Due to proliferating digital data, one of the fastest growing industries is data science. In this class you will create a Python data science model within the sphere of COVID-19 impacts. The products will be data science correlation tables, graphic plots, QGIS maps, and a published dashboard. You’ll be equipped with skills that are marketable with almost any agency trying to turn data into knowledge.
Use Python, Jupyter Notebooks, and QGIS
Harvest data and assess schema problems
Write Python scripts to run models
Build a random forest model
Interpret model coefficients and correlations
Join attribute tables to spatial files
Create a dashboard and graphic plots
Reevaluate variables and stacked models to improve predictions
Join this class and get access to 10 lessons that give you the ability to build a data science model and visualize the results in GIS.
- The Sea of Big Data
(aka Harvesting the right data needed to analyze the problem)
- The Rag and Mop
(aka Scrub your data to clean it up)
Spot the Indicators
(aka Figure out the variables that your problem is sensitive to)
(aka Write a data science model)
Did it Work?
(aka Interpret your resulting numbers)
(aka The model makes predictions on other datasets)
(aka Where the data science model gets hooked up to GIS)
Where's the Problem
(aka Visualize the numbers and locations)
(aka Author a web dashboard)
Tackle the Model Again
(aka Reiterate your model using other datasets and hyperparameter changes)
You also get access to these bonuses
A list of black box and white box models
Exploratory Data Analysis guide
A Python data cleaning script
What You'll Learn
With these skills you will get an introduction to data science and learn to integrate the results with GIS which is a quickly growing skill combination.
Become a dual threat with data science and GIS
You will cross train on two exploding fields.
Write a data science model
You will work with code and statistics.
Connect results to a GIS dashboard
You will learn how data science supports GIS visualizations.
Who Should Attend
The GIS industry is projected to be worth $18B by 2023. Despite this massive growth in new applications, there hasn’t been a single place for all of us to quickly learn tried and true skills from successful Subject Matter Experts (SMEs). Until Now! The people that purchase this course are:
- College students and graduates looking to get fast track industry insights
- Government professionals looking to retrain themselves and remain relevant
- Corporate professionals looking to maintain a competitive advantage with relevant projects.
About the Instructor
Melissa Anthony has 16 years of experience in the GIS/Data Science industry with a strong background in anthropology. She has worked as an archaeologist for several companies throughout Colorado, Wyoming, and Belize. She was a GIS Supervisor for an electric company covering 5,000 square miles of territory, 11 counties, and 160,000 customers. She’s a graduate of a Data Science Fellowship from General Assembly where she collaborated with veteran Data Scientists on the latest data science methods. She currently works as a Data Scientist for Xcel Energy.
Why my course is useful
Both GIS and Data Science are growing fields. It’s obvious that the data explosion has created the need for more people to do analysis of data to make sense of it all. And it’s a natural feed to GIS which is often the best visual representation of any data informed issue.
Why I built it
I’ve worked as a GIS analyst and managed GIS’s for non dynamic features like cultural resources. And I learned there is so much data out there, free and widely available. I wanted to use that to take actionable steps in providing tools to local governments and non-profits to make data-driven decisions.
What I hope for you
I hope you take the class and feel the rewarding feeling of creating actionable insights out of data. There’s an ocean of data out there and we need to inform decision makers more quickly with the best data science to save our planet. Every student in my class is a seed to new world insights!
Here's a Reminder Of EVERYTHING You'll Get Inside of 'Analyzing pandemic food insecurity using machine learning and GIS'
- Big Data harvesting
- Data cleanup
- Python scripting of a data science model
- Analysis of statistical models and coefficients
- Run model predictions
- Matching model outputs with GIS
- Generate plots and a map dashboard
- Hyperparameter model tuning