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GOAL

Draw meaningful conclusions from data using Python programming and Scientific Computing principles

2025-08-18_v1.0

Scientific Computing &

Data Visualization with Python

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Objectives

Learn what code is

Learn about libraries

Download a dataset

Code with Python in Google Colaboratory

Write a program to explore and visualize your data

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PART 1

Scientific Computing and Code

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Examples of Scientific Computing !

USING HIVE DATA TO SAVE THE BEES

IMPROVING ATHLETE PERFORMANCE THROUGH BIOMETRIC DATA

BUILDING MODELS TO PROMOTE WILDLIFE CONSERVATION

MAPPING OUR FUTURE IN SPACE EXPLORATION

Scientific computing is just using computers to solve problems in our world, so it truly is all around us, and I’m sure you guys can think of ways that computing is relevant in your own lives! It’s never been easier to think of how integral computers are to society, but here are a few more ways that computing is impacting our world in awesome ways that we might not have thought of! For example, we can collect data with sensors in bee hives and then write algorithms that can detect stress for bee populations early so we can intervene and save bee populations that are suffering! We can follow similar models to conserve our ecosystem and protect our planet and other wild animals as well. Another super cool application of computing is to use athlete wearables to track their performance data and optimize their training and prevent injury, or even to guide safe missions into space, like companies such as SpaceX, NASA, or Blue Origin are doing!

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ANSWER ME!

So let’s think… what are some topics you are interested in?

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Getting Things Done

So how do we actually get these things done?? Coding!

…But what do computer programs look like???

And what actually is code?

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What is coding?

Coding is simply telling a computer what to do by giving it instructions!

We write these instructions in programming languages.

We write programs in a development environment.

The answer might be simpler than you think! Coding is literally just telling a computer what to do by giving it very specific, ordered instructions which are in the form of a program. So of course, we write these programs in programming languages because despite what ChatGPT and advanced AI models might make us think, computers cannot understand english (or any written language) and we need to pick from one of the many different programming languages to get computers to do what we want!

In this lab, we will use Python to code in because it’s extremely popular, easily accessible, and also pretty easy for beginners to learn and understand!

In addition to choosing a language, we also have to choose where we write the code which makes sense because, as you guys can imagine, we can’t type code directly into the Google search bar or on this Google Slides deck and have it run – we need to code in a development environment or somewhere that can actually execute the code we write!

For this lab, we will use Google Colaboratory which is a really cool development tool that allows you to code in files that save directly to your google drive so it kind of looks like something I’m sure you’ve seen before since certain aspects of the file work in the same way that a Google Doc or a Google Slides presentation would! It’s also very easy to use and offers some functionality to better organize your code and things like that!

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Understanding Code

Even though code is written in a different language, you might be able to interpret and understand it better than you think!

Watch this video to learn more about what code is, what it looks like, and how we can read and understand it!

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PART 2

Diving Into Python

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Python Code

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Each line of code usually does something!

print("Hello, World!")

Comments are special lines of Python code that don’t do anything

# This is a comment

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Let’s Try Google Colab!

Step 1) Open this Colab Notebook.

Step 2) Click the button located in the top left corner.

Step 3) From the dropdown menu, select

Step 4) A new tab should appear with a copy of the Colab Notebook. This is now your personal copy to edit and learn with!

Step 5) Feel free to rename your file in the top left corner next to the

Step 6) Read through all the contents of the Colab Notebook, run the cells, and do the activities to learn more about coding and try it out yourself!

Open the dropdown menu to copy the file here

Save a copy in your Drive here

Name your file here

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Python Libraries

What about when we need to do something more complicated?

We can always write more complicated code, or we can call upon pre-written instructions from libraries!

A library is essentially a collection of code that has already been written and is now shared for everyone to use!

By now, we’ve seen how coding is just giving instructions to the computer, and we’ve seen firsthand how we can write code, run it, and observe the output! What we’ve done so far is very basic – just setting variables and using arbitrary if statements, and obviously, we need to do a little bit more than this to get the impactful results we’re looking for, so does this mean we need to write super long programs and complicated code?? No! It doesn’t! We can actually do very complex things in our programs with very few lines of code by calling upon pre-written instructions from libraries!

So a library is essentially a collection of code that has already been written and is now shared for everyone to use. And like I said, we can use libraries to perform complex tasks with very little coding actually being done on our end! Also, just so you guys know, this is very common and is best practice in the coding industry, and these libraries are automatically baked into Python now, so we’re not just stealing someone else’s code – we’re using a functionality of Python and this is how you would see these tasks done by professionals as well!

To help us understand libraries a how we use them even better, think of a library like a toolbox: when we need to build something, we’re not going to start by building a hammer and melting down the metal to shape it and fusing it with the plastic: no. We know that we’re going to need that hammer for other things we want to build down the line anyway, so we don’t need to make it from scratch every time. We can just get the hammer out of our toolbox and use it for whatever we need at any point. In this scenario, libraries are our toolbox where we keep handy tools that we know we will need, and we can just call upon their functionality or “take tools out” of them any time we need to use them in our other projects!

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Using Libraries

import library_name as alias

alias.function_name(parameters)

Pandas

Pandas is a library that helps you load in and work with data.

Example Usage:

import pandas as pd

df = pd.read_csv("my_data.csv")

Plotly Express

Plotly is a library that helps create graphs and make interactive visualizations. Plotly express is a simplified version that helps streamline code even more.

