One of the biggest challenges students face is acquiring and managing large datasets. Big data projects by definition work with massive amounts of data that can be difficult to store, access, and process. This presents issues around finding suitable datasets, downloading terabytes of data, cleaning and organizing the data in databases or data lakes, and developing the computing infrastructure to analyze it. To overcome this, students need to start early in researching available public datasets or working with industry partners who can provide access. They also need training in setting up scalable storage, like Hadoop and cloud services, and using data processing tools like Spark.

After acquiring the data, students struggle with exploring and understanding such large datasets. With big data, it is difficult to gain a holistic view or get a sense of patterns and relationships by manually examining rows and columns. Students find it challenging to know what questions to ask of the data and how to visualize it since traditional data analysis and visualization methods do not work at that scale. Devising sampling or aggregation strategies and learning big data visualization tools can help students make sense of large datasets and figure out what hidden insights they may contain.

Modeling and analysis are other problem areas. Students lack experience applying advanced machine learning and deep learning algorithms at scale. Training complex models on massive datasets requires significant computing power that may be unavailable on a personal computer. Students need hands-on practice with distributed processing frameworks to develop and tune algorithms. They must also consider challenges like data imbalance, concept drift, feature engineering at scale, and hyperparameter tuning for big data. Getting access to cloud computing resources through university programs or finding an industry partner can help students overcome these issues.

Project management also becomes an issue for big data projects which tend to have longer timelines and involve coordination between multiple team members and moving parts. Tasks like scheduling iterations, tracking deadlines, standardizing coding practices, debugging distributed systems, and documenting work become exponentially more difficult. Students should learn principles of agile methodologies, establish standard operating procedures, use project management software for task/issue tracking, and implement continuous integration/deployment practices to help manage complexity.

One challenge that is all too common is attempting to do everything within the scope of a single capstone project. The scale and multidisciplinary nature of big data means it is unrealistic for students to handle the full data science life cycle from end to end. They need to scope the project keeping their skills and time limitations in mind. Picking a focused problem statement, clearly defining milestones, and knowing when external help is needed can keep projects realistic yet impactful. Sometimes the goal may simply be exploring a new technique or domain rather than building a full production system.

Communicating findings and justifying the value of insights also poses difficulties. Students struggle to tell a coherent story when delivering results to reviewers, employers or sponsors who may not have a technical background. Techniques from fields like data journalism can help effectively communicate technical concepts and analytics using visualizations, narratives and business case examples. This is vital for big data projects to have broader applicability and impact beyond academic evaluations.

Acquiring and managing massive datasets, finding insights through exploration and advanced modeling, coordinating complex distributed systems, scoping realistic goals within timeframes, and communicating value are some major challenges faced by students in big data capstone projects. Early planning, hands-on practice, collaborating with technical experts, and leveraging cloud resources can help students overcome these obstacles and produce impactful work. With the right guidance and experiences, big data projects provide invaluable training for tackling real-world problems at scale after graduation.