ISA 419: Data-Driven Security

class: center, middle, inverse, title-slide

.title[
# ISA 419: Data-Driven Security
]
.subtitle[
## 02: Python Programming Basics
]
.author[
### <br>Fadel M. Megahed, PhD <br><br> Professor <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z" /></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z" /></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z" /></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z" /></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Fall 2025
]

---

## Quick Refresher of Last Class

✅ Define **information security (Infosec)**, its **main goals**, and how it fits within a firm’s overall security protocols

✅ Describe the **three main steps** in information security: prevention, detection, and response.

✅ Explain why **prevention as a sole security measure is deemed to fail**.

✅ Describe **course structure**, **goals**, and **overview**.

---

## Learning Objectives for Today's Class

- Use pseudocode to map out a problem.

- Python syntax, data types, and data structures

- Convert data types using type casting

- Manipulate lists and use methods on lists

---
class: inverse, center, middle

# Use Pseudocode to Map out a Cybersecurity Problem

---

## Background: Why is BUS 104 is a Pre-Req?

### From the MU Bulletin

> .bold[BUS 104. Introduction to Computational Thinking for Business. (2)]

> As part of the Farmer School of Business first-year integrated core curriculum, this course introduces students to the fundamentals of computational thinking as an aid to data-driven business problem-solving. Topics include: computational thinking as **problem solving**, **representing data through abstractions**, and **thinking in terms of algorithms** (loops, conditions, reusable code, functions and events) to **automate finding solutions**. The course lays the foundations for students identifying, analyzing, and implementing solutions for data-driven business problems and the communication of results.

.footnote[
<html>
<hr>
</html>

**Source:** [Business Analysis (BUS) | Miami University Bulletin](https://bulletin.miamioh.edu/courses-instruction/bus/).
]

---

## Background: (Distributed) Denial of Service Attacks

.footnote[
<html>
<hr>
</html>

**Credits:** Jeff Crume. (2023) [IBM Technology: Denial of Service Attacks Explained](https://www.youtube.com/watch?v=bDAY-oUP0DQ).
]

---

## Simplifying our Task Ahead: SYN Flood Attacks

- We will focus on the simple **SYN flood attack**.
- Attackers send a succession of SYN requests to a target's system.

.footnote[
<html>
<hr>
</html>

**Image Source:** Captured from the Jeff Crume. (2023) [IBM Technology: Denial of Service Attacks Explained](https://www.youtube.com/watch?v=bDAY-oUP0DQ) video (~ the frame corresponding to 3:47). Then, the image was adjusted by: (a) removing the presenter's picture, and (b) adding a circle around the SYN attack.

]

---

## Class Activity: Detecting SYN Flood Attacks

.panelset[

.panel[.panel-name[Task]

- Toward creating an algorithm for monitoring network traffic and detecting SYN flood attacks, in pairs, please think and answer the following questions.

- Write down your answers in each tab, after each question.
]

.panel[.panel-name[Q1]
1. What kind of **information** or **data points** do you think would be important to collect and analyze?

.can-edit.key-activity3a[
- **Data Points to Collect:**   
  + Edit me.  
  + Edit me.  
  + Edit me.
]
]

.panel[.panel-name[Q2]
1. In a DDoS attack, what aspects of network traffic might change when there's an attack happening? How could you measure or quantify these changes?

.can-edit.key-activity3b[
- **Changes in Network Traffic during an Attack:**     
  + Edit me.  
  + Edit me.  
  + Edit me.
]
]

.panel[.panel-name[Q3]
1. Think about monitoring network traffic over a period of time. What time-related factors should you consider when designing a detection system?

.can-edit.key-activity3c[
- **Changes in Network Traffic during an Attack:** Edit me.     
]
]

.panel[.panel-name[Q4]
1. If you were trying to identify multiple requests coming from the same IP address, how would you keep track of these requests? What kind of **data structure** could you use to achieve this?

