Indiana Topics in Computer Science

In this lesson, students will reflect on the concepts that will be covered in this course.

In this lesson, students learn about the basic history of video games and the industry. Students will reflect on how video games evolved with technological improvements and changes to game design and development in the early years of the industry.

In this lesson, students dive deeper into common elements that all fun games possess.

In this lesson, students will be introduced to the JavaScript code syntax they will be using for the Game Development content in this course.

In this lesson, students will be introduced to the concept of using timers to animate their graphics.

In this lesson, students will get more time practicing with timers as they learn how to stop their timers when a specific condition is met.

In this lesson, students learn about the logic required to implement their own collision detection functionality in their graphics animations.

In this lesson, students learn how to detect and take action upon a mouse click event.

In this lesson, students learn how to extend mouse events to make interactive animations using the movement and dragging motion of the mouse.

In this lesson, students will learn how to use keyboard keys to control events. Keyboard events capture when the user presses keys on the keyboard. This allows students to write programs that take input from the keyboard to change what is happening in the program.

In this lesson, students review content with a 25 question End-of-Unit Quiz.

In this lesson, students will learn about various software development life cycle models and tools that can be used at different stages of the process.

In this lesson, students go through the game development life cycle as they create their own game.

In this lesson, students explore the impacts of being online. Students reflect on social media use and their digital footprint as well as learn how to identify and respond to cyberbullying.

In this lesson, students explore the complex world of data collection. Students learn about the types of data that companies collect and what they do with it. Students also consider the ethics of data collection by working through a scenario about location tracking in school. Lastly, students learn about privacy within the context of law.

In this lesson, students will learn about and discuss information literacy. Information literacy is having the ability to find information, evaluate information credibility, and use information effectively.

By the end of this lesson, students should have a better understanding of cyberethics, responsible internet use, and the impact of copyright and licenses on the digital world. They should also be more aware of the potential consequences of unethical online behavior.

In this lesson, students learn about the importance of personal data security and explore different forms of authentication and browser security settings. Students then consider the tradeoffs between ease of use and security when implementing different authentication methods.

In this lesson, students are introduced to the core concepts of cybersecurity, focusing on the importance of protecting information and devices in the digital world. Students will explore key vocabulary terms, security frameworks, and the potential risks associated with the Internet of Things (IoT).

In this lesson, students complete a summative assessment of the module’s learning objectives.

In this lesson, students will learn how to make the most basic python program- one that displays text on the screen. Students will learn how to print in python using the print statement. They will also learn how to use quotations, apostrophes, and strings.

In this lesson, students will be reintroduced to the concept of variables, which store information in a program to be used later. Students will also learn about three different variable types- strings, integers, and floating point numbers.

In this lesson, students revisit user input. They learn how to request user input as both strings and integers, learn where the input is stored, and learn how to convert strings and integers.

In this lesson, students will learn about using mathematical operators in their Python programs. They will work through multiple examples to get comfortable with operator precedence and using different types of operators.

In this lesson, students will be able to perform string operations in order to concatenate values together.

In this lesson, students revisit the concept of comments. Comments are helpful because they allow programmers to leave notes about the program they are writing.

In this lesson, students review content with a 20 question Unit Quiz.

In this lesson, we learn about Functions. Functions let us break our program into different parts that we can organize and reuse however we like. Functions are the main building block of complex Python programs.

In this lesson, we dive deeper into the concept of functions by exploring how to use parameters.

In this lesson, we explore where variables exist and what the difference is between a local and global variable.

In this lesson, students explore functions with return values and deepen their understanding of and ability to use functions.

In this lesson, students review content with a 15 question Unit Quiz.

In this lesson, students will learn about and practice using tuples. A tuple is a heterogenous, immutable data type that stores an ordered sequence of things that can be accessed using indices.

In this lesson, students will learn about their second data structure, lists. A list is a mutable, heterogeneous data type that stores an ordered sequence of things.

In this lesson, students will learn how lists can be iterated over in a similar way to strings.

Methods, in general, are like functions that can be called on objects. Students have seen previously how string methods are called on strings. In this lesson, students will learn about the various list methods that can be called on lists!

In this lesson, students will learn how 2d lists are stored and how to use indexing and slicing to extract specific items from a list of lists.

