A major challenge students face in module 1 is properly explaining the business problem and framing the data science solution in a way that is clear, concise and compelling for the stakeholder. This is difficult because it requires translating the technical aspects of the project into everyday language that a non-technical audience can understand. Some tips to help with this include: conducting interviews with stakeholders to clearly define the problem from their perspective; using non-technical terms and simple visuals/explanations whenever possible; and focusing on how the solution will specifically help the stakeholder rather than focusing too much on technical details.

In module 2, acquiring and preparing the data for analysis can pose significant challenges. Data may be in inconsistent or incompatible formats that need extensive cleaning and preprocessing. Some common issues include: data from multiple sources not joining together properly; missing or ambiguous data values that must be addressed; and dirty, corrupt or improperly formatted data that requires debugging. To overcome these challenges, students should: assess the data quality early; explore the data carefully before cleaning; start by addressing null/missing values; standardize data formats; and document all data processing steps carefully. Leveraging Python skills like regular expressions and working iteratively in small chunks can help manage complexity.

Feature engineering is a major hurdle in module 3. Determining the most useful predictive features to extract from raw data and transform for modeling requires creativity, experimentation and understanding the problem domain. Issues include: difficulty selecting meaningful features; over-reliance on inherently non-predictive features; and feature extraction processes that are overly complex, computationally intensive or rely on domain knowledge that may be lacking. Some approaches to help include: starting simply with raw features before transforming; using exploratory data analysis like correlations to guide feature selection; considering both technical and domain-based perspectives on important factors; and validating features actually improve model performance and solve the business problem.

Developing and evaluating machine learning models to find the best for the problem and data is another significant module 3 challenge. Issues can involve: poor model choice for the problem which require retraining from scratch; algorithms not scaling well to large, complex data; lack of optimization of hyperparameters resulting in suboptimal models; and difficulty assessing model performance without proper validation. To tackle these, students should: consider multiple model types; carefully split data for training, validation and testing; use grid search or randomized search to tune hyperparameters; evaluate models on multiple relevant metrics including accuracy, errors, outliers; and apply techniques like ensemble modeling to boost performance.

In addition to technical challenges, time management across all modules poses a major hurdle for capstone project work. Capstone involve open-ended problem exploration, iteration and demonstration of skills – requiring perseverance, teamwork and pacing to complete on schedule. To overcome this, students must: break work into discrete milestone-driven tasks; establish clear communication with teammates and stakeholders; maintain modular, well-documented code; leverage automation, parallelization and cloud resources to speed processing; pace longer workflows realistically and leave time for refinements; and ask for help to avoid bottlenecks/roadblocks. With careful planning and open-minded problem solving, students can rise above these common challenges to deliver a quality end-to-end data science solution.

Modules 1-3 cover the breadth of initial steps in any data science project – from problem definition to acquiring/preparing data to selecting modeling techniques. The challenges stem from balancing technical rigor with human/business factors; adapting to diverse, imperfect real-world data sources; and managing open-ended iterative workflows under time constraints. With experience, the right mindset and community support, students can gain skills to methodically work through such obstacles, producing insights of tangible value for stakeholders. Completing these initial modules successfully lays the foundation for developing a polished, impactful capstone project.