.can-edit.key-activity3d[
- **Tracking Requests from the Same IP:**  Edit me.    
]
]

.panel[.panel-name[Q5]
1. What could be some potential signs that a specific IP address is involved in a DDoS attack?

1. How would you set a threshold to determine when an IP address should be flagged as a potential source of an attack?

.can-edit.key-activity3e[
- **Setting a Threshold:**  Edit me.    
]
]

]

???
1. **Data Points to Collect:** Important data points to collect might include IP addresses of incoming requests, the time of each request, and the number of requests from each IP address.

1. **Changes in Network Traffic during an Attack:** During a DDoS attack, there may be a sudden increase in the number of incoming requests from various IP addresses. To quantify these changes, you could monitor request frequency, request rate, and the total number of unique IP addresses making requests.

1. **Time-Related Factors:** When monitoring network traffic over time, you should consider the duration of the monitoring period (time window) to observe patterns and changes. You might also consider the timestamps of incoming requests to track the rate at which requests are arriving.

1. **Tracking Requests from the Same IP:** To keep track of requests from the same IP address, we could use a data structure like a dictionary or hash table. Each IP address could be a key, and the associated value would be the count of requests made from that IP address.

1. **Setting a Threshold:** To flag an IP address as a potential source of a DDoS attack, you would need a threshold value representing an unusual level of traffic. If the number of requests from an IP address exceeds this threshold within the specified time window, you would flag it as a potential source of the attack.

---

## Computational Thinking: From Problem to Code/Solution

.footnote[
<html>
<hr>
<html>

**Image Source:** Harimurti, R., & Asto, B. I. (2019, May). The concept of computational thinking toward information and communication technology learning. In IOP Conference Series: Materials Science and Engineering (Vol. 535, No. 1, p. 012004). IOP Publishing. [Figure 02](https://www.researchgate.net/figure/The-elements-of-computational-thinking_fig1_333826796).
]

???
- **Decomposition:** Breaking down a complex problem into smaller, more manageable parts. This process involves dissecting a large, complex problem into smaller, more understandable and solvable components.

- **Pattern Recognition:** Looking for similarities among and within problems. This involves identifying patterns, trends, or common characteristics in a set of data or problems to simplify and solve them more effectively.

- **Abstraction:** Focusing on the important information only, ignoring irrelevant detail. It involves extracting the essential details about an issue or a problem, ignoring the background noise or unnecessary information.

- **Algorithm Design:** Developing a step-by-step solution to the problem, or the rules to follow to solve the problem. This is about creating a clear set of instructions or a plan that can be followed to solve a problem or perform a task.

---

## Pseudocode for Detecting SYN Flood Attacks

``` python
BEGIN DDoS_Detection
    `SET` threshold_limit `to` a significant number representing unusual traffic
    `SET` time_window `to` the period of monitoring traffic (e.g., per minute)
    `SET` ip_request_count `to` an empty data structure (e.g., dictionary)

for each request in network_traffic
        `GET` ip_address from request
        `GET` request_time from request

if ip_address is not in ip_request_count
            `SET` ip_request_count[ip_address] to 1
        else
            `INCREMENT` ip_request_count[ip_address] by 1

if ip_request_count[ip_address] exceeds threshold_limit within time_window
            `FLAG` ip_address as potential DDoS source
            `TAKE` necessary action (e.g., block IP, alert admin)

if time elapsed > time_window
            `RESET` ip_request_count for each ip_address

`END` FOR loop
END DDoS_Detection
```

---

## App: Detecting Simulated Attacks based on Pseudocode

.footnote[
<html>
<hr>
</html>

**Notes:** Students are encouraged to "play" with the app's simulation configurations to better understand the pseudo_code. The app can be opened in your browser by navigating to: <https://muddos.streamlit.app/>.
]

