DEV Community: j

My Redemption Arc

Sun, 04 Feb 2024 23:18:55 +0000

As I approach the end of my Data Science bootcamp, I want to reflect on MY journey. If you had asked me about my life’s direction last year, I wouldn’t have a clear answer for you. The past couple of years has been challenging, with a bleak outlook on the future and stressful job searches.

The first week of the program hit me like a truck. I truly underestimated the intensity of what I had gotten myself into. Doubts crept in, and I questioned my capability. There were moments I didn’t think I would make it this far.

For context, I want to share a bit about myself. This is not for sympathy points but to have an understanding of where I am coming from. I have a learning disability and you may think a bootcamp is definitely NOT the place for someone like me. And I agree. The uncomfortable pace at which this program moves is almost impossible to follow. I’m surprised and proud of myself for getting this far, especially considering it has been absolute hell for me without medication. Due to a nationwide Adderall shortage, I have had to explore alternative methods to cope. With the right accommodations and self-kindness (reminder: this is all new and unfamiliar), this process has been having a positive impact on my mental and physical well-being. I had very unhealthy study habits when I was in college where I functioned on less than 5 hours of sleep, isolated myself, and had a poor diet. This time around I knew I could not continue that route or I would be burnout again. I have been neglecting my mental health for so long and now, it’s finally catching up to me. Trust me, nothing is more important than your well-being; it deserves to be prioritized and respected! This approach is crucial for positive learning experiences. I now try to get a good 7 hours of sleep, eat well(ish) (a lot of spinach!), and ask for help when needed (very important!!). A helpful practice for me is to add comments on every line of code. It not only helps others follow my thought process when reading my code but also reinforces my understanding of the materials and for future Julie, as well!

Things I wish I knew before starting the bootcamp

Most bootcamps advertise that anyone can do it and I DO strongly believe anyone can learn to code. For some, it will take more time and it’s perfectly okay (I’m in that boat too). That being said, these bootcamps are adversary to time. While there is prep work that needs to be completed before the start of the program, however, I don’t think that was enough preparation for what you are about to get yourself into. I can’t emphasize this enough: please be comfortable with the programming language you will be using before you begin. It is the foundation of everything you will be learning!!

If you are interested in doing Data Science, datacamp has been my lifeline in addition to the resources provided by my bootcamp. Note: If you do decide to use datacamp, don’t pay full price for it. They frequently offer discounts!

Hopeful

Looking back on my progress, I have learned a tremendous amount in a relatively short timeframe. I have worked on collaborative projects, created machine learning models, gained confidence in presenting my work, and I'm coding! I feel like I've been given a second chance to redeem myself. While there's still a long road ahead, I finally have a sense of direction and no longer feel like I am aimlessly wandering. Not to sound trite, but I see the light at the end of the tunnel and can't help but feel hopeful and excited about what lies ahead in my career!

I wish you the absolute best in your career journey. If you have any questions, feel free to reach out to me! Let’s connect!

I’m still hung up on p-value

Mon, 15 Jan 2024 21:54:04 +0000

If you are reading this, you are probably in the same boat as me; overwhelmed with all the statistical jargon. Why are there so many letter terminologies (p-value, z-score, z-test, f-test, t-test, etc.)?? For now, we will just focus on p-value.

First, let’s go over some definitions:

A null hypothesis is when we want to discredit an idea by assuming it is true and showing it is false with proof by contradiction. Null means nothing so the null hypothesis says there is no effect.

Example: There is no difference in test scores whether having studied vs. not studying.

An alternative hypothesis is the contradiction of the null hypothesis. It's usually the hypothesis we want to show.

Example: Studying for an exam will increase your test score vs. not studying.

The p in p-value stands for probability. A p-value is the probability of getting “extreme” data given that the null hypothesis is true. So, we would want to aim for a low p-value since we are trying to show the null hypothesis is false.

In the case of the example above, let’s say the p-value = 0.002 with an alpha value of 0.05. Here we are saying that there is a 0.2% chance of showing that there is no difference in test scores having studied or not.

Note: p-value does not tell us the probability that the null hypothesis is true or false! When we calculate our p-value, we have already assumed that our null is true.

This illustrates a right-sided p-value where T is the data and our observed value, T_o. [p-value = P(T ≥ T_o | null is true)]

Note: P-values are often two-sided. This allows us to reject the null hypothesis if our observed value significantly deviates from the mean, whether it's higher or lower.
[For two-sided: p-value = P(|T| ≥ |T_o| | null is true)]

Alpha Value
In null hypothesis significant testing, p-values need a cutoff to reduce the risk of drawing a false conclusion. This is known as the significance level aka the alpha value. Generally, we use an alpha value of 0.05.

