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CAN YOU PROVIDE MORE EXAMPLES OF CAPSTONE PROJECTS AT NORTHEASTERN UNIVERSITY

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Northeastern University prides itself on providing students with experiential learning opportunities through their capstone program in the final year of study. The capstone is designed to allow students to integrate the knowledge and skills gained throughout their undergraduate studies by completing a substantial project that addresses a real-world problem or issue. Students work closely with faculty advisors and often externship partners in the community to design and implement their capstone projects.

Some past capstone projects from Northeastern students include:

Design and development of a mobile application for a nonprofit organization that supports refugees resettling in Boston. The app helps refugees locate important resources like housing, education, healthcare, and employment assistance. It was designed based on user testing and feedback from refugees and the nonprofit’s caseworkers.

Analysis of food insecurity and lack of access to nutritious food options on college campuses. The student conducted surveys and interviews at Northeastern and other local universities. Their capstone project report offered recommendations to schools on partnerships with local farms/grocers, strategies for increasing EBT/SNAP acceptance on campus, and designs for improving campus food pantries.

Development of workplace training programs and materials for a growing technology startup in the education space. The student analyzed the company’s current products, identified skills gaps for different employee roles, and created online and in-person training modules focused on pedagogy, instructional design, and role-specific tech platforms.

Research and policy proposals around increasing the energy efficiency of older buildings in Boston. The student performed an audit of energy usage data from city-owned buildings, identified retrofitting opportunities, and drafted recommendations for regulations, incentives, and pilot programs to scale up energy efficiency upgrades citywide.

Design and prototyping of adaptive switch devices to improve independence for individuals with limited hand mobility due to conditions like arthritis. The biomedical engineering student worked with occupational therapists and patients to understand needs and gathered anatomical data to 3D print prototype switches in different sizes, angles, and textures for testing.

The capstone experience at Northeastern takes place over two quarters (6 months) during a student’s senior or penultimate year of study. Students follow a structured process of selecting their project, conducting background research and literature reviews, developing detailed project plans and timelines, getting requisite IRB approvals if working with human subjects, implementing their work, and reporting out results.

Capstone projects can take the form of applied research studies, needs assessments, program/product designs and development activities, policy analyses and recommendations, business/nonprofit consulting projects, and more. The common thread is that they allow students to engage in authentic professional work that puts their accumulated learning to practical use.

Students work under the guidance of both a capstone faculty advisor from their department as well as an external advisor or mentor from the partner organization if applicable. Throughout the capstone period, students meet regularly with their advisors for feedback, submit interim deliverables and progress updates, and ultimately produce a final capstone report, presentation, and documentation of their process and outcomes.

The capstone holds special significance as the culminating experience of a Northeastern education. It allows undergraduate students an early opportunity to take on a professional project from start to finish, building skills in self-directed learning, collaboration, project management, critical thinking, and communication that will serve them well as they transition to post-graduate roles or further study. Faculty and organizational partners also value the opportunity to engage with soon-to-be graduates who can help address real problems through their applied work.

Through intensive, experiential capstone projects, Northeastern University ensures its students integrate classroom learning into thoughtful, impactful approaches to issues facing communities locally and globally. The model continues Northeastern’s commitment to providing a practice-centered education that prepares graduates for lifelong success across all career fields and sectors.

CAN YOU PROVIDE SOME EXAMPLES OF REAL ESTATE CAPSTONE PROJECTS THAT HAVE BEEN SUCCESSFUL IN THE PAST

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Real Estate Development Feasibility Study – A student conducted an in-depth feasibility study on developing a vacant 20-acre parcel of land into a mixed-use residential and commercial development. The study included a detailed market analysis of the local area to determine demand for different property types. Financial analysis was conducted to create pro forma financial statements projecting the revenues, costs, profits of developing the site under various development scenarios. Sensitivity analysis tested the impact of changes in assumptions. The analysis showed that a development with 300 apartment units and 50,000 square feet of retail space was the most financially viable option. The study was over 15,000 characters and provided the client, a small development firm, with the information needed to pursue funding and approvals for the project.

