Smart Data: 3 Case Studies of Machine Learning in Fundraising Analytics

Track: Data Analytics Symposium

Session Number: 1135
Date: Wed, Aug 8th, 2018
Time: 11:00 AM - 12:00 PM
Room: Room 317-318

Description:

As our institutional data continues to growth, so do the challenges and complexities to extract insights with the same brain power. This session provides an overview of machine learning as a tool to unlock the value of our data and maximize the impact of our analytics and research efforts.
We present 3 common machine learning algorithms for supervised and unsupervised classification, what questions they help us answer and how to implement them in Python and R.
Sub-Categorization: Start-Up Track
Session Type: Breakout Session

Primary Competency: DA:Competency 4: Statistical Techniques and Competencies
Secondary Competency: DA:Competency 1: Cross Industry Standard Process for Data Mining (CRISP-DM)
Tertiary Competency: DA:Competency 3: Data Manipulation Skills
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R or Python helpful but not required.
Learning Objective #1: Attendees will gain an understanding of 3 of the most common machine learning algorithms , and when to apply them.
Learning Objective #2: Attendees will learn the general steps to implement 3 common machine learning algorithms in Python or R.
Shop Size: All Shop Sizes
Sub-Categorization: Start-Up Track
Session Type: Breakout Session

Primary Competency: DA:Competency 4: Statistical Techniques and Competencies
Secondary Competency