Learn how to code using Python through exercises, assessments, and projects, and gain vital data analysis and visualization techniques
Duration: 150 hours
Grades: 9-12
Level: Beginner
About this course
This course unveils the captivating world of Computer Science — where knowledge fuels innovation! Explore core principles and real-world applications in Artificial Intelligence and Data Science. Learn how to code using Python through exercises, assessments, and projects, and gain vital data analysis and visualization techniques. This course also facilitates discussions around several ethical implications of computing. This course prepares students to take the AP Computer Science Principles exam.
What you'll learn
Understand and apply computer science fundamentals to the real world
Use the Python programming language
Apply data analysis and visualization techniques
Think critically about computer science and AI applications including their impacts on society
How this course is structured
The course is divided into 9 total units. Each unit has several lessons. Within each lesson are instructional videos and practice activities. It will take no more than 60 minutes to complete each lesson. Learners will be asked to complete a creative project every 2-3 units.
Course Syllabus
Unit 1: Fundamentals of Communicating with a Computer
Lesson 1: Motivations & Applications
Lesson 2: Print Statements and Commenting
Lesson 3: Data Types
Lesson 4: Variables
Lesson 5: Bugs and Debugging
Lesson 6: User Input and Output
Unit 2: Decision Making with Computers using If-else Statements
Lesson 1: If Statements and Operators
Lesson 2: Decision Trees and Flow Charts
Lesson 3: If-else Statements
Lesson 4: Elif Statements
Lesson 5: Nested if statements
Unit 3: Expanding Capabilities with Functions and Libraries
Lesson 1: Functions
Lesson 2: Functions with Return Values
Lesson 3: Built-In Functions
Lesson 4: Using Modules and Libraries
Unit 4: Storing Data with Lists
Lesson 1: Creating Lists
Lesson 2: Indexing into Lists and Changing Elements in Lists
Lesson 3: Appending to and Removing from Lists
Lesson 4: Tuples and Nested Lists
Unit 5: Repetition and Iteration with Loops
Lesson 1: While Loops
Lesson 2: For Loops
Lesson 3: Looping over Strings and Lists
Lesson 4: Nested Loops
Lesson 5: Loops for Data Scientists
Unit 6: Storing Data with Dictionaries
Lesson 1: Creating a Dictionary
Lesson 2: Accessing Elements in a Dictionary
Lesson 3: Changing and Adding Elements in a Dictionary
Lesson 4: Complex Data Structures
Unit 7: Creating Custom Data Types with Classes
Lesson 1: Creating Classes
Lesson 2: Adding Behavior to Classes with Methods
Unit 8: Data Analysis Life Cycle
Lesson 1: What is the Data Analysis Life Cycle?
Lesson 2: Importing and Exploring Datasets
Lesson 3: Cleaning Datasets
Lesson 4: Uncovering Information from Data
Unit 9: Data Visualization
Lesson 1: Purpose of Data Visualizations
Lesson 2: Creating Bar Plots
Lesson 3: Creating Line Plots and Scatter Plots
Lesson 4: Using Visualizations to Find Trends in Data
Kernels of Curiosity
These mini-units cover concepts beyond programming that are critical to students' understanding of how computer science shows up in the real world in different ways, and the risks and limitations associated with it.
Kernel of Curiosity 1: Data under the Hood
Kernel of Curiosity 2: Theory of Computing
Kernel of Curiosity 3: Computer Systems and Networks
Kernel of Curiosity 4: Privacy and Ownership
Kernel of Curiosity 5: Community and Access
