How to Learn Anything

Track: Data Analytics Symposium

Session Number: 1224
Date: Wed, Aug 8th, 2018
Time: 10:15 AM - 10:45 AM
Room: Room 317-318

Description:

This session will walk participants through the process of learning anything. Specifically, we will learn some techniques using R and Python however the process could be applied to learning anything. While participants will leave with some concrete tactics that they can employ immediately the emphasis will be on the process we used to learn these concepts so they can continue to grow and expand their knowledge after the conference.
Sub-Categorization: Start-Up Track
Session Type: General Session

Primary Competency: DA:Competency 3: Data Manipulation Skills
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 9: Change Management/Strategic Thinking
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R and/or Python may be helpful but it is absolutely not required.
Learning Objective #1: Participants will get a blueprint for learning that they can apply to learning anything
Learning Objective #2: Participants will learn two concrete examples: extracting a calculated value from a data set and visualizing data in a way that highlights the strengths of R and Python.
Shop Size: All Shop Sizes
Sub-Categorization: Start-Up Track
Session Type: General Session

Primary Competency: DA:Competency 3: Data Manipulation Skills
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 9: Change Management/Strategic Thinking
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R and/or Python may be helpful but it is absolutely not required.
Learning Objective #1: Participants will get a blueprint for learning that they can apply to learning anything
Learning Objective #2: Participants will learn two concrete examples: extracting a calculated value from a data set and visualizing data in a way that highlights the strengths of R and Python.
Shop Size: All Shop Sizes