Analytics Beyond Excel

Track: Data Science

Session Number: 2054
Date: Sat, Aug 3rd, 2019
Time: 9:15 AM - 10:15 AM
Room: Grand Sonoran F

Description:

Every institution can benefit from more analytics expertise, but it's often daunting for prospect development experts to expand their skills beyond tried and true methods in Excel. To bridge that gap, this presentation will introduce attendees to the R programming language from the perspective of those who are already comfortable analyzing data in spreadsheets. By learning R, one of the most popular programming languages for doing data science, attendees will be able to maximize the value of their organization's data. In this presentation, attendees will learn the R equivalents of intermediate Excel skills such as combining separate spreadsheets, summarizing data in pivot tables, or manipulating text. We will also sample some more advanced R topics, such as Web scraping, data visualization, and programming our own functions.
Session Type: Breakout Session (60min)

Primary Competency: DA:Competency 3: Data Manipulation Skills
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 2: Project Management
Intended Audience Level: Level I
Recommended Prerequisites: Intermediate knowledge of Excel
Learning Objective #1: Attendees will be able to explore and analyze data in R using techniques that are familiar to Excel users.
Learning Objective #2: Attendees will learn best practices on how to structure their analyses.
Shop Size: All Shop Sizes
Session Type: Breakout Session (60min)

Primary Competency: DA:Competency 3: Data Manipulation Skills
Secondary Competency: DA:Competency 5: Visualization/Reporting Techniques and Competencies
Tertiary Competency: DA:Competency 2: Project Management
Intended Audience Level: Level I
Recommended Prerequisites: Intermediate knowledge of Excel
Learning Objective #1: Attendees will be able to explore and analyze data in R using techniques that are familiar to Excel users.
Learning Objective #2: Attendees will learn best practices on how to structure their analyses.
Shop Size: All Shop Sizes