Example Usage:

import plotly.express as px

fig = px.scatter(df, x="age", y="income")

fig.show()

To use a library’s functions, you have to first import the library into your code so its tools are available to you. This is basically like taking your toolbox into the room that you’re working in. If I was building a bookshelf in the living room but my toolbox was in the garage, I would not be able to get my hammer out of it! So we have to bring the toolbox into where we’re working. This is super simple, and you just do this by typing “import” and then the library name. During this step, we can also give the library a nickname, called an alias, that we will use to refer to the library. This is commonly done by shortening the library name to 2 letters, and usually most people will use the same aliases for the common libraries. You can see the format for this here is import library_name as alias!

Then, when we want to call specific functions from the library, we call upon the alias, and then call the specific function from there. We might also have to pass some information into the function for it to give us what we want! So this will look like alias.function_name with parentheses and any parameters inside the parentheses.

2 common libraries that we will use in this lab are Pandas and Plotly. So pandas is a super important library for loading in and working with data and you will definitely use it a lot if you are in this field. And then Plotly is a common way to graph, and you may also see matplotlib, which is a very common graphing library too!

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Pandas DataFrames

A DataFrame is simply a 2 dimensional table of data in Python.

They’re used to organize and store data, label rows and columns, and easily work with and manipulate different types of data all in one place.

DataFrames also have built in capabilities to help you load in, clean, explore, and analyze your data!

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PART 3

Dealing With Data

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Data

We code to understand data and the world around us!

Data is everywhere!!

With scientific computing, we can turn this data from just a bunch of numbers into real, valuable insights that can improve our lives!

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CSV Files

In this lab, we will use CSV files, which stands for comma-separated values. CSV files are one of the simplest and most commonly used ways to store and organize data, where each row represents a new record and each column represents a different variable!

Watch this video to learn more about CSV files.

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PART 4

Your Turn! Creating A Data Visualization

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Your Task: Using Sci Comp to Visualize Your Own Data

Select a data file of your choosing – pick something fun!
Load the data into a Colab Notebook
Work with a Python code skeleton to explore the data
Create multiple visualizations to help us understand what the data tells us!

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WHAT YOU’LL DO:

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Your Turn! Find a Dataset

Now, it’s your turn to find a dataset! Think about what interests you, and search for data on a topic you enjoy. As long as it’s a CSV file, you can use any dataset you want, so make it fun!! We recommend starting your search at one of the following websites:

DATA.GOV

KAGGLE.COM

This website contains lots of information from the U.S. government!

From the link above, scroll down to the sidebar section titled “Format” and select “CSV.” From there, type any topic in the search bar to see all CSV files about it!

Once you’ve found a file, download the CSV version!

Kaggle has all kinds of datasets about unique and fun topics, but you will have to log in to access them.

Navigate to “Datasets” in the sidebar, select “CSV” from the filters located in the search bar, and search away!

Once you’ve found a file, scroll down to where you see the table, and download from there!

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Back to Google Colab!

Now, we’re going to work with a new Colab Notebook to take a look at this data! Following the same initial process as last time, do the following:

Step 1) Open this Colab Notebook.

Step 2) Click the button located in the top left corner.

Step 3) From the dropdown menu, select

Step 4) Work through your copy of the Colab Notebook up until the cell that tells you to return to the slides! Make sure you read through the code and try to understand what is happening!

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Thinking About Your Data

Now that everyone has loaded their data and started to explore it, let’s come back together and think about the next step.

Ask good data questions help us uncover patterns, trends, or relationships�

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Types of Graphs in Plotly

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Chart Type	Code Example	When to Use
Scatter	px.scatter(df, x=..., y=...)	To compare two numeric columns
Bar	px.bar(df, x=..., y=...)	To compare quantities across categories
Line	px.line(df, x=..., y=...)	To show changes or trends over time
Histogram	px.histogram(df, x=...)	To show the distribution of a single numeric column
Box plot	px.box(df, x=..., y=...)	To compare distributions & spot outliers across groups

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Customizations in Plotly

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Parameter to Add	Purpose
title="Your Chart Title"	Adds a title to your chart
color="another_column"	Adds color groups
labels={"column_x": "X Label", "column_y": "Y Label"}	Renames axes
template="plotly_dark"	Recolors the chart (you can google other templates for different color schemes)

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Your Task: Creating Your Own Visualization!

Work through all of these steps with your own dataset that you have selected!

Follow along with the code given to you, and then make your own chart from scratch!

Make sure your chart includes the following:

A different chart type than a scatter plot
A title that explains what your chart shows
At least 1 more element of your choosing from the table on the previous slide!

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Continue to Explore

If you liked today’s activity, you may be interested in these topics:

Scientific Computing
Data Science
Computer Algorithms

Types of engineering relevant to Scientific Computing:

Computer Scientist
Mechanical Engineer
Essentially any STEM discipline!

If you want to dive deeper into this topic, Engineering Tomorrow offers a full Scientific Computing course which can be completed independently of this lab. We will teach you to code from scratch and help you work on an awesome project to learn more about sci comp and boost your resume!

If you’re interested, ask your teacher to talk to our ET staff!

As you work through this lab, if you enjoy it, there are a lot of wonderful other topics for you to explore in this field. If you’re curious to learn more about computer science principles as a class, we have ET labs about Software Engineering, Machine Learning, Cybersecurity, Robotics, and more!! Also, ET has developed a full Scientific Computing course that is designed to teach you how to code for scientific computing skills through a series of interactive modules, assignments, and lessons! We have staff that will guide you through the course, give you feedback, and help you produce a final product that will teach you a lot about sci comp and help you market yourself and your skills for future internships and opportunities! This course is a lot of fun and also does not require any prior knowledge of coding. If you’re interested, feel free to have your teacher reach out to our staff and we’d love to give you more information about when you can next register for this course!

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Thank you!