In this lesson, students will see how dictionaries differ from other data structures and why they are useful.

In this lesson, students will learn what data science is, what a data scientist does, and the different types of questions that can be asked about data. Students will learn that statistical questions include computations or finding a relationship or pattern.

In this lesson, students will learn about the data cycle and apply the first two steps of asking questions and considering data. Students will start a mini-project that spans through the rest of the module by asking a statistical question about a field of interest and gathering and structuring the data. They will also learn about and consider both quantitative and qualitative data.

In this lesson, students will learn the basics of Python programming in the context of data science. This includes how to define and use variables and lists, how to use comparison and logical operators, and the importance of knowing the different data types used in Python.

In this lesson, students will learn about Python modules and libraries and how to implement and use them within the editor.

In this lesson, students will learn how to create a use a Pandas Series. They will also learn and explore measures of central tendency including the mean, median, and mode.

In this lesson, students will expand their statistical knowledge to include the spread of a dataset. They will learn about and apply measures of spread including standard deviation, variance, range, and interquartile range.

In this lesson, students will learn how to create a data frame using the Pandas library. They will also learn and use functions to explore a data frame further including which data types are included, the shape of the data frame, the descriptive statistics of the data in each column, and more.

In this lesson, students will learn how to filter a data frame by selecting and displaying only specific columns. They will also learn how to filter rows displayed by using conditionals. Lastly, students will learn how to change the index used in a data frame and set it to a column of their choice.

In this lesson, students will define and use functions, along with values in a dataset, to calculate and create new columns of data.

In this lesson, students will practice collecting, explaining, and presenting the important data and details of their mini-project.

In this lesson, students review content with a 15 question end-of-module quiz.

In this lesson, students will be introduced to the module project. They will take a look at how the data is used in the business world to improve aspects of the business as well as predict future outcomes.

In this lesson, students will learn how to import large datasets. They will also learn how to filter a dataset using index-based selection (iloc) and label-based selection (loc).

In this lesson, students will learn how to conditionally filter a dataset using label-based selection (loc) and comparison operators.

In this lesson, students will learn the importance of data cleaning and how to do it. Data cleaning deals with fixing or removing incorrect or missing values.

In this lesson, students will learn about the importance of data visualization when telling a data story. Students will be using a variety of charts, graphs, images, and other common data visualizations to help to bring meaning and understanding to otherwise complex data.

In this lesson, students will learn about univariate data and how to visualize and compare datasets using line and bar charts.

In this lesson, students will learn how to determine the quality of a dataset. They will explore a few raw datasets to access their quality and completeness.

In this lesson, students will practice aggregating data by using different sort and group functions and parameters.

In this lesson, students will practice combining data by using different concatenation and merging techniques.

In this lesson, students will work on their module projects by gathering and combining data from multiple sources. They will check the quality of the datasets as well as clean, combine and sort them.

In this lesson, students will learn different types of bias that can be present and affect data analytics. They will also take time to analyze and interpret their project datasets.

In this lesson, students will work on their module projects by creating a business report.

In this lesson, students review content with a 10 question end-of-module quiz.

In this lesson, students are introduced to the field of Artificial Intelligence. Students explore the definition of intelligence and the different types of artificial intelligence in computers.

In this lesson, students will learn more about the types of AI and dive deeper into the three most popular machine learning algorithms.

In this lesson, students will learn about different subsets of Artificial Intelligence, specifically machine learning and neural networks.

In this lesson, students discuss important ethical issues related to the development of Artificial Intelligence, and debate the necessity of Artificial Intelligence in modern society.

In this lesson, students are introduced to TensorFlow and the basics for creating a Neural Network in TensorFlow.

In this lesson, students will learn about convolutional neural networks in order to create an image prediction model. Students will have the opportunity to apply these to a TensorFlow model to make predictions about images.

In this lesson, students will learn about what key characteristics make up a good training dataset and explore the impact of using a biased dataset on a face-recognition model.

In this lesson, students will learn about tokenizing text to be used in Natural Language Processing models. They will then use that along with embedding layers to create text sentiment models.

In this lesson, students will learn about recurrent neural networks (RNN) and apply it to create a text-generating model using unsupervised input data.

In this lesson, students will demonstrate their knowledge to create a final TensorFlow model. Students can choose from one of the starter projects or choose a project of their own.