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https://github.com/el634dev/acs-1120-intro-data-structures


https://github.com/el634dev/acs-1120-intro-data-structures

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README 

        
# ACS 1120: Intro to Data Structures



## Course Description



A project based course that looks under the hood at data structures and algorithms to see how they work. In addition to implementing these structures in an application; students will build them from scratch, analyze their complexity, and benchmark their performance to gain an understanding of their tradeoffs and when to use them in practice. Students will write scripts, functions, and library modules to use text processing tools like regular expressions, construct and sample probability distributions to create a Markov language model and gain insight into how grammar works and natural language processing techniques.



## Repository Setup



:warning: **Important:** Please follow [these instructions](Setup.md) exactly to set up your clone of this course repository.



## Learning Objectives



By the end of this course, students will be able to:



1. Create Python programs that read and write text files and manipulate strings

2. Build web apps with the Flask framework and deploy to the web

3. Construct and sample probability distributions based on observed word frequencies

4. Create Markov language models and use them to generate new sentences

5. Use unit tests that assert correct behavior of functions and classes

6. Implement core data structures including singly linked lists and hash tables

7. Analyze the complexity of iterative algorithms and data structures with visual loop counting



## Schedule



**Course Dates:** Wednesday, January 20 – Friday, March 11, 2022


**Class Times:** Monday, Wednesday, & Friday at 4:30-6:20pm PST



Day | Lesson Topics

:-: | :------------------------------------------

01  | [Strings & Random Numbers]

02  | [Histogram Data Structures]

03  | [Probability & Sampling]

04  | [Flask Web App Development]

05  | [Application Architecture]

06  | [Generating Sentences]

07  | [Arrays & Linked Lists]

08  | [Algorithm Analysis]

09  | [Hash Tables]

10  | Hash Table [Algorithm Analysis]

11  | [Higher Order Markov Chains][final project]

12  | [Regular Expressions]

13  | Lab Day

14  | Launch Day: Show & Tell



## Project Tutorial



Students will complete the following guided project tutorial in this course:



- [**Tweet Generator:** Data Structures & Probability with Python](https://bit.ly/tutorial-tweet-generator)



## Evaluation



To pass this course, students must meet the following requirements:



- Actively participate in class and abide by the attendance policy

- Make up all classwork from all absences

- Complete the required [project tutorial](#project-tutorial)

- Turn in all deliverables on Gradescope



[algorithm analysis]: Lessons/AlgorithmAnalysis.md

[application architecture]: Lessons/Architecture.md

[arrays & linked lists]: Lessons/ArraysLinkedLists.md

[due]: ReadMe.md#Deliverable-Schedule

[final project]: Lessons/MarkovChains.md

[flask web app development]: Lessons/FlaskWebApp.md

[generating sentences]: Lessons/Sentences.md

[hash table challenges]: Lessons/HashTables.md#Challenges

[hash tables]: Lessons/HashTables.md

[higher order markov chains]: Lessons/MarkovChains.md

[histogram data structures]: Lessons/Histograms.md

[linked list challenges]: Lessons/ArraysLinkedLists.md#Challenges

[probability & sampling]: Lessons/Probability.md

[project check-in]: Lessons/FlaskWebApp.md

[quiz]: StudyGuides.md

[quiz study guides]: StudyGuides.md

[regular expressions]: Lessons/RegularExpressions.md

[strings & random numbers]: Lessons/RandomStrings.md