Learning Pathways

What self-guided resources does Arcus Education offer?

A lot! In this section, we will introduce you to our main categories of resources. We offer two ways to browse this information: (1) as self-guided resources by modality (e.g. webinars, articles), and (2) learner pathways that match context to your specific use case, role, and skill level.

Recorded webinars

Many Lab Down? Skill Up! data skills webinars are avaialble for you to view at any time. View a list of recorded webinars on a wide range of topics.

Glossary of terms

Just starting? Trying to figure out if this site will be relevant to you? A good place is to begin with the glossary of terms, and then choose “Arcus Orientation” as your “educational pathway” below to get a selection of articles that probably makes the most sense for what you need.

Tutorials and articles

Further along the path? You can use the topics & search page as well as tags (keywords). Tags are at the top of each post and are clickable, or you can go to the tag listing.


Learner pathways (alpha)

We curate and offer all Arcus Education webinar resources into pathways that match the specific uses cases and skill levels of our learner communities. Currently our learning pathways are in alpha development - expected to see a full release by end of June 2020. But take a look at some of our articles and tutorials already grouped into pathways for you!:

Curious about Arcus?

Your first stop in understanding Arcus is to grasp why we exist and how we want to help your science advance!

You may also want to understand a bit more about Arcus services:

Tabular Phenotypic Data Researcher (the Excel Guru)

Do you work with data that can be displayed appropriately in a table (like a spreadsheet in Excel)? Are your data files on the smaller size (< 100k rows, <500M file size)? You're a perfect candidate to learn more about using reproducible tools to prepare and analyze your data. If you do most of your analysis in Excel or in a program like SPSS by using point-and-click (not syntax or code), you'll want to get up to speed on a few topics quickly:

Data Combiner

You're used to doing advanced work on your own data, but you're suddenly faced with trying to integrate, clean, and prepare data from several sources, possibly including large clinical datasets. You need to be able to "munge" (reshape prepare for computation) a bunch of data quickly and reliably. Try:

Statistical Novice

It's been awhile since your last statistics class, and you need to brush up on basics like null hypothesis testing, correlations, linear models, effect sizes, and picking the right statistical test for your experiment. Check out these articles!

Poster Maker / Visual Thinker

You're statistically solid and don't need much help with the analysis portion of your research, but you would love to be able to generate better publication-quality graphics (or even just exploratory visualizations for yourself!). You would do well to check out:

Networker

You work with data that is interconnected: family members, linked by common heredity, community members connected by exposures to contaminants at particular locations, scientific authors who are linked by collaborations, dimensional aspects of phenotypes that are connected to genes or exposures, etc. You want to use elements of graph theory to store, visualize, and analyze your data. Start here!

*omics Researcher

You work with data that is big -- large files (measured in gigs). You need high performance computing and advanced methods. You'll want to know about:

Predictive Model Builder

You already use a statistical programming approach to prepare, analyze, and visualize your data. Now you want to learn how to use machine learning to come up with predictive models based on your data. Flogging Stack Overflow is not how you want to spend your day. You may be interested in:

Language Researcher

You've assembled data that consists, at least in part, of natural language. You want to learn how to analyze texts by sentiment, part of speech, keywords, etc.