Week 8

I didn’t think I’d ever be the kind of person to say this, but learning Python gave me some serious data superpowers. When I started my Python class, I didn’t know much — just that it was a popular language and people used it for “data stuff.” But as the weeks went on, I realized Python wasn’t just about writing code — it was about understanding how to work with information, solve problems, and build things that actually do something.

We started with the basics, like how to make variables, loops, and if/else stuff. At first it felt like I was learning a new language (because I literally was), but once I started writing small programs, it started clicking. The first time I got my code to run without blowing up was a good moment — and yeah, I celebrated a little.

The projects were the best part. I made stuff like a calculator, a number guessing game, and some simple apps in Replit. It wasn’t just about passing the class — it felt like I was building real things. Every time I figured out how to fix a bug or make my code do what I wanted, it felt like a win.

One of the biggest takeaways for me wasn’t even the coding — it was how I started thinking. The class taught me how to break things down, figure out problems step by step, and just keep trying until it worked. That’s something I know I’ll use even outside of coding.

Now that the class is over, I’m still messing around with Python on my own. I’ve been trying out Replit and even played around with a no-code app builder called Loveable. Python gave me the base to build off of, and now I feel like I can actually make cool stuff.

If you're thinking about taking a Python class, go for it. You don’t need to be a computer genius — just be ready to learn, mess up, and keep going.

Week 7

 

Loveable is a no-code app builder designed to help anyone turn their ideas into real, working apps—no programming required. Whether you’re creating a simple planner, a habit tracker, or something totally unique, Loveable gives you the tools to design, customize, and launch your app with an easy drag-and-drop interface. It’s beginner-friendly but still flexible enough for more advanced users who want to dig deeper.

To get started, I browsed through Loveable’s template library. There were a ton of options
— from budget trackers to daily planners. I chose to build a basic productivity app. The platform's layout builder was easy to use — more like working with building blocks than writing code. I could choose button actions, page layouts, and even set up custom reminders.

The database setup was what I expected to be the hardest part, but Loveable made it visual and beginner-friendly. I added fields for task titles, deadlines, notes, and even categories — all without touching a single line of code. The app could save and retrieve data without needing any external setup, which was a big plus.

Publishing the app was probably the most exciting part. Loveable let me test it on my phone, fine-tune a few things, and then share it with friends. It ran smoothly, looked great, and most importantly, it worked like I’d hoped.

Week 6

 Making an app on Replit was simple and straightforward. I didn’t have to install anything or go through a complicated setup—everything was ready to use in the browser. I started by choosing the HTML, CSS, and JavaScript template, which came with three separate files for structure, style, and functionality. I decided to build a basic to-do list app where I could add and remove tasks.

The process of writing the code was clear. I used HTML to create the layout, CSS to apply some basic design, and JavaScript to handle the user interactions. Every time I clicked “Run,” the changes appeared right away in the preview window, which made it easy to keep track of progress. If there was a mistake, I could fix it quickly and see the updated result immediately. Replit also helped catch small errors, and the console was useful for checking what was going wrong.

When the app was working, sharing it was simple. Replit gave me a link that anyone could open in a browser, and it worked on mobile too. I didn’t need to deal with hosting or setting up anything else. By the end of the process, I had a working app built entirely online. The platform made everything easy to understand and manage, even for someone still learning how to code.

Week 5

 Probability and statistics are core components of data science, helping practitioners explore data, make predictions, and evaluate model outcomes. Chapter 5 of Probability and Statistics for Data Science offers a focused introduction to these key ideas, using clear Python examples to show how they apply in practice.

The chapter first covers descriptive statistics, which summarize important features of a dataset. Metrics like mean, median, mode, range, variance, and standard deviation help describe how data is centered and spread out. With libraries like NumPy and pandas, the chapter walks through how to calculate these measures using both basic lists and real-world datasets, including the Titanic passenger data.

Next, it explores probability distributions, particularly the normal distribution, which commonly models real-world patterns like human height. Readers learn to simulate and visualize distributions using Python and Matplotlib, and then apply this to actual data using the Iris dataset and Seaborn for detailed visual analysis.

By combining theory with real examples, the chapter builds a solid foundation in statistics and probability—skills that are essential for anyone looking to move forward in data science or machine learning.

Week 4

 In Chapter 3, I learned how to use Python to deal with and clean data, using two important libraries: NumPy and Pandas. NumPy excels at performing mathematical operations on large collections of numbers, whereas Pandas assists in organizing, correcting, and cleaning up unorganized data. Because it can handle far larger files, automate repetitive activities, and give you much more control, Python is far superior to spreadsheets alone. Additionally, connecting to additional tools is simple in case you wish to undertake more complex tasks in the future.

 For the Chapter 3 exercise, I used a video game sales dataset in Google Collab. First, I uploaded the file and looked at the first few rows to get an idea of what the data looked like. Then I cleaned it up by getting rid of any rows that were missing information and deleting any duplicates to keep everything neat and correct. I also changed some of the column names to make the data easier to read and work with.