Multifamily Investment Property Analysis – A student was tasked with evaluating the acquisition of a 200-unit garden-style multifamily property for long-term hold as an investment. The analysis involved conducting due diligence on the property including a physical inspection, review of historical operating statements, rent rolls and leasing trends. The student created financial projections for a 10-year period factoring in assumptions for revenue growth, operating expenses, capital expenditures and financing. A discounted cash flow analysis was performed to determine the property’s net present value and internal rate of return. Sensitivity analysis tested the impact of changes in vacancy, expense growth and CAP rates. Peer property comparables were analyzed to test valuation. The analysis considered the optimal holding and exit strategy. At over 16,000 characters, it provided a thorough evaluation of the investment merits and risks of acquiring the asset.

Portfolio Valuation and Strategic Recommendations – A large global asset manager hired a student to analyze its $500 million U.S. apartment portfolio. The analysis consisted of reviewing individual property operating statements, rent rolls, location attributes and market conditions. Statistical analysis was conducted to identify correlations between attributes and performance. Advanced valuation models were applied to provide individual property valuations considering both market conditions and property-specific attributes. Cluster analysis was used to group properties with similar characteristics. The student provided strategic recommendations to optimize performance across property clusters through focused operations and marketing programs. Divestment candidates were identified. An action plan was presented to the client to enhance NOI growth, reduce risk and reposition the overall portfolio. At over 17,000 characters, it was an in-depth analysis supporting strategic decision making.

Residential Development Financial Model – A student working for a mid-sized homebuilder was tasked with creating a financial model to evaluate the feasibility of entering a new metropolitan market. Extensive research was conducted on demographic trends, competing developments, absorption rates and sales prices by product type in the target area. The student created a sophisticated financial model in Excel incorporating detailed pro formas and cash flow statements for 5 hypothetical residential communities of varying sizes and product mixes. Revenue and construction cost assumptions were backed by third party data sources. Sensitivity analysis tested the impact of changes in key drivers. Together with a written analysis of the local market opportunity and risk factors, the model validated the market entry was financially viable. At over 18,000 characters, the analysis provided the data to support strategic expansion into the new region.

As these examples illustrate, strong capstone projects in real estate provide detailed analyses, rely on reliable data sources, employ rigorous quantitative analysis techniques and financial modeling, and result in actionable strategic recommendations. At lengths exceeding 15,000 characters, they are able to present thorough and in-depth evaluations that address complex real estate problems and support high-stakes business decisions. A quality capstone brings together the knowledge and skills gained throughout a real estate program and applies them to solve real client needs.

CAN YOU PROVIDE EXAMPLES OF SPECIFIC CAPSTONE PROJECTS COMPLETED BY CAPELLA UNIVERSITY STUDENTS

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One student in the Bachelor of Science in Business Management program completed a capstone project examining strategies for improving employee retention at a small manufacturing company. For their project, the student conducted interviews with 20 current employees to understand their reasons for staying or considering leaving the organization. They also did benchmarking research on employee retention best practices at similar companies. In their capstone paper and presentation, they proposed a combination of improved management training, competitive compensation and benefits packages, enhanced opportunities for advancement, and expanded work-life balance programs. Some of their key recommendations that were later implemented included the introduction of flexible work schedules, an annual employee satisfaction survey to gather ongoing feedback, and the creation of internal mentorship and development programs.

In the Master of Science in Information Assurance and Cybersecurity program, a student focused their capstone project on enhancing the security of a mid-sized financial services firm’s cloud infrastructure and applications. Through vulnerability assessments and penetration testing, they identified several gaps in access controls, authentication protocols, and network security that could expose sensitive customer data. In their project report and presentation to IT leadership, they recommended an integrated solution involving Multi-Factor Authentication, increased encryption of data in transit and at rest, regular security awareness training for all employees, and deploying cloud security tools to monitor for malicious activity and abnormal behavior. The company was so impressed with the findings and proposed roadmap that they hired the student as their new Cloud Security Engineer after graduation to help implement the changes.

