How I did not deploy my first SARIMA COVID-19 forecasting model using Dash, Plotly, and Heroku.

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Photo by Ethan Hu on Unsplash

I did not deploy a SARIMA time series model using the statsmodels library that predicts future COVID-19 infection and death rates. Using Plotly to create interactive graphs of current and predicted case and death rates, allowing users to to decide which statistics to include, which countries or states to predict, and how far out to predict, I did not make a publicly accessible and interactive predictive website. I worked hard and learned a lot in not deploying this model to a Heroku server.

Here is my story.

In this walkthrough you will learn to deploy a website to Heroku that does NOT make machine learning predictions. …

Can we predict whether a student will pass an online course without knowing anything about who they are?

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Photo by Frank Romero on Unsplash

Learning online has been a growing trend for decades now. In 2018, 35% of college students took at least one course online and 17% took all of their classes remotely (NCES study). With COVID-19 a reality, learning online has exploded and become a necessary health and safety issue for more people than ever. While students will eventually return to school, the industry has had opportunity, funding, and impetus to improve and expand. This will undoubtedly lead to a sharper rise in the importance of internet based learning in the post COVID future.

In my work as a teacher I leveraged online learning opportunities and digital learning environments to allow students to learn at the pace that was right for them. The could take more time here and zip through there, rather than being forced to march at the pace of the rest of their peers. Virtual learning also streamlined data collection and analytics to help me plan interventions and adjust curriculum. Services such as Dreambox, Khan Academy, Coursera, and Udemy became vital tools for my self-driven learners. …

Predictive analytics, human expertise, data mining, and empathy come together to improve graduation rates for tens of thousands of students, many the first in their families.

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Photo by Element5 Digital on Unsplash

My first year of college was hard in so many ways. I had never lived away from home, my friends, family, and girlfriend were far away, and I didn’t know anyone. I was on my own for the first time and encountering some of the most difficult challenges I had yet faced. But, my struggles were invisible. I didn’t reach out to campus services, and they did not know I needed them. If you had asked me, I would not have been able to tell you who my academic advisor was or if I even had one. I had a rough time and nearly quit more than once. …

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Photo by Xavi Cabrera on Unsplash

How fun is it to explore? As data scientists, we are all about discovery and interacting with data. Folium allows you and your audience to explore data with interactive maps, and it is quick and simple to set up.

Folium is a python library that allows you to combine the amazing data wrangling libraries of python and the beautiful mapmaking abilities of Leaflet.js. With just a few lines of code in your IPython Jupyter Notebook, you can produce eye-catching interactive maps to help your audience explore your data in a visual and geographical way. …

If you love Pandas and MatPlotLibs’s Pyplot, you’ll love GeoPandas!

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Photo by Capturing the human heart. on Unsplash

Beautiful data deserves beautiful visualizations, and GeoPandas makes displaying geographical data easy for anyone who knows Pandas and MatPlotLib. It’s one thing to show your readers tables of data or bar charts, but putting it on a map makes your story more tangible.

GeoPandas comes with a handy starter shapefile, which can be loaded with:

shapefile = geopandas.datasets.get_path('naturalearth_lowres')
earth = geopandas.read_file(shapefile)

Shapefiles like these are available on the web for maps of just about anyplace. The files are tables with data and a column that defines the shape of and location of each region you want to plot.

For example, OpenStreetMap offers many shapefiles in zip formats at geopandas.read_file() is a powerful function that can sometimes find the right file straight out of a zipfile, but it’s sometimes easier to unzip it first. A shapefile often actually consists of several files (.shp, .dpf, .shx, etc) and given the path to one of these, GeoPandas will automatically check the same directory for the rest it needs and merge them into the complete geodataframe! So cool! The documentation on this flexible and powerful import tool can be found here. …

From Educator to Data Scientist.

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Photo by NeONBRAND on Unsplash

My middle school students frantically drew blurbs of text on the margins of large sheets of butcher paper. In groups of four and each with their own colored marker, they discussed, deciphered, and paraphrased articles of the U.N. Declaration of Human Rights to decode their meanings. The would use this understanding as part of their projects on modern-day slavery and human trafficking.

I took comprehensive observational notes and counted the contributions of each member by their marker color. At the end of the class period, the students took a survey asking how they enjoyed the group work, whether they felt they and their peers contributed their fair share, and answered an assessment question. …


Josh Johnson

I'm a data scientist with a background in education. I empower learners to become the folks they want to be.

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