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basicneeds

Our SMS-based application provides access to routing, emergency, mental health, sanitation, education, location, weather, and feedback services to vulnerable populations without wifi dependency. Basic Needs addresses immediate needs, connects vulnerable populations to their needs, and initiates a dialog between cities and vulnerable populations for social engagement and improvement.

creating_guis_using_pyqt4

Using the Python PyQt4 library to create a GUI layout with several functionalities.

fakenews

A collection of labeled fake news and real news (top credible news sources from https://webhose.io/) was used to train a Naive Bayes model to predict probabilities of fake news based on article text. The model predicted fake or real with 85% accuracy using ~1,100 articles in our testing set and a training set of ~4,000 articles. We cannot guarantee the correctness of our labels, given the subjectivity of the terms "fake" and "real". Therefore this tool is not a perfect judge of all articles, but can be used as a gentle guide.

functional_programming_in_scala

This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs. You'll see how the functional paradigm facilitates parallel and distributed programming, and through a series of hands on examples and programming assignments, you'll learn how to analyze data sets small to large; from parallel programming on multicore architectures, to distributed programming on a cluster using Apache Spark. A final capstone project will allow you to apply the skills you learned by building a large data-intensive application using real-world data.

orysyastus.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

powerbi-hierarchyslicer

Home of the code of my Power BI Custom Visual: HierarchSlicer

python-for-probability-statistics-and-machine-learning

Jupyter/IPython Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

sandiegohearts.github.io

We designed an interactive website https://sandiegohearts.github.io that examined the factors contributing to varying heart disease rates across San Diego to recommend ways to bring down the rates in the high risk areas. The goal is to help the potential users to make better decisions to run the campaigns and channelize the funds to the right regions.

seismicblog

Learn about data science at Seismic Software.

tableaututorials

trafficcruising-dssg2017

Public Repository for Traffic Cruising DSSG 2017 Project

ucsd_bioengineering_analysis

Reports, data analysis, or programming files related to Bioengineering Coursework from UCSD.

ucsd_data_mining_certificate

Modern databases can contain massive volumes of data. Within this data lies important information that can only be effectively analyzed using data mining. Data mining tools and techniques can be used to predict future trends and behaviors, allowing individuals and organizations to make proactive, knowledge-driven decisions. This expanded Data Mining for Advanced Analytics certificate provides individuals with the skills necessary to design, build, verify, and test predictive data models. Newly updated with added data sets, a robust practicum course, a survey of popular data mining tools, and additional algorithms, this program equips students with the skills to make data-driven decisions in any industry. Students begin by learning foundational data analysis and machine learning techniques for model and knowledge creation. Then students take a deep-dive into the crucial step of cleaning, filtering, and preparing the data for mining and predictive or descriptive modeling. Building upon the skills learned in the previous courses, students will then learn advanced models, machine learning algorithms, methods, and applications. In the practicum course, students will use real-life data sets from various industries to complete data mining projects, planning and executing all the steps of data preparation, analysis, learning and modeling, and identifying the predictive/descriptive model that produces the best evaluation scores. Electives allow students to learn further high-demand techniques, tools, and languages.

ucsd_data_science_and_engineering

In the Data Science and Engineering program, engineering professionals combine the skills of software programmer, database manager, and statistician to create mathematical models of the data, identify trends/deviations, then present them in effective visual ways that can be understood by others. Data scientists unlock new sources of economic value, provide fresh insights into science, and inform decision makers by analyzing large, diverse, complex, longitudinal, and distributed data sets generated from instruments, sensors, internet transactions, email, video, and other digital sources. Students entering the MAS program for a degree in Data Science and Engineering will undertake courses in programming, analysis, and applications management and visualization. This program requires three foundational courses, four core courses, and two electives totaling thirty-four units, plus a capstone team project course of four units, for a total of thirty-eight units.

university_of_michigan_python_for_informatics_certificate

This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

wastebotsunleashed.github.io

We extracted a sample of 40 littered Google Street View San Diego images for training and sample of a random sample of ~50,000 Google Street View images across San Diego, California for testing, processed the data, trained shape recognition and color recognition models, classified our images based on each model as littered/not littered, and designed an interactive website http://wastebotsunleased.github.io for visualizing which zip codes in San Diego are least and most littered. The goal is to engage public and government officials in keeping San Diego beautiful. This is a prototype and shows the potential of using data science for public benefit.