Here’s what helped me succeed in my immersive program.

While I was completing an immersive data science program, I often found myself turning to additional resources to solidify my understanding of key concepts and practices. Below is a list of resources that I found useful and complimentary to my studies. This list is a solid starting place for anyone interested in the field of data science or those looking to dive in deeper to a specific area.

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Photo by Markus Spiske on Unsplash

Kaggle

Kaggle is a platform for participating in machine learning competitions. …


Using Machine Learning to detect clickbait

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Source: Pexels

The term “clickbait” refers to an article headline written with the sole purpose of using sensationalist language to lure in a viewer to click through to a certain webpage. The webpage then generates ad revenue on the user’s clicks or monetizes the user’s activity data. The article itself is not written with journalistic integrity, research or really striving for any deeper meaning — it is simply a vehicle to monetize user clicks and data.

With the explosion of social media, smartphones and the state of an increasingly digital world , there is no shortage of content vying for our attention. …


A text classification project with Jude Buenaseda

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Source: Pixabay

As the impacts of climate change become more and more prevalent — the necessity for alternative energy resources becomes even more imperative. And thus, the discussion around nuclear energy is impossible to avoid as it is a key contender whose efficiency cannot be denied. However, nuclear energy has become a divisive and polarizing topic as its byproducts can have devastating effects on human health and the environment without the proper precautions.

For this project, we wanted to understand if sentiment could be easily identified around nuclear energy and what that looks like. With the help of Twitter and its users’ opinions — we were able to attempt this classification. Our goals for this project were to first get a read on public opinion regarding nuclear energy by classifying the sentiment around tweets and then extract value in terms of key words or themes associated with each sentiment. This would be incredibly useful for both companies and policymakers in terms of optimizing and tailoring messaging and strategy around what their audience is talking about. …


What to forage, what to avoid? A Python classification project

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Source: Pixabay

In recent years, the popularity of mushrooms as a superfood and the understanding of their vast health benefits has surged. What used to be typically seen as just a traditional food, mushrooms are now being widely consumed and acknowledged for their healing and health abilities. With more and more people interested, foraging for mushrooms in the wild is likely to increase and I wanted to understand if edible or poisonous mushrooms could be easily identified and what that looks like.

The Data

The raw dataset utilized in this project was sourced from the UCI Machine Learning Repository. …


Unpickling Pickle

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When you think about pickles in the real world, a jar full of small cucumbers or some other vegetable immersed in a vinegary brine probably comes to mind. And if you take a moment to ponder pickles a bit further, a few additional thoughts may come to mind — for one, pickles are delicious. But two, the process of pickling is all about preserving and extending the shelf life of a food for later use.

The Pickle in Python is more or less the same idea; preserving Python objects for later use.

I was first introduced to Pickle when I started diving into Machine Learning algorithms and creating my own models. Pickle allows you to preserve your model that you’ve just trained, fit and tested to predict on new data at a later time. This is particularly useful if your model took hours to train — instead of having to train it again, you can just preserve (pickle) it and then unload it later for immediate use! But, how exactly does it work, and why? …


A renewable energy love story

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Almost three years ago, Bill Gates wrote an open letter to the graduating class of 2017 and it’s still very relevant today. He offered advice for those looking to make a big impact in the world, and three fields they should consider: artificial intelligence, energy and biosciences.

One is artificial intelligence. We have only begun to tap into all the ways it will make people’s lives more productive and creative.

The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change. …


A linear regression project using Spotify song data

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This project idea recently came to me after participating in a bit of Zoom quarantine fun — a Zoom facilitated music bracket. The week prior, each participant was tasked with nominating four songs that they felt the group did not know but would enjoy. All participants spent a week listening to the choices and prepped for casting their votes for each matchup of songs. Spoiler alert: my songs did not go far — songs that I was so sure of, that I personally listened to over and over again. My failed choices left me seeking to understand if song popularity can be predicted and what that looks like. …


A beginner’s attempt . . .

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Animation of school shootings in the US from 2009–2018. Created in Python using Seaborn.

There are many ways to make static graphs in Python — such as with the use of Matplotlib, Pandas, and Seaborn, to name a few. And I think it is safe to say that successfully making a static graph is one crucial tool for a beginner data scientist to have in their toolbox.

After feeling pretty confident in my static graph-making, I stumbled across a graph that takes the art of graph-making to a whole other level — that being the animated graph. I can admit that I am a sucker for animated visuals, and likewise, find animated graphs both mesmerizing and impactful. Something about seeing the plot ‘grow’ on its own, and doing all the visual work for the reader. I also find that it really emphasizes the point that the creator is making. …


an EDA project in collaboration with Jude Buenaseda

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The Scenario

CompanyX sees all the other movie companies creating original video content, and they want to get in on the fun. CompanyX decides that their breakout original content will be a sci-fi movie. They really want to capture the niche and create a success with their first original feature. CompanyX has enlisted your team for consultation.

And with that, this project aimed to analyze the sci-fi movie genre through exploratory data analysis to ultimately understand successes within the genre and provide insight as to what type of films should be created to capture the sci-fi market. …


Examples from COVID-19

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As the coronavirus pandemic continues to unfold each day, and while most of us are confined to our homes, it is almost impossible to avoid the latest news and daily briefings. And in this endless stream of updates and information, there seems to be one constant — and that is the use of data. It seems as though every article or news report is sharing or referencing data visualizations, data-driven conclusions and modeled predictions, and even just data.

Datasets are being compiled and updated daily and pushed to the masses. The use of data throughout this pandemic has undeniably provided a face and something tangible, to an otherwise invisible enemy. As a new student to data science, I wanted to dig into some examples and use cases that really show how powerful data can be in real life settings. …

About

Alison Salerno

Data Science | Machine Learning www.linkedin.com/in/alison-salerno

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