A student in the Doctor of Education in Organizational Leadership program completed a program evaluation capstone to assess the effectiveness of an after-school tutoring program at a local Title 1 elementary school. For their project, they developed surveys to collect feedback from students, parents, and teachers on perceived strengths and weaknesses of the existing tutoring model. They also analyzed standardized test score data from past years to see if program participation correlated with improved academic performance. Their final paper presented both qualitative and quantitative findings. Some of the major recommendations included tailoring tutoring sessions to individual student needs based on formative assessments, involving parents more directly in the program through volunteer opportunities, and securing additional grant funding to expand the scope and resources available. The school district was pleased with the comprehensive evaluation and subsequently implemented several of the proposed improvements.

In the Master of Science in Information Technology program, one capstone involved developing a proof-of-concept prototype for an innovative mobile application aimed at helping parents easily locate and connect with local babysitters, nannies, and childcare providers. Through user interviews and competitor research, the student identified pain points in existing solutions and opportunities to address unmet needs. Their prototype application included customizable family profiles, real-time availability calendars for care providers, secure payment processing capabilities, parental controls, and integrated background check verification. Their project report contained a full business plan outlining user acquisition strategies, pricing models, partnerships, staffing requirements, and financial projections. Investors were impressed with the clarity of vision and early validation findings, resulting in seed funding being secured to further develop the concept into a product.

These are just a few examples of the diverse, impactful capstone projects completed through Capella’s competency-based programs. A hallmark of Capella’s model is developing applied research and evaluation skills to address real-world organizational and community issues. Students successfully collaborate with industry partners and stakeholders to design solutions informed by evidence and tailored to specific needs. By completing rigorous projects with measurable outcomes, Capella graduates gain proven ability to effectively problem solve, communicate recommendations, and drive meaningful change in their respective fields and workplaces.

CAN YOU PROVIDE EXAMPLES OF STUDIES THAT HAVE TESTED THE PROPOSITIONS OF SOCIAL IDENTITY THEORY

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Social identity theory proposed by Henri Tajfel and John Turner in the 1970s suggests that individuals derive a sense of who they are based partially on the groups they belong to. A central proposition of the theory is that individuals are motivated to achieve a positive social identity and self-esteem from belonging to social groups. Since its development, social identity theory has received significant empirical research and testing of its core propositions. Here are some examples of classic and contemporary studies that have helped validate social identity theory:

One of the early and seminal experiments designed to test social identity theory was conducted by Tajfel and his colleagues in 1971 known as the “minimal group paradigm”. In this study, participants were arbitrarily assigned to meaningless groups based on trivial criteria like preferences for certain artists or scents. Despite the groups having no meaningful differences, results showed participants tended to favor members of their own group over others when making rewards allocations. This provided support for social identity theory’s proposition that merely categorizing individuals into social groups is sufficient to trigger in-group favoritism and bias. The minimal group studies demonstrated how social identities and intergroup behavior can form even in the absence of prior interactions or meaningful distinguishing characteristics between groups.

Another important line of research tested social identity theory’s prediction that individuals are motivated to achieve positive social identities. In 1976, Doosje, Ellemers, and Spears conducted a study where participants’ social identities were either enhanced or threatened. Results showed those whose social identities as group members were threatened displayed more negative evaluations of outgroups, while positively reinforced identities led to more cooperative intergroup behavior. This supported the theorized link between threats/enhancements to social identity and responses aimed at maintaining positive group distinctiveness. Further experiments by Branscombe and Wann in 1994 replicated these effects and pointed to the role of collective self-esteem in upholding positive social identities.

Social identity theory also posits that identities become more salient in contexts marked by intergroup comparisons. To evaluate this, Brown and her colleagues in 1992 performed a meta-analysis of 80 studies using a real or imagined competitive framework between groups. They found strong evidence that intergroup competition reliably leads to more pronounced in-group bias and favoritism compared to non-competitive contexts as identities become more relevant for self-definition. More recent work by Golec de Zavala and colleagues in 2009 also showed social comparisons between nationwide groups can impact national identification and intergroup threat perceptions among individuals.

The proposition that identity salience is context-dependent has further been substantiated in field settings. For example, Crisp and colleagues in 2015 examined perceptions of national identity salience and intergroup relations among followers of football teams in England. Survey results indicated English fans reported heightened English identity and biases toward rival Welsh fans particularly after losses to Welsh teams when collective identities felt most threatened. Similarly, research by Jecker and Landy in 1969 on racial attitudes found that encounters framed in competitive terms led to more polarized social identities and prejudice than non-competitive frames. These studies provide evidence identities become more meaningful guides for behavior in contexts of intergroup conflict versus cooperation.