A p-value less than the alpha value is sufficient evidence to allow us to “reject” the idea that the null hypothesis is true. When we reject the null hypothesis, we consider our result to be statistically significant. Statistical significance means it is not due to a random chance/luck in choosing the sample.

For example, alpha value = 0.05 means that there is a 5% chance of making the wrong conclusion under the true null.

How to select the alpha value:
An alpha value is between [0, 1].

It is just an arbitrary number but there are tradeoffs (so choose wisely):

If we select an alpha value that is too low, there is a higher risk of failing to reject the null hypothesis even though it is false.
If the alpha value is too high, there is a higher risk of rejecting the null hypothesis even though it is true.

This illustrates a type I error and a type II error.

Type I Error (aka false positive): Mistakenly rejecting the true null hypothesis, and accepting the alternative when it is false. We think we have detected an effect, but there isn’t one. The associated probability for this is alpha.

Type II Error (aka false negative): Mistakenly accept a false null hypothesis and reject the alternative when it is true. There was an effect, but we didn’t see it. The associated probability for this is beta.
Beta is related to something called power. Power = 1 - beta. It's the probability of rejecting the false null hypothesis.

Interpreting the p-value:

When p-value < alpha value: Reject the null hypothesis and in favor of the alternative hypothesis
When p-value >= alpha value: Fail to reject the null hypothesis

Is it possible to ever accept the null hypothesis?
There are only two options: either reject or fail to reject. This is because you can’t prove the null is true. It doesn’t mean that there isn’t an effect or relationship. We just did not have enough evidence to say there is one with certainty. The absence of evidence IS NOT the evidence of absence.

Source(s):
First image
Second image

Everything I Know So Far About Git/GitHub...for now

Tue, 19 Dec 2023 01:15:28 +0000

This is just the very basics of Git/GitHub as I am still learning the ins and outs of this complex tool!

Introduction
Git is a version control system that helps manage changes made to files and directories in a project. They allow you to keep track of what you did when, undo any changes you decided you don't want, and collaborate with others.

GitHub is a web-based hosting service for git repositories.

Basically, it is like when working on a Google Docs where you have multiple people making edits to a document and you can see the edit histories and possibly revert to an old version.

Basic Terminologies

Local repository(repo): a local directory containing code and files for the project. (aka your personal computer)
Remote repo: an online version of the local repository hosted on services like GitHub
Cloning: making a clone or copy of a repository in a new directory
Commit: a snapshot of the project you can come back to -Branch: a copy of the project used for working in an isolated environment without affecting the main project
Git merge: the process of combining two branches

Installation and Set Up
I don't feel well-versed enough to explain the installation process so I've attached a link on how to install and set up: Link

Git Basic Commands (These are my everyday commands)
1.) To create a local repo, clone a repo from a remote host(GitHub):
git clone <the_remote_repo_url>
(Note: you will need to fork a repo on GitHub to get the URL. Forking is getting your own copy of the repo)

Just click on the fork button and it will take you to another page, click create fork and then copy that url.

2.) See the current status of your local repository:
git status

3.) Create a branch:
git branch <name_of_branch>
(Note: 'git branch' can show you which branch you are currently on)

4.) Switch into an existing branch named :
git checkout <new_branch>

5.) Add all untracked/tracked files inside the current directory to git:
git add .

6.) Saving a snapshot of the staged changes with a custom message:
git commit -m "Commit message"
(Note: be careful of what you write for the message because it will be difficult to revise the message in the future)

7.) Saving staged/unstaged changes to stash for later use:
git stash
(Note: stash is great for when you want to save the edit but are not ready to commit the changes. You put them off to the side.)

8.) List remote repos in details:
git remote -v

9.) Push a copy of the local branch named branch to the remote repo:
git push <remote_repo> branch

For example:

(learn-env) julie@Julie ~ % git push origin main

Here, I am taking all the changes I made on my local repo (my computer) and "pushing" them onto my remote repo (in GitHub) which is called origin, and specifically onto my main branch.

10.) Pull a copy from remote branch named branch to the local repo:
git pull <local_repo> branch

For example,

(learn-env) julie@Julie ~ % git pull upstream main

So, same as the previous example. Here, we are “pulling” new updates from a remote repo (from GitHub) and syncing the changes to my local computer. Upstream is the name of the remote repo and main is the name of the branch you are taking the updates from.

