In the information age, anyone can learn anything if they have passion and dedication.
At the beginning of 2018 I started teaching myself how to code in Python. Shortly thereafter, I left my corporate job, got a part-time job at a daycare, and started studying data-science and software engineering full time. My passion sustained me. I spent many night in front of a computer screen, studying coding tutorials and discussing on data science forums.
I still treat every day as an opportunity to learn something new. Recently, I have started working as a data science freelancer for various research teams and businesses across the US. I am also pursuing a computer science Masters at the University of Texas at Dallas.
Over the next few months I will be interning at Sprint as a data scientist.
The first step in building amazing machine learning applications is collecting and understanding data.
I build intelligent systems that use text and speech data to understand, describe, and interact with people.
I have a deep understanding of the theory, implementation, and best practices that make machine learning applications successful.
I am learning about horizontally scaled databases. I hope to implement these ideas soon.
I implement machine learning models in real world production systems using REST APIs.
I maintain DigitalOcean servers for database storage, model training, and model deployment.
Server Management (Linux / SSH) - 3
Database Manipulation (SQL) - 3
Cluster Computing (Spark) - 1
Visualization and Presentation - 5
Data Munging - 4
Statistical Methods - 3
Deep Learning (Keras) - 4
Natural Language Processing - 4
Predictive Modelling (SKLearn) - 4
Take a look at my recent work.
I recently collected data from 1.4 million stories of Medium.com.
I used the data to make the first public analysis of Medium stories, to create a performance metric to fuel authors, and lastly I published the full dataset for Medium's community of data-scientists.
In this article, published in Towards Data Science and KDNuggets, I recount my months of self-study and gave advice and resources for the aspiring data-scientist.
This article was the most shared story on KDNuggets for October. It was also well received on Medium.com, receiving 22k reads since publication and breaking the 99.9th percentile of claps for all Medium articles.
In this project I experimented with regularization in convolutional neural networks. I found that removing dropout layers increased
performance in image recognition tasks.
I published an article in Towards Data Science and KDNuggets detailing the experiment and my results.