---
class: inverse, center, middle

# Python Syntax, Data Types & Data Structures

---

## Why are we Discussing Syntax?

> .font120[Good coding style is like **correct punctuation**: you can manage without it, **butitsuremakesthingseasiertoread**.]
 <br> -- [The tidyverse style guide](https://style.tidyverse.org)

<br>

> .font120[Code is **read much more often than it is written**. The guidelines provided here are intended to improve the readability of code ...]  
-- [PEP 8 - Style Guide for Python Code](https://peps.python.org/pep-0008/)

---

## Python Syntax: Key PEP 8 Guidelines

.pull-left[
**Indentation**
- Use .bold[4 spaces per indentation level].
- Do **not mix tabs and spaces for indentation**.
- Example:

``` python
def my_function():
    for i in range(10):
        print(i)
```

]

.pull-right[
**Naming Conventions**
- .bold[Functions and variables:] `lower_case_with_underscores`
- .bold[Classes:] `CapWords` (or CamelCase)
- .bold[Constants:] `UPPER_CASE_WITH_UNDERSCORES`
- Example:

``` python
class MyClass:
    CONSTANT_VALUE = 123

def my_method(self):
        my_variable = 10
```

]

.footnote[
<html>
<hr>
</html>

**Tip:** Consistency with these guidelines improves code readability and maintainability. See [PEP 8 - Style Guide](https://peps.python.org/pep-0008/) for more details.
]

---

## Python Syntax: Key PEP 8 Guidelines

.pull-left[
**Comments**
- Write .bold[clear comments (full sentences)] for tricky parts of the code.
- .bold[Inline comments] should be used .bold[sparingly].
- Example:

``` python
# Calculate the square of x (yes)
square = x ** 2  # Square value (No)
```

]

.pull-right[
**Importing Modules**
- Imports should be on separate lines.
- **Order:** standard library (e.g., `os`) `\(\longrightarrow\)` related third-party (e.g., `pandas`) `\(\longrightarrow\)` local application/library specific imports (e.g. from other `.py` files).
- Example:

``` python
import os
import sys

import numpy as np
import pandas as pd

from my_local_module import my_function
```

]

.footnote[
<html>
<hr>
</html>

**Tip:** Consistency with these guidelines improves code readability and maintainability. See [PEP 8 - Style Guide](https://peps.python.org/pep-0008/) for more details.
]

---

## Python's Data Model: "Everything is an Object in Python"

.footnote[
<html>
<hr>
</html>

**Source:** The embedded page is from [3. Data model - Python 3.12.1 documentation](https://docs.python.org/3/reference/datamodel.html).
]

---

## Data Types

.center[An overview of the most common data types in Python. Note that I excluded the `None` type as well as `modules` and `callables` from this flowchart (for the sake of conciseness).]

.footnote[
<html>
<hr>
</html>

**Image Credits:** Created by the author (Fadel M. Megahed) for our ISA 419 course, based on the information provided in the [Python's Data model documentation](https://docs.python.org/3/reference/datamodel.html).
]

---

## Python Variables are Pointers, Not Buckets

.pull-left[
- Variables in Python are not buckets containing things; they're **pointers** (they point to objects).

- A **pointer** just represents the **connection between a variable and an objects**.

- Imagine **variables** living in *variable land* and **objects** living in *object land*. A pointer is a little arrow that connects each variable to the object it **points to**.

- Assignment statements point a variable to an object.
]

.pull-right[

``` python
numbers = [2, 1, 3, 4, 7]
numbers2 = [11, 18, 29]
name = "Trey"
```

]

.footnote[
<html>
<hr>
</html>

**Source:** The content and the figure in this slide are from Trey Hunter (2022). [Variables and objects in Python](https://www.pythonmorsels.com/pointers/)
]

---

## Assignment Points a Variable to an Object

.pull-left[
- Variables in Python are not buckets containing things; they're **pointers** (they point to objects).

- A **pointer** just represents the **connection between a variable and an objects**.

