• Question that you want to answer and what motivated you to study the question.
• What data will you analyze? Identify at least one data source (can be from Kaggle, etc.)
• What challenges do you face in analyzing this data?
• What packages were needed for this case study?
• Discussion: What did you learn from this experience? What more could you do with this project in the future?
Describe the topic, any challenges inherent in the area, and relationships with related topics. Apply your chosen method(s) to a data set of your choice, including any analyses and data-driven decisions from machine learning that can help you analyze the data.
• [Case Study] Apply computation techniques in R. Discuss the challenges in the problem and the data set, and how you circumvented these problems. Consider issues of, for example, sparsity in the features and response, high dimensions, and the scalability issues of BIG data. For any problem, apply any method that you see appropriate and discuss the advantages and disadvantages of each method and why you found them appropriate. Thoroughly explore and assess any inference that you make on the data and what lead to your analysis. In your presentation, explain the data, why it interested you, and your step-by-step analyses that lead to any final conclusions.