DataPhilly Workshops
Workshops 2020
September 2020
From Stored Data to Data Stories.
- Instructor: Paul Kowalzyck.
- Detail: This workshop will take one through the steps associated with an end-to-end machine learning campaign: data retrieval; data curation; model construction, evaluation, selection and interpretation; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.
- Workshop files can be found here
October 2020
- Instructor: Kayleigh Smoot.
- Detail: SQL is the back-end language for direct data manipulation for many major websites, databases, and computer systems around the world. This workshop is intended to give you a clear understanding of basic SQL syntax and concepts.
- Interactive Workshop Examples: JDoodle
- Slides and Data Files can be found in this folder
November 2020
Tidyverse: How R should be used.
- Instructor: Zane Murphy.
- Detail: R has been rapidly growing as one of the most widely used programming languages for data science in recent years. However, in its most basic form, R is complicated, a bit choppy, and generally doesn’t provide much benefit over its main competitor, Python. Enter Tidyverse, a collection of packages maintained by the R foundation for reading, cleaning, processing, modeling, and visualizing data. In this workshop, we will learn the best practices for coding in the Tidyverse and will come to understand why it is giving Pandas a run for its money. We will cover everything from installing and configuring the latest versions of R and the Tidyverse to building a model on sample data and visualizing the results in a markdown document.
- Workshop files can be found here or here.
- The files include details on software and packages to be downloaded (WorkshopInfo.pdf), cheat sheets, and .rmd file (parking_violations_nb.Rmd) used in the workshop.
December 2020
Hyperparameter Optimization
- Instructor: Ben Attix.
- Detail: Attendees will be able to:
- Define a hyperparameter and explain why they are important
- Describe specific hyperparameters
- Understand different methods for hyperparameter tuning
- Perform hyperparameter optimization
- Build a model from scratch while incorporating hyperparameter optimization
- Workshop Setup Instructions:
- Mac Users:
- Open terminal
- Change directory:
cd Desktop - Clone the repository:
git clone https://github.com/benattix/ml_tutorials.git - Change directory:
cd ml_tutorials - Create new conda environment with .yml file:
conda env create -f environment_mac.yml - Activate new environment source activate hptuning
- Launch a Jupyter notebook:
jupyter notebook
- Change directory:
- Open terminal
- Windows Users:
- Open git bash
- Change directory:
cd Desktop - Clone the repository:
git clone https://github.com/benattix/ml_tutorials.git
- Change directory:
- Open anaconda prompt
- Change directory:
cd Desktop/ml_tutorials - Create new conda environment with .yml file:
conda env create -f environment_windows.yml - Activate new environment:
conda activate hptuning - For 64-bit operating system only:
- Install NumPy version 1.19.3:
pip install numpy==1.19.3
- Install NumPy version 1.19.3:
- Launch a Jupyter notebook:
jupyter notebook
- Change directory:
- Open git bash
- Mac Users:
January 2021
Satellite Imagery Analysis with Python
- Instructor: Simon Kassel.
- Detail: Participants will learn the basics of working with geospatial data in Python. They will learn how to generate basic analytics using both vector (e.g. points, lines, and polygons) and raster (e.g. satellite imagery) datasets. The workshop will also discuss how to prepare imagery and labels for training machine learning models. Throughout the process of the workshop, attendees will be introduced to indispensable open-source geospatial libraries like GDAL, Rasterio, GeoPandas, and Shapely.
- Workshop downloads and setup instructions can be found here
- Video recording of workshop
May 2021
Workshop: Geospatial Data Analysis
- Speaker: Dr. Fei Jiang
- Detail: Landcover classification using satellite data (e.g. Landsat8, Sentinel2, etc.) is becoming more and more popular as it is the foundation for many other projects (city planning, farm management, etc.). However, downloading large-sized satellite images (GBs) can be time consuming and requires large memory space. On the other hand, Google Earth Engine Python API provides an easy and fast way to get access to and query satellite data to points of your interest. In this workshop, we will learn how to use Google Earth Engine Python API to get time-series Sentinel2 data to two groups of points: points in urban area and points in crop lands, and then visualize and compare time-series Sentinel2 spectrums of points in urban areas and crop lands. If time permits, we will explore how to build up a machine learning model for landcover classification (cropland vs. urban land) using the above time-series data we obtain.
- Workshop downloads and setup instructions can be found here
- Video Recording of Event