- Imagine **variables** living in *variable land* and **objects** living in *object land*. A pointer is a little arrow that connects each variable to the object it **points to**.

- Assignment statements point a variable to an object.
]

.pull-right[

``` python
numbers = [2, 1, 3, 4, 7]
numbers2 = numbers
name = "Trey"
```

]

.footnote[
<html>
<hr>
</html>

**Source:** The content and the figure in this slide are from Trey Hunter (2022). [Variables and objects in Python](https://www.pythonmorsels.com/pointers/)
]

---

## Implications: Equality Compares Objects

Python's `==` operator checks that two objects **represent the same data** (a.k.a. **equality**):

.pull-left[

The variables `my_numbers` and `your_numbers` point to **objects representing the same data**, but the objects they **point to are not the same**.

``` python
my_numbers = [2, 1, 3, 4, 7]
your_numbers = [2, 1, 3, 4, 7]

my_numbers == your_numbers
```

```
## True
```

``` python
my_numbers is your_numbers
```

```
## False
```
]

.pull-right[

So changing one variable/object **does not** change the other:

``` python
my_numbers[0] = 7

my_numbers == your_numbers
```

```
## False
```

]

---

## Changing the Object will Change Both Variables

If two variables **point to the same object:**

``` python
my_numbers_again = my_numbers
my_numbers is my_numbers_again 
```

```
## True
```

Changing the object one variable points to **also changes** the object the other variable points to:

``` python
*my_numbers_again.append(419)

print('my_numbers_again =', my_numbers_again, 'and my_numbers also =', my_numbers, '.')
```

```
## my_numbers_again = [7, 1, 3, 4, 7, 419] and my_numbers also = [7, 1, 3, 4, 7, 419] .
```

On a positive note, this does not apply to strings and numbers (both are **immutable**).

.footnote[
<html>
<hr>
</html>

**Source:** The content in this slide is adapted from Trey Hunter (2022). [Variables and objects in Python](https://www.pythonmorsels.com/pointers/)
]

---
class: inverse, center, middle

# Convert Basic Data Types

---

## Type Conversions in Python

.center[An overview of type conversions in Python.]

.footnote[
<html>
<hr>
</html>

**Image Credits:** Created by the author (Fadel M. Megahed) for our ISA 419 course.
]

---

## Class Activity: Type Conversion

.panelset[
.panel[.panel-name[Task]

- Based on the previous chart, use [Google Colab](https://colab.research.google.com/) (or your preferred Python environment) to run the following two code examples.

- Write down your observations in the next tab.

.pull-left[

``` python
course_a = 'isa419'
course_list_a = list(course_a)
print(course_list_a)
print(type(course_list_a))
```
]

.pull-right[

``` python
course_b = 419
course_list_b = list(course_b)
print(course_list_b)
print(type(course_list_b))
```
]
]

.panel[.panel-name[Observations]

.can-edit.key-activity4[
- **My observations from these simple examples are:**   
  + Edit me.  
  + Edit me.  
  + Edit me.
]

]
]

---
class: center, inverse, middle

# Manipulate Lists and Use Methods on Lists

---

## Lists in Python

- A **list** is a **mutable** sequence of elements, i.e., it can be changed after it is created.

- Lists are created by placing elements inside square brackets `[]`, separated by commas.

- Lists can contain elements of different types, including other lists.

- Lists are **ordered** and **indexed** (i.e., you can access elements by their position).

---

## Lists: Indexing and Slicing

.font80[
.pull-left[
- **Indexing:** Accessing individual elements in a list.

- Starts from 0 (for the first element) and goes up to `n-1` (for the last element).

- To reference the last element, you can use `-1`, and so on.

