You are an analyst at a "scrappy" online gaming company that specializes in remakes of last year's fads.

Your boss, who runs the product development team, is convinced that Pokemon Go's fatal flaw was that you had to actually move around outside. She has design mock-ups for a new game called Pokemon Stay: in this version players still need to move, but just from website to website. Pokemon gyms are now popular online destinations, and catching Pokemon in the "wild" simply requires browsing the internet for hours in the comfort of your home.

She wants you to program a prototype version of the game, and analyze the planned content to help the team calibrate the design.

Follow the question prompts and use base Python to create data stuctures and functions for the Pokemon Stay game.

When you've built the game components and loaded supplementary data, use statistics and probability to describe and refine the gameplay.

We will be looking for the following things:

• For coding questions, Python code that gives the correct output for each question
• For written questions, clear answers in Markdown cells

Your project should demonstrate your ability to do the following:

• Use a Jupyter notebook to perform an organized, well-documented analysis
• Use basic python syntax and control flow to solve a problem
• Create and manipulate python data structures, including lists and dictionaries
• Calculate basic descriptive from base formulas

Deliverable: Your project should be executed in a clearly commented Jupyter notebook. This notebook should be pushed to your personal (GA) GitHub repo and submitted to instructors by issuing a pull request to DSI-EAST-1/course-info (which is where you originally forked it from).

• This draws heavily on Python concepts from the first couple days of class -- review those lectures!
• For some questions, you may want to brush up on list comprehensions.

Projects will be evaluated across several cateogires. For each category, students will receive a score on a scale of 0-3.

For project 1 the evaluation categories are as follows:

• Organization: Clearly commented, annotated and sectioned Jupyter notebook or Python script. Comments and annotations add clarity, explanation and intent to the work. Notebook is well-structured with title, author and sections. Assumptions are stated and justified.
• Data Structures: Python data structures including lists, dictionaries and imported structures (e.g. DataFrames), are created and used correctly. The appropriate data structures are used in context. Data structures are created and accessed using appropriate mechanisms such as comprehensions, slices, filters and copies.
• Python Syntax and Control Flow: Python code is written correctly and follows standard style guidelines and best practices. There are no runtime errors. The code is expressive while being reasonably concise.
• Probability and Statistics: Descriptive and inferential statistics are calculated and applied where appropriate. Probabilistic reasoning is demonstrated. There is a clear understanding of how probability and statistics affects the analysis being performed.

For more information on how we grade our DSI projects, see our project grading walkthrough.