Max Joseph

Max Joseph has contributed to the materials listed below. Max is a data scientist with the Analytics Hub at Earth Lab and maintains this website.

Course Lessons

Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Max Joseph has contributed to the following lessons:

Data Workflow Best Practices - Things to Consider When Processing Data

Identifying aspects of a workflow that can be modularized can help you design data workflows. Learn best practices for designing efficient data workflows.

How Do You Create a Data Workflow - Design and Develop a Workflow For NDVI Over Time

Designing and developing data workflows can complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.

About the ReStructured Text Format - Introduction to .rst

Restructured text (RST) is a text format similar to markdown that is often used to document python software. Learn how create headings, lists and code blocks in a text file using RST syntax.

Introduction to Documenting Python Software

Lack of documentation will limit peoples’ use of your code. In this lesson you will learn about 2 ways to document python code using docstrings and online documentation. YOu will also learn how to improve documentation in other software packages.

The GitHub Workflow - How to Contribute To Open Source Software

Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.

Introduction to Open Source Software - What Is It and How Can You Help?

Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.

Guided Activity on Git/ For Collaboration

This lesson teaches you how to collaborate with others in a project, including tasks such as notifying others that an assigned task has been completed.

Guided Activity on Undo Changes in Git

This lesson teaches you how to undo changes in Git after they have been added or committed.

Guided Activity to Submit Pull Requests

This lesson teaches you how to submit pull requests on to suggest changes to another repository.

Guided Activity on Version Control with Git/GitHub

This lesson teaches you how to implement version control using Git and GitHub.

What Is Version Control

This lesson reviews the process and benefits of version control and how Git and GitHub support version control.

Interactive Maps in Python

This lesson covers creating interactive maps with Python in Jupyter Notebook.

Challenge Yourself

This lesson contains a series of challenges that require using tidyverse functions in R to process data.

Automate Workflows Using Loops in R

When you are programming, it can be easy to copy and paste code that works. However this approach is not efficient. Learn how to create for-loops to process multiple files in R.

Handle Missing Data in R


Use tidyverse group_by and summarise to Manipulate Data in R

Learn how to write pseudocode to plan our your approach to working with data. Then use tidyverse functions including group_by and summarise to implement your plan.

Get Started with Clean Coding in R


Submit a pull request on the GitHub website

Learn how to create and submit a pull request to another repo.

How to fork a repo in GitHub

Learn how to fork a repository using the GitHub website.

Introduction to undoing things in git

Learn how to undo changes in git after they have been added or committed.

First steps with git: clone, add, commit, push

Learn basic git commands, including clone, add, commit, and push.

An introduction version control

Learn what version control is, and how Git and GitHub are used in a typical version control workflow.

Programmatically Accessing Geospatial Data Using API's - Working with and Mapping JSON Data from the Colorado Information Warehouse in R

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

Programmatically Access Data Using an API in R - The Colorado Information Warehouse

This lesson covers accessing data via the Colorado Information Warehouse SODA API in R.

Introduction to the JSON data structure

This lesson covers the JSON data structure. JSON is a powerful text based format that supports hierarchical data structures. It is the core structure used to create geoJSON which is a spatial version of json that can be used to create maps. JSON is preferred for use over .csv files for data structures as it has been proven to be more efficient - particulary as data size becomes large.

Access Secure Data Connections Using the RCurl R Package.

This lesson reviews how to use functions within the RCurl package to access data on a secure (https) server in R.

An Example of Creating Modular Code in R - Efficient Scientific Programming

This lesson provides an example of modularizing code in R.

Introduction to APIs

In this module, you learn various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into R, reading in data stored in text format from a website, into a data.frame in R and finally, accessing subsets of particular data using REST API calls in R.

Use lapply in R Instead of For Loops to Process .csv files - Efficient Coding in R

Learn how to take code in a for loop and convert it to be used in an apply function. Make your R code more efficient and expressive programming.

If Statements, Functions, and For Loops

Learn how to combine if statements, functions and for loops to process sets of text files.

Create For Loops

Learn how to write a for loop to process a set of .csv format text files in R.

Working with Function Arguments

Learn how to work with function arguments in the R programming language..

Get to Know the Function Environment & Function Arguments in R

This lesson introduces the function environment and documenting functions in R. When you run a function intermediate variables are not stored in the global environment. This not only saves memory on your computer but also keeps our environment clean, reducing the risk of conflicting variables.

How to Write a Function in R - Automate Your Science

Learn how to write a function in the R programming language.

What Could be Improved In this R Code?

Write Efficient Scientific Code - the DRY (Don't Repeat Yourself) Principle

This lesson will cover the basic principles of using functions and why they are important.

Use Regression Analysis to Explore Data Relationships & Bad Data

You often want to understand the relationships between two different types of data. Learn how to use regression to determine whether there is a relationship between two variables.

Data tutorials