- You can call a particular item out of a list by using its index. For example, `my_list[0]` will return the first item in the list.  
]

.pull-right[
- **Slicing:** Accessing a subset of elements in a list, using the `:` operator, where: `my_list[start:stop:step]`.   
  - If `start` not provided, it defaults to 0   
  - If `stop` is not provided, it defaults to the length of the list.  
  - If `step` is not provided, it defaults to 1.    
      * `my_list[:3]` returns the first three elements.  
      * `my_list[3:]` returns all elements from the fourth to the last.  
      *`my_list[::2]` returns every other element, and   
      * `my_list[::-1]` returns the list in reverse order.
]
]

---

## Python List Functions

| List Function          | Description                                           | Example                                 |
|------------------------|-------------------------------------------------------|-----------------------------------------|
| `len()`                | Returns the length of the list.                      | `my_list = [1,2,3]` <br/> `length = len(my_list)`<br/>`# length is 3`                          |
| `max()`                | Returns the maximum value in the list.               | `my_list = [1,2,3]` <br/> `maximum = max(my_list)`<br/>`# maximum is 3`                        |
| `min()`                | Returns the minimum value in the list.               | `my_list = [1,2,3]` <br/> `minimum = min(my_list)`<br/>`# minimum is 1`                        |
| `sum()`                | Returns the sum of all elements in the list.         | `my_list = [1,2,3]` <br/> `total = sum(my_list)`<br/>`# total is 6`                            |
| `sort()`               | Sorts the list in ascending order.                   | `my_list = [3,2,1]` <br/> `my_list.sort()`<br/>`# my_list is now [1, 2, 3]`                     |

---

## Python List Functions (Cont.)

| List Function          | Description                                           | Example                                 |
|------------------------|-------------------------------------------------------|-----------------------------------------|
| `index(item)`          | Returns the index of the first occurrence of `item`. | `my_list = [1,2,3]` <br/> `my_list.index(2)`<br/>`# index is 1`                        |
| `append(item)`         | Adds `item` to the end of the list.                  | `my_list = [1,2,3]` <br/>  `my_list.append(4)`<br/>`# my_list is now [1, 2, 3, 4]`              |
| `pop(index)`           | Removes and returns the element at `index`.          | `my_list = [1,2,3]` <br/> `popped = my_list.pop(1)`<br/>`# popped is 2,` <br/> `# my_list is [1, 3]`     |
| `remove(item)`         | Removes the first occurrence of `item` from the list.| `my_list = [1,2,3]` <br/> `my_list.remove(2)`<br/>`# my_list is [1, 3]`                |

---

## Class Activity: List Manipulation

.panelset[
.panel[.panel-name[Task]

In pairs, build on the code below to:

``` python
# your path will be different
with open("../../data/simulated_logs.txt", "r") as file:
    lines = file.readlines()

# Strip newline characters from each line
log_entries = [line.strip() for line in lines]
type(log_entries)
```

1. Show how to get the **total number of log entries**.  
1. Show how to slice the **first 5 log entries**.  
1. **Filter out all** log entries that contain the word **ERROR**.  
1. **Count** the number of **ERROR** entries.
]

.panel[.panel-name[Solution]

In [Google Colab](https://colab.research.google.com/) (or your favorite Python environment), write code to address the questions in the previous tab.

]
]

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class: inverse, center, middle

# Recap

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## Summary of Main Points

By now, you should be able to do the following:

- Use pseudocode to map out a problem.

- Python Syntax, Data Types, and Data Structures

- Convert data types using type casting

- Manipulate lists and use methods on lists

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## 📝 Review and Clarification 📝

1. **Class Notes**: Take some time to revisit your class notes for key insights and concepts.
2. **Zoom Recording**: The recording of today's class will be made available on Canvas approximately 3-4 hours after the end of class.
3. **Questions**: Please don't hesitate to ask for clarification on any topics discussed in class. It's crucial not to let questions accumulate.

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## 🎯 Assignment 🎯

Please work on and submit [Assignment 02](https://miamioh.instructure.com/courses/229048/quizzes/702078) prior to our next class.