limitations of computational methods and need for further experimental validation. Visualizations of molecular structures and interactions may help communicate insights. The project aims to demonstrate how multi-omic datasets and modern machine learning/AI are revolutionizing various stages of drug development process.

WHAT ARE SOME IMPORTANT FACTORS TO CONSIDER WHEN SELECTING AN AI CAPSTONE PROJECT

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When selecting a capstone project for your AI studies, there are several important factors to take into consideration to help ensure you pick a meaningful project that allows you to demonstrate your skills and that you will find engaging and rewarding to work on. The project you choose will be the culmination of your AI learning thus far and will leave a lasting impression, so it is important to choose carefully.

The first key factor is to select a project that genuinely interests you. You will be spending a significant amount of time researching, developing, and implementing your capstone project over several months, so make sure the topic captivates your curiosity. Choosing a project that intrigues you intellectually will better maintain your motivation through challenges and setbacks. It is easy to lose steam if you feel disconnected from your work. Selecting a domain that matches your own personal interests or fields you are passionate about learning more about can help tremendously with sustaining focus and effort to project completion.

Secondly, consider a project that is appropriately scoped and can realistically be finished within the allotted timeframe. An overambitious idea may sound exciting but could render unsatisfying results or even result in an incomplete project if the timeline is unrealistic. Discuss your ideas with your capstone advisor to get feedback on feasibility. Smaller, well-defined problems within a domain are generally better than broad, loosely framed ones. That said, the work should still allow application of appropriate AI techniques and demonstrate skills learned. Finding the right balance of scale and challenge is important.

Another key deliberation is selection of a project domain or application area that has relevance and potentially useful impact. Examples could include areas like healthcare, education, sustainability, transportation, assistive technologies and so on. impactful applications tend to be more motivating and can open up potential for future work. They also better simulate real-world machine learning scenarios. Avoid very narrow or niche problems unless there is a clear path toward broader implications. The work should in some way advance AI capabilities and potentially benefit others.

Assessment criteria your capstone project will be evaluated on is also an important factor. Strong consideration should be given to selecting a project that will allow you to showcase a broad range of machine learning skills and knowledge gained throughout your studies. Make sure the selected idea provides opportunity for implementing multiple techniques, like various models, embedding approaches, neural architectures, optimization methods, evaluation strategies and so on based on the problem. Capstone projects are aimed to assess comprehensive mastery of core AI principles and methods.

The availability of appropriate, high-quality datasets is another critical logistical factor that must be carefully planned for early on. Gathering and cleaning data consistent with your research questions can consume significant portions of a project timeline. Public datasets may not fully address your needs or goals. You will need to realistically assess your ability to acquire necessary data of adequate size, quality and relevance before finalizing a project idea. If needed datasets seem uncertain or out of reach, it may be wise to modify project ideas or scopes accordingly.

Beyond technical factors, consider how to design your project to clearly communicate your work to others. Excellent documentation, reporting and presentation skills are just as important. Select an idea that lends itself well to visualizations, demonstrations, papers, videos and oral defenses that can help evaluate mastery of explaining complex technical concepts. The ability to relate your work to important societal issues will also serve you well for industr, assessments and future career opportunities. Choosing a project focused explicitly in an area of personal or societal benefit can facilitate compelling storytelling.

Make sure to check that your capstone project idea selections do not overlap substantially with existing research literature. While building on prior work is expected, evaluators want to see new innovative ideas or applications of techniques. Be sure to research what has already been done within your proposed domain to identify novel directions or problems to explore that expand the current frontier of knowledge. Significant redundancy of published findings or very minor extensions could diminish perceived scholarly contribution.

When selecting an AI capstone project, key factors to heavily weigh include your intrinsic interest in the domain, realistic scoping, relevance, assessment criteria alignment, data availability, communication strengths, novelty, and feasibility within time constraints. With careful consideration of these numerous determining elements, you can match yourself with a project that allows the most meaningful demonstration of your machine learning abilities while remaining engaging and set up for success. The project chosen will be the culmination of your studies thus far, so choosing wisely is paramount for an optimal capstone experience and outcome.