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a-mystery-in-two-parts

This project allows me to show the SQL skills I've learned in the GT course. SQL stands for “Structured Query Language.” Put simply, SQL is the language that we use to interact with a database. SQL is the most widely-used database language as it’s incredibly effective at manipulating data. My database can be seen as the third part of the client-server relationship. Essentially, the client sends a request to the server, and the server grabs the associated data from the database. Then the retrieved data is returned to the client by the server. In this project I worked with PostgreSQL, or Postgres, a free, open-source object-relational database management system.

adoptabledogsintheusa

Visualization

belly-button-biodiversity

This project I’ll use a new plotting tool, Plotly. Why use Plotly instead of Matplotlib or another library? While Plotly.js is not necessarily "better" than Matplotlib or another plotting library, it is useful for a variety of reasons! Plotly is also incredibly popular in the data community. Plotly.js uses both D3.js and WebGL to render graphics, works with JSON schema, and supports a large variety of map charts, from basic to statistical, financial, and scientific!

brewery-startup

Each project that tackle serves multiple purposes, and they will all be highly critical in your development as a data professional. First, group projects allow me to start developing a collaborative workflow that is very similar to what I would experience in many jobs in the field. Working in a group also allows me to accomplish more and create more impressive projects than I would be able to alone. This project will be my first opportunity to build a data presentation from the ground up.

citibike

This project utilizes Leaflet. This project showcases the Citi Bike API to get the status and location of every Citi Bike station in New York.

decoding-aircraft-emergencies

Team Project 3 - Visualization that includes Python Flask–powered API, HTML, CSS, JavaScript, and at least one database

deep-learning-charity-funding-predictor

indianfood-rainfall

This project allowed me to combined my SQL skills, Pandas, Python, and more. The team and I picked a top and used the ETL process to do an analysis.

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About Me

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Portfolio of GT Data Science Bootcamp Homework & Projects.

kickstart-my-chart

This Excel project uses pivot tables, lookups, conditional formatting, scatter plots & trend lines, regression creation & moving averages calculations for analysis.

mission-to-mars

In this project I used the data I found on the web in my project using a concept called web scraping, as well as connecting to a new type of database, MongoDB. While SQL is an extremely powerful language for defining and manipulating data, it can actually be restrictive. SQL requires you to use predefined schemas to determine my data structure, and requires that all of my data follow the same structure. A NoSQL database like MongoDB has a dynamic schema for unstructured data and can store data in a variety of ways. This gives it much more flexibility in practice as not all data will come structured in the same way.

predicting-credit-risk

Supervised Machine Learning

predicting-hospial-readmission-for-diabetes

py-me-up-charlie

The purpose of this project was to demonstrate the use of the core Python language to store and manipulate data, read and write to files, and "translate" logic-based solutions to Python implementations of those solutions. This work highlights the work a Chief Data Scientist for PyCity school and analyze the district's standardized test results. The data was aggregated to showcase trends in found in the schools performance. After the analysis was preformed a presentation was created using Pandas. The final report included: District Summary of all schools with an analysis of the total budget, average grades, average passing averages.

pycityschools

The purpose of this project was to demonstrate the use of the core Python language to store and manipulate data, read and write to files, and "translate" logic-based solutions to Python implementations of those solutions. This work highlights the work a Chief Data Scientist for PyCity school and analyze the district's standardized test results. The data was aggregated to showcase trends in found in the schools performance. After the analysis was preformed a presentation was created using Pandas. The final report included: District Summary of all schools with an analysis of the total budget, average grades, average passing averages.

stock-market-ticker-analysis

VBA is also a great tool you can use to automate the data manipulation that we do in Excel. VBA is also prevalent in many older companies and in the financial industry.

surfs-up

In this project I’ll be demonstrating my advanced SQL skills with a new toolkit to store and retrieve datasets. SQLite is a dialect for using an SQL database. The syntax is very similar to the PostgreSQL syntax, with the main difference between the two being that SQLite is entirely serverless. SQLite allows for the power of database storage but with a tiny footprint. SQLite is also conveniently part of the standard Python library, so no additional setup is needed to get it up and running. SQLite is not comparable with PostgreSQL or any other SQL language, rather it focuses on providing local data storage for individual applications. What does ORM mean? ORM stands for Object-Relational Mapper. Object-relational mapping allows me to write SQL queries using the object-oriented paradigm of the language. In other words, for our purposes in class, it allows us to interact with our database using Python. Python is an "Object-Oriented Programming (OOP) language", which means that it is highly concerned with organization and reusability. Classes are crucial to OOP in that they allow us to group related things and keep them together. A class is essentially a template that allows us to create objects, which have variables and behaviors associated with them. It helps streamline my code and create efficiencies whenever something needs to be used various times.

the-power-of-plots

This work showcases Python and adding in a package with lots of new methods and functions that make working with data easier in the long run. Python is a scripting language, while Pandas is a library that works on top of Python and makes data manipulation easier. Libraries are pre-written chunks of code that make it easier to unlock the capabilities of a programming language.

unsupervised-machine-learning-cryptocurrency-clusters

Unsupervised Machine Learning

visualizing-data-with-leaflet

Maps! Datasets should be visualized and explained in different ways depending on the background context of the data and what the stakeholders are most interested in learning. Oftentimes, the geography in the data is highly relevant. For example, a marketing stakeholder would be interested to know the popularity of their product in specific states to understand how to best move forward with their campaigns.

weather-html-dashboard

This project creates a visualization dashboard website using visualizations we've created in past assignment. Specifically, plotting weather data. In building this dashboard, I created individual pages for each plot and a means by which I can navigate between them. These pages will contain the visualizations and their corresponding explanations. I also have a landing page, a page where we can see a comparison of all of the plots, and another page where I can view the data used to build them.

whats-the-weather-like

An API, or application programming interface, is a pre-written set of code that helps you interact with somebody else’s information. Using APIs will give us the ability to programmatically obtain and parse data from sources like OpenWeatherMap, the US Census, and more. In this project I plotted Data Frames from the API data using Matplotlib. This is one of the ways that I was able to source outside data to use for my projects, and its a great opportunity to practice working with external data. When working with APIs - documentation is key! Each API I use has a different set of rules and guidelines and half the fun is learning all the features each one can provide. Using Python request, APIs, and JSON traversal to answer a fundamental question: "What's the weather like as we approach the equator?" In this project, I'll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I use a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. To assist in future vacation decisions, a heat map that displays the humidity for every city will be generated using the weather data, jupyter-gmaps, and Google Places API.