Mind the gap: effective strategies to help data science learners navigate the path from R novice to R programmer

Track: Data Analytics Symposium

Session Number: 1145
Date: Wed, Aug 8th, 2018
Time: 2:00 PM - 3:00 PM

Description:

One of the biggest pain points for both teachers and learners of data science in R is navigating the often unspoken prerequisite skills and content knowledge necessary to successfully apply R to data science problems. In this talk, R for data science educators will learn actionable strategies to more effectively bring learners up to speed, while learners will develop strategies to identify and address their own knowledge gaps.

By incorporating learnings from the establishment of a data-driven culture at Teaching Trust, coupled with her experience creating and leading the R for Data Science Online Learning Community, Ms. Mostipak will share strategies that can be immediately implemented with groups of any size in order to more quickly develop data science skills in R. These include methods for identifying individual gaps in knowledge, establishing mentor and learner relationships, incorporating best practices from the field of education, and bolstering fundamental computer science skills.
Sub-Categorization: Start-Up Track
Session Type: Breakout Session

Primary Competency: DA:Competency 9: Change Management/Strategic Thinking
Secondary Competency: DA:Competency 3: Data Manipulation Skills
Tertiary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Intended Audience Level: Level I
Recommended Prerequisites: None
Learning Objective #1: Session attendees will leave the session with concrete educational strategies that they can implement both in their own personal R learning journey, as well as use to develop the R and data science skills of others, regardless of group size.
Learning Objective #2: Session attendees will learn techniques for building, sustaining, and growing a learning community, as well as better understand the importance of community building on learning R and data science.
Shop Size: All Shop Sizes
Sub-Categorization: Start-Up Track
Session Type: Breakout Session

Primary Competency: DA:Competency 9: Change Management/Strategic Thinking
Secondary Competency: DA:Competency 3: Data Manipulation Skills
Tertiary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Intended Audience Level: Level I
Recommended Prerequisites: None
Learning Objective #1: Session attendees will leave the session with concrete educational strategies that they can implement both in their own personal R learning journey, as well as use to develop the R and data science skills of others, regardless of group size.
Learning Objective #2: Session attendees will learn techniques for building, sustaining, and growing a learning community, as well as better understand the importance of community building on learning R and data science.
Shop Size: All Shop Sizes