Final Project Instructions

• Think about the way you communicate your results:

  • Please write a clear introduction to your project, providing a background about the field you’re studying, the questions you had in mind and the way the data of your chosen data set was collected as well as its structure.

  • When possible, show a graphical representation of the samples in your study

  • Please construct a reasonable workflow. After showing a general exploratory analysis go in depth instead of horizontally (when applicable)

• Make sure you have enough data:

  • As a general rule of thumb I would suggest to have at least 50 observations for each level of categorical variable or numerical variable

• Don’t hesitate to use the web to tackle your problems

• Use packages like ‘SpatStat’ to check more elegant hypotheses

• To strengthen your analysis, download another data set, merge it with your data or use it to validate your findings

Project Grading Rubric (20% each)

You need to document and demonstrate all aspects of data science foundations discussed in the class.

1. Correctly apply tools and techniques of data preparation and wrangling

   1. Missing data handling, joining, or other transformations, removing outliers etc.

   2. Gathering, spreading data (if needed)

2. Use Exploratory Data Analysis and dplyr transformation methods to identify structure and correlations in the data

3. Formulate questions and possible ways of analysis and visualization

   1. Identify appropriate visualization methods for analysis of your data set

   2. Choose the right geoms for the questions at hand

4. Correctly interpret results of analysis (clinical/biological significance)

   1. Demonstrate domain specific knowledge of clinical data

   2. Propose an hypothesis based on visualization and results

   3. Compare the usefulness of the obtained results/conclusions

5. Formulate appropriate plans for validation, further analysis, or to collect additional data needed.