Titanic: Machine Learning from Disaster

To enter the world of machine learning competitions, I decided to join Kaggle.com’s Titanic: Machine Learning from Disaster competition. This competition does not award the winners with any prices, but instead, is more of a mock competition that makes great practice for junior data scientists. The competition contains data describing the passengers that occupied the Titanic on its maiden voyage in 1912. The ship struck an iceberg, sinking the ship, and killing 1502 out of 2224 passengers and crew. …


At the beginning of my journey to becoming a data scientist, I discovered Kaggle.com. I had never heard of this site before, and there was a reason why — it is a mecca for data sets, and I had never sought out data sets prior. Kaggle is a site for data scientist, data analysts, or anyone else who needs access to data sets, or who may want to improve their data cleaning or machine learning skills. It is chockfull of data sets to chose from, most of which are uploaded by users who have accessed the data elsewhere via downloads…


Amazon has not only cornered the online shopping market, but they have also staked a substantial claim in the cloud service market with Amazon Web Services, or AWS. Cloud services provide developers, individuals and companies alike, a tool box filled with numerous micro services that range from data storage to robotics and machine learning, or from app development to streaming desktop applications. Not only does AWS offer such a wide range of micro services, but in addition, these tools are constantly improving and expanding. Given that these services are remote, users can launch massive data movements using a simple personal…


This week marks my one month as a professional data scientist! After I completed my course, my number one fear was — am I ready to complete the tasks I learned in school, in an industry setting? So far, the answer has been a resounding yes. It is such a great feeling when what you learned in school is exactly what you needed to know to be successful in your career. I know this wasn’t the case after I completed university as a chemist, or even for others who have completed similar, or the same, immersive data science bootcamps as…


Building Web Apps as a Data Scientist

By the time I had completed my data science bootcamp, I was ready to take on statistical analysis, machine learning, data visualization, and so many other data challenges. And while I understood the basis of a webpage, HTML defining the content of a webpage and CSS defining the style of the webpage, I was far from understanding the process of web development. I knew this was gap in my portfolio, and I had already faced the need for building simple web applications. For instance, recently, I had the need to create a polling application which would allow users to define…


When beginning my official job search as a data scientist, I knew the importance of keeping my GitHub portfolio active. Always learning new skills and keeping a pet project are two of the most important tasks that someone entering the tech industry should be doing. Lately, I have learned some important aspects about GitHub that I will talk about in this post, and I hope that it will answer questions for others in the future.

On the front page of a GitHub profile, there is a live calendar showing user commits in the last year. The shade of green signifies…


Predicting Weather Conditions in Jena, Germany

We have come to the end of this time series project. Let’s wrap this thing up! As a reminder, here is what this project is about. The goal of this project is to build a time series model that will make predictions for future weather conditions. The data used for this project was extracted from kaggle.com and contains climate data collected in Jena, Germany from 2009 to 2016. A time series model built from climate data can have a huge impact in the real world and can help predict and/or understand natural disasters, global warming, or even agriculture production. …


Predicting Weather Conditions in Jena, Germany

Here is a quick update to help pick up where I left off: the goal of this project is to build a time series model that will make predictions for future weather conditions. The data used for this project was extracted from kaggle.com and contains climate data collected in Jena, Germany from 2009 to 2016. A time series model built from climate data can have a huge impact in the real world and can help predict and/or understand natural disasters, global warming, or even agriculture production. …


Predicting Weather Conditions in Jena, Germany

A time series data set is one in which the progression of time is crucial to the values that are present. Considering time is never ending, and always progressing, time series data can be found and analyzed in a plethora of domains. One of this most important, and well known time series dataset, is the New York Stock Exchange. The New York Stock Exchange provides the market place for buying and selling stocks, constantly updating the ever changing price for each and every investment. Companies and individuals will perpetually analyze the data extracted from the New York Stock Exchange in…


Exploratory data analysis, or EDA, is one of the most important things a data scientist needs to complete on any project. As the name implies, it is the deep exploration of your data, most often completed with visualizations such as bar plots, scatter plot, etc. From EDA, a scientist will uncover relationships between different variables, or possibly, important missing data. No matter where the data is sourced from, it is always important to complete in-depth EDA on your project.

I personally discovered the importance of EDA the hard way, on a project I completed which predicted whether or not a…

Stacy Shingleton

I am a data scientist, formally a traditional scientist, from the University of Texas.

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