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Foundational Quantitative

Mathematics is a language through which you can represent and describe information in a way that makes it useful in interpreting new information, forecasting, and computing.

It helps us understand the world by building models of it in meaningful terms. Allowing mathematical proficient to be better critical thinkers, have mental discipline, and provide them with a way of abstraction of information. Abstraction can be considered the most important skill in creating or designing anything. Whether it’s artwork, research methods, or systems design and investigation, mathematics can always be handy in  daily life and many processes. Despite the experiences many people had in high school and college math, everyone can learn and work with the foundations of quantitative methods. 

Below are some of our favourite resources, examples, and scholars prepared to help introduce you to these topics. 

Quantitative Guide

This toolkit is a starting point for academic and non-academic communities to explore approaches to socially-distanced but deeply engaged qualitative research methods. Each method described has its own benefits and shortcomings, and place in the qualitative methodological tool kit. Our goal is to provide a practical foundation for imagining the range of what is possible in social research during lockdown, and then point to resources to more fully engage in learning these methods.

Access the Quantitative Guide

Quantitative Toolkit

Quantitative Buckets

Foundations of quantitative methods

Despite the experiences many people had in high school and college math, everyone can learn and work with the foundations of quantitative methods. There are a handful of core concepts from math that are critical to being able to engage with data.
Aug 03, 2021

Understanding data collection

With well-collected data in hand that has been cleaned and prepared for analysis, testing enables us to learn something about the world through data. Doing good testing of your data requires you to be very familiar with where it is coming from so that you can choose the right test and understand its limits. This is an exciting step in the data cycle, and we want to help make it enjoyable and rewarding for you!
Aug 03, 2021

Data cleaning, wrangling, and exploration

The insights and analysis of your data can only get as good as the data you are using. One of the most significantly important steps in dealing with data is data cleaning. It’s the process of handling data to make sure that it’s clear of any incorrect or corrupted data. In addition to removing or modifying any incorrectly formatted data or duplicates within a dataset. By learning how to preform data cleaning, wrangling and exploration you can gain powerful soft skills in addition to techniques to make your data easier to interpret and make your insights of higher significance.
Aug 03, 2021

Testing

With well-collected data in hand that has been cleaned and prepared for analysis, testing enables us to learn something about the world through data. Doing good testing of your data requires you to be very familiar with where it is coming from so that you can choose the right test and understand its limits. This is an exciting step in the data cycle, and we want to help make it enjoyable and rewarding for you!
Aug 03, 2021

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