Argument project

The final project for Unit 2 is a research-based argument. You will select a research topic which interests you, identify specific research questions, find or create a data set which could help answer your research questions, and then use Jupyter Lab and Pandas (which you learned in the Pokemon lab) to answer your questions. Here is an example of a complete data science project.

Data science has become an important tool for journalism. All journalism involves a process of gathering information, analyzing it, determining what is most important, and presenting it in a way that readers will be able to understand. FiveThirtyEight was one of the first organizations focused on data science journalism. Now almost every news outlet has a data science team. If you click the link to FiveThirtyEight, you will see a collection of their stories, along with the data and analysis they used to reach their conclusions.

To be successful in this project, you will need to find a question that is both interesting to you and answerable (at least in part) with data from the social media dataset. Your teachers will help you make sure your proposal can achieve these goals.




Before you start your research, you need to make a plan. Answer the following questions in


Once your proposal has been approved, you are ready to start your analysis in argument.ipynb. Make sure your data set is saved as a .csv file in data.

💻 To get started, run jupyter lab. When you save your work within Jupyter, the argument.ipynb file will be updated.

💻 When you are ready to end a work session, press Control + C in Terminal to stop the Jupyter Lab server.

You may want to start by reviewing the Pokemon lab for techniques you have already learned. However, you will likely also need to use some skills you have not yet learned, particularly for working with DataFrames in pandas and creating plots using pandas, seaborn, and maybe matplotlib. Your teacher will support you, but also


Approaches expectationsMeets expectationsExceeds expectations
Concepts: Making data meaningfulBUTYou use a summary statistic (mean, median, mode) to answer at least one of your research questions. You generate a plot to answer at least one research question, and explain what the plot means.AND
Concepts: Data structuresBUTYou use data structures (i.e. list, dict, dataframe) which are appropriate for the data you areAND Your use of data structures is elegant and/or creative. You write at least one function which creates or transforms a data structure.
Practices: PlanningBUT you might be missing required components of the proposal, not have enough commits, or not have enough detail in your commit messages.Between your proposal and your commit messages, a reader can understand the process by which you designed, developed, and debugged your drawing project.AND your process documentation contains evidence of reflection, showing what you have learned or how your thinking has changed over the course of this project.
Perspectives: Reflection on Tech and SocietyBUTBoth the analysis and the presentation of your arugment are designed to connect with and be understood by a real-world audience.AND