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PLTW’s CSP Index

Project Lead the Way (PLTW)’s Computer Science Curriculum sequence includes a College Board recognized CS Principles (CSP) course. For the four units of PLTW’s CSP Course we show how the lessons relate to and complement the professional development material. Clicking titles in color gives the links to related PD4CS posts.

Unit 1: Algorithms, Graphics, and Graphical User Interfaces

1.1 Algorithms and Agile Development
1.1.1 Principles
1.1.2 Lightbot – Input, Output, State
1.1.3 Branching and Iteration
1.1.4 Objects and Methods
1.1.5 Variable Roles I
1.1.6 Variable Roles II
1.1.7 Scratch Game or Story
1.2 Mobile App Design
1.2.1 Bits and Bytes
1.2.2 Introducing App Inventor
1.2.3 Creating Mobile Apps
1.2.4 Analyzing a Program
1.2.5 Modifying a Program
1.2.6 Designing an App
1.3 Algorithms in Python
1.3.1 Programs are Data
1.3.2 Python Variables and Functions
1.3.3 Branching and Output
1.3.4 Nested Branching and Input
1.3.5 Strings
1.3.6 Tuples and Lists
1.3.7 For Loops
1.3.8 While Loops
1.3.9 Tools for Collaboration
1.3.10 Game Theory
1.4 Images and Object-Oriented Libraries
1.4.1 Procedural Abstraction
1.4.2 Objects and Methods
1.4.3 Images and Arrays
1.4.4 Python Imaging Library API
1.4.5 Image Algorithms
1.4.6 Digital Property and Forensics
1.4.7 Image Artist
1.5 GUIs in Python
1.5.1 Human-Computer Interaction
1.5.2 The API for the Tkinter Canvas
1.5.3 The MVC Pattern with Tkinter
1.5.4 Design a Python GUI

Unit 2: The Internet

2.1 The Internet and the Web
2.1.1 The Rise of the Internet
2.1.2 Your Favorite Web Page
2.1.3 Protocols and Bandwidth
2.1.4 HTML and CSS
2.1.5 Secure Protocols
2.2 Shopping and Social on the Web
2.2.1 HTML5 and JavaScript
2.2.2 Introducing PHP
2.2.3 Databases and SQL
2.2.4 Dynamic Data-Driven Design
2.2.5 Career Fields of CS and IT
2.3 Security and Cryptography
2.3.1 The Vulnerable User
2.3.2 Security by Encryption
2.3.3 Security and Liberty
2.3.4 The Heist

Unit 3: Raining Reigning Data

3.1 Visualizing Data
3.1.1 Time Series and Trends
3.1.2 Privacy Issues with Data
3.1.3 Data Innovations and Parallel Algorithms
3.1.4 Pie Charts and Bar Graphs
3.1.5 Histograms and Distributions
3.2 Discovering Knowledge from Data
3.2.1 Inferential Statistics
3.2.2 Image Data
3.2.3 Linked Data
3.2.4 Geographic Data
3.2.5 Simulation Data
3.2.6 Genomic Data
3.2.7 Investigate with Data

Unit 4: Intelligent Behavior

4.1 Moore’s Law and Modeling
4.1.1 Computing Impacts All Fields
4.1.2 Basic Control Circuits
4.1.3 Introducing Simulations
4.1.4 Varying Parameters
4.1.5 Assumptions, Abstractions, and Ethics
4.2 Intelligent Agents
4.2.1 Emergent Behavior
4.2.2 Neural Networks
4.2.3 Modifying a Simulation’s Assumptions
4.2.4 Beauty in Chaos and Fractals
4.2.5 CS Principles

nsf1 This work is supported by the National Science Foundation under grant number 1502462. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation"