CS 1.2: 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

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Schedule

Course Dates: Monday, October 21 – Wednesday, December 11, 2019 (7.5 weeks)

Class Times: Monday & Wednesday at 1:30-3:20pm (section A) or 3:30–5:20pm (section B)

Class Date Topics
1 Mon, Oct 21 Strings & Random Numbers
2 Wed, Oct 23 Histogram Data Structures
3 Mon, Oct 28 Probability & Sampling
4 Wed, Oct 30 Flask Web App Development
5 Mon, Nov 4 Application Architecture
6 Wed, Nov 6 Generating Sentences
7 Mon, Nov 11 Arrays & Linked Lists
8 Wed, Nov 13 Linked List Algorithm Analysis
9 Mon, Nov 18 Hash Tables
10 Wed, Nov 20 Hash Table Algorithm Analysis
11 Mon, Nov 25 Project Lab Day
Wed, Nov 27 No Class (Thanksgiving Break)
12 Mon, Dec 2 Higher Order Markov Chains
13 Wed, Dec 4 Regular Expressions
14 Mon, Dec 9 Time to Tweet & Launch Day!
15 Wed, Dec 11 Activity To Be Determined

Prerequisites

Students must pass the following course and demonstrate mastery of its competencies:

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

Project Tutorial

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

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
  • Pass the project according to the associated project rubric
  • Pass the summative assessment (final exam)

Attendance

Just like any job, attendance at Make School is required and a key component of your success. Attendance is being onsite from 9:30am to 5:30pm each day, attending all scheduled sessions including classes, huddles, coaching and school meetings, and working in the study labs when not in a scheduled session. Working onsite allows you to learn with your peers, have access to support from TAs, instructors and others, and is vital to your learning.

Attendance requirements for scheduled sessions are:

  • No more than two unexcused absences ("no-call-no-shows") per term in any scheduled session.
  • No more than four excused absences (communicated in advance) per term in any scheduled session.

Failure to meet these requirements will result in a Participation Improvement Plan (PIP). Failure to improve after the PIP is cause for not being allowed to continue at Make School.

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