Avoiding a merge conflict
Luckily, I have not experienced a merge conflict (fingers crossed). The only advice I can offer right now to avoid a merge conflict is to communicate with your collaborators when you are adding/committing changes to the repo. It always comes back to communication.

More Resources
This is just the tip of the iceberg with the commands.
Here's a list of some of the more commonly used commands for Git/terminal:
More commands

P.S. I will be continuously expanding on this list as I learn more features of Git/GitHub! :)

How did I get here?

Mon, 27 Nov 2023 07:03:27 +0000

We live in a world guided by data whether we are aware of it or not. Do you ever wonder - How can we market these goods to consumers? How are these products performing? How do we improve our standing in the polls? What do our constituents care about? How do we prevent traffic accidents? How do we use animal tracking to further understand animal behavior? Data makes our decisions, as it has for my career.

I have a Bachelor of Science in Mathematics with a secondary study in Digital Design and Fabrication. After graduating in 2019, I experienced a common phenomenon, as most college grads do - an existential crisis. Navigating life post-grad after being a professional student for the past two decades was quite daunting. I didn’t know where to begin. How was I supposed to figure out what I wanted to do for the rest of my life? Then, the Covid-19 pandemic emerged right as I was beginning to explore a career in civil engineering. Inadvertently it was also during this time that I was introduced to the transformative power of data.

In an effort to mitigate the spread of the coronavirus disease, scientists utilized data from cell phones to monitor the virus through people's interactions. Suddenly my iPhone had “exposure notifications” alerting me if I’ve been in close proximity to a person who recently tested positive for the virus. This was Apple’s version of participating in the contact tracing process. According to the World Health Organization, contact tracing is the process of identifying, assessing, and managing people who have been exposed to someone who has been infected with the COVID-19 virus. This process enables a swift response to identify the source and quarantine affected individuals, serving as an essential tool in preventing outbreaks. And contact tracing was my exposure to data science. Through contact tracing, I was able to have a clearer understanding of how data can be used.

In the past five years, in my pursuit of a defined career path, I have gained diverse work experience across various industries, ranging from retail and construction to government roles. While I have not yet had direct experience in handling data, we are in an era where data sharing is ubiquitous. Whether through making online purchases or engaging on social platforms or simply using a DNA test kit, it becomes imperative to understand how this information is being handled and leveraged.

Back in 2022, with my newfound interest in data science, I found myself contemplating whether returning to school was the right decision for me. On the one hand, there was a field I was interested in exploring. But, the cost and time commitment of graduate school did not seem feasible for me. While I did enjoy my time in college, it failed to provide the practical skills necessary for a career; it was learning for the pure joy of learning filled with captivating courses. Pursuing graduate school would mean perpetuating a cycle of theoretical learning, not adequately preparing me for the “real” world. And, in this world, you need a job. A boot camp bridges these gaps. The focus on rapidly building technical skills for a career in a short time frame was the solution I needed.

As I explored various boot camps, what distinguished (redacted_name) was its physical campus and in-person classes. A feature that resonates with my preferred learning style. Additionally, (redacted_name) offers scholarships. When browsing other boot camps' scholarship resources, they often led to a “do your own research” scenario. Generally, those scholarships are tailored for students applying to traditional institutions and are not applicable to boot camp students. Unfortunately, financial aid doesn’t extend support to boot camps either, as they are considered alternatives to conventional learning. Moreover, other boot camps use third-party loan companies with high-interest rates as their version of financial assistance. (redacted_name), however, not only offers various payment options but also provides monthly installment plans with zero interest - a highly appealing feature.

As of now, what I do know is I want a career in the field of data. I have a strong interest in America’s transportation network, particularly in public transit. Recognizing the pivotal role of reliable transportation, not only on an individual level but also on a global scale, is essential. From facilitating the transportation of imports and exports and supporting the tourism industry to the daily commute to work, a robust transportation system is vital for the seamless functioning of various aspects of society. I aim to utilize data to gain a deeper understanding of the current state of public transit in cities across America. The use of cell phone data during the pandemic has showcased its potential for studying people’s movements in public spaces, providing valuable insights that could inform transformative changes to those areas, ultimately contributing to the advancement and efficiency of public transit systems. Additionally, I also care deeply about conservation, and with the inevitability of climate change, it is more important than ever to contribute positively to our earth. Through this program, I’m excited to gain the skills necessary to become a data scientist.

SOURCE(S)
https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-contact-tracing