After cleaning the data, I grouped it in different ways to find some cool insights, like which gaming platforms sold the most, which genres were the most popular, and which video games had the highest sales overall. Then, I used NumPy to figure out the average sales for each genre and how much the sales numbers changed from game to game.

Overall, this exercise showed me how useful Pandas really is when working with data. Pandas makes it easy to clean up messy datasets, organize information, and find important insights without a lot of complicated steps.

Week 3/ Control Structures

 For week 3 I spent time working on control structures including conditional statements and loops. Control structures are important because they allow you to tell what code needs to be put into place. There are two loops which are for loops and while loops. For loops are used by working through a sequence executing a block of code for each item in the sequence (Data Toolkit: Python+ Hands-On Math). While loops are used when a given condition remains true throughout the block of code. Conditional statements include if statements and if-else statements. If statements occur when only one condition is met, while if-else statements occur when one condition is met, and the other condition is not met. I spent time working on these control structures on google collab and while some of it was plug and chug, I realized that just having one wrong indentation can affect you.

The first exercise I completed was printing numbers 1-10 using a for loop. This exercise was pretty simple, but the next one was where I had my first bump. When I tried to create a while loop, I came across an error and my error was that my indentation was wrong. Indentation is key when using python. If it is not used correctly this is when you run into errors. After indenting for the while loop, my processing was correct, and the numbers were printed out. The next exercise was trying conditional statements. I successfully completed the first if statement and also tried it out using different numbers than the one that was given. I also completed an if-else statement and had no problems with indentation on this as well. The next exercise was setting up code to guess a word. This tripped me a little bit, but after pasting the code and fixing the indentations, I was able to set it up.

I learned quite a bit about control structures after going through the exercises in the book on google collab. The biggest thing I learned was how important indentation was and if you didn't have it correctly put in then you were going to have errors occur when put into place. Overall, I feel a little more comfortable with python and I am ready to see what else I am able to do complete and achieve with it.

 

Week 2/ Coding and Math Careers Audio

 For week 2 I listened to the audio piece given by Professor Kelsey. One of the first points Professor Kelsey makes is the impact AI has on different tech skills. New technology has become so prominent over the past couple of decades and AI is adding a whole new layer to it. While AI is replacing jobs, it is important that we know and understand how to use AI to benefit ourselves. Another point made in the audio is how AI is similar to calculators. Some people think that calculators defeat the purpose of math, but in reality, they help speed up the process of solving equations especially more complex ones. AI is similar to calculators because some people think it is defeating the purpose of learning, when in reality it is helping us solve much more complex problems that arise today. Another key point that was made is the fact that AI and chatbots aren't perfect, and it is important that we use AI in a way where we aren't hoping for the best, rather using it to correct ourselves.

Generative AI is already being used in many workplaces, so it is imperative that coming out of college we at least have some understanding of AI and the benefits it provides. The more knowledge we have on AI can only help us because it is becoming so widespread and used in many different places. Professor Kelsey also makes a really good point that the less you know about AI the less secure your job may be because it is so becoming so prominent in the workplace. If you actually know some of the ins and outs of AI, the better suited you are for your job because you can use it in ways that differ from others to help you. Having knowledge of AI and python is attractive to job recruiters since it is becoming more and more popular.

Rapid changes are happening with AI and the more we know about it the better suited we are for the effects it has on the job landscape and the world as well. I took Professor Kelsey's management class last year, and I saw some of these rapid changes that were taking place. I also learned that while AI is replacing some jobs, it is also creating many jobs. Having knowledge of python and AI can be so beneficial because it can help lead to different opportunities that are popping up with AI. The main takeaway from this audio in my opinion is that we need to use AI as a tool to benefit us and help achieve things that could not be achieved without it. AI is here to stay whether we like it or not, so we must use it in a way that can help us, rather than hurting us, so having knowledge about AI and python is so important.

Week 1/ Intro to Python

 Python is super popular in data science because it’s easy to use, simple to read, and can do a lot of different things. It’s more than just a coding language—it helps both beginners and experts work on big tasks like AI, machine learning, and analyzing large amounts of data. One big reason people love Python is because it comes with lots of helpful tools, like NumPy for math, pandas for organizing data, and scikit-learn for building smart programs. Plus, there’s a big community to help out, and the code looks clean and easy to understand. That’s why so many people start with Python when learning data science.

I personally had zero experience with python and the tools it offers before I joined this class. After reading chapter one, and trying one of the exercises on google collab, I realized it was much easier than I thought it would be using python coding. For the first exercise, I put print ("Hello World") into the code cell, and that was the first exercise I completed using Google Collab. Though, it was a simpler exercise for beginners, it helped my confidence but also made me realize that using Google Collab is similar to cooking. Just as you put whatever ingredients into the dish you are preparing, the same goes for python on Google Collab. What things you put into the system decides what is going to come out just like when you are preparing a dish. 

Overall, the first week taught me that coding, while complex, is easier when you understand and use the tools that are there for you.

# This prints a message

print("Hello, world!")

# Simple function

def greet(name):

    return f"Hello, {name}!"

print(greet("Aidan"))