Project Plan: Enhancing the NFL Stats Tracker with Algorithmic and Data Structures Components Objective Enhance the existing NFL Stats Tracker project to incorporate algorithmic techniques and data structures, demonstrating proficiency in list comprehensions, sorting/searching algorithms, 2D iteration, and Big(O) complexity analysis.
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List Comprehensions Task: Add functionality to generate summaries of game results using list comprehensions.
Implementation Steps:
Use list comprehensions to create summaries of game results based on home and away scores.
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Sorting / Searching Task 1: Implement sorting functionality to sort games based on specified statistics (e.g., score_home).
Implementation Steps:
Define a method get_sorted_games in the FootballScoreModel class. Use Python’s sorted function to sort games by a specified statistic.
Task 2: Implement searching functionality to search for games involving a specific team.
Implementation Steps:
Define a method search_games in the FootballScoreModel class. Use SQLAlchemy’s filtering capabilities to search for games by team name.
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Big(O) Complexity Task: Analyze and document the time and space complexity of sorting and searching algorithms.
Implementation Steps:
Analyze the sorting algorithm (sorted): O(n log n) time complexity. Analyze the searching algorithm (SQLAlchemy filter): O(n) time complexity (depending on database indexing). Document these analyses in the project’s documentation and blog posts.
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2D Iteration Task: Implement functionality to display game results in a grid format using 2D iteration.
Implementation Steps:
Define a method display_game_results_grid in the FootballScoreModel class. Use 2D iteration to format and display the game results in a grid.
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Deployment (Full Stack) Task: A complete deployment illustration multiple people using and updating your Full Stack Web Application simultaneously.
Implementation Steps:
Deploy backend with both team members being able to access and make changes to deployed server.