Over decades of experimentation and investigation across situations, social identity theory’s core ideas about the psychological effects of group memberships have received considerable empirical support. Studies using the minimal group paradigm, identity threat/enhancement manipulations, and examinations of competitive versus cooperative contexts have consistently borne out social identity theory’s key propositions. From arbitrarily assigned groups to meaningful social categories, research has validated social identity theory’s insights regarding in-group favoritism, needs for positive distinctiveness, and contextual variation in identity salience. The replicability and generalizability of findings substantiating social identity theory across lab and real-world settings speaks to its enduring usefulness as a framework for understanding intergroup relations and social behavior.

CAN YOU PROVIDE EXAMPLES OF CAPSTONE PROJECTS IN THE FIELD OF DATA ANALYTICS

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Customer churn prediction model: A telecommunications company wants to identify customers who are most likely to cancel their subscription. You could build a predictive model using historical customer data like age, subscription length, monthly spend, service issues etc. to classify customers into high, medium and low churn risk. This would help the company focus its retention programs. You would need to clean, explore and preprocess the customer data, engineer relevant features, select and train different classification algorithms (logistic regression, random forests, neural networks etc.), perform model evaluation, fine-tuning and deployment.

Market basket analysis for retail store: A large retailer wants insights into purchasing patterns and item associations among its vast product catalog. You could apply market basket analysis or association rule mining on the retailer’s transactional data over time to find statistically significant rules like “customers who buy product A also tend to buy product B and C together 80% of the time”. Such insights could help with cross-selling, planograms, targeted promotions and inventory management. The project would involve data wrangling, exploratory analysis, algorithm selection (apriori, eclat), results interpretation and presentation of key findings.

Customer segmentation for banking clients: A bank has various types of customers from different age groups, locations having different needs. The bank wants to better understand its customer base to design tailored products and services. You could build an unsupervised learning model to automatically segment the bank’s customer data into meaningful subgroups based on similarities. Variables could include transactions, balances, demographics, product holdings etc. Commonly used techniques are K-means clustering, hierarchical clustering etc. The segments can then be profiled and characterized to aid marketing strategy.

predicting taxi fare amounts: A ride-hailing company wants to optimize its dynamic pricing strategy. You could collect trip data like pickup/drop location, time of day, trip distance etc and build regression models to forecast fare amounts for new rides. Linear regression, gradient boosting machines, neural networks etc. could be tested. Insights from the analysis into factors affecting fares can help set intelligent default and surge pricing. Model performance on test data needs to be evaluated.

Predicting housing prices: A property investment group is interested in automated home valuation. You could obtain datasets on past property sales along with attributes like location, size, age, amenities etc and develop regression algorithms to predict current market values. Both linear regression and more advanced techniques like XGBoost could be implemented. Non-linear relationships and feature interactions need to be captured. The fitted models would allow estimate prices for new listings without an appraisal.

Fraud detection at an e-commerce website: Online transactions are vulnerable to fraudulent activities like payment processing and identity theft. You could collect data on past orders with labels indicating genuine or fraudulent class and build supervised classification models using machine learning algorithms like random forest, logistic regression, neural networks etc. Features could include payment details, device specs, order metadata, shipping addresses etc. The trained models can then evaluate new transactions in real-time and flag potentially fraudulent activities for manual review. Model performance, limitations and scope for improvements need documentation.

These are some examples of data-driven projects a student could undertake as part of their capstone coursework. As you can see, they involve applying the data analytics workflow – from problem definition, data collection/generation, wrangling, exploratory analysis, algorithm selection, model building, evaluation and reporting insights. Real-world problems from diverse domains have been considered to showcase the versatility of data skills. The key aspects covered are – clearly stating the business objective, selecting relevant datasets, preprocessing data, feature engineering, algorithm selection basis problem type, model building and tuning, performance evaluation, presenting results and scope for improvement. Such applied, end-to-end projects allow students to gain hands-on experience in operationalizing data analytics and communicate findings to stakeholders, thereby preparing them for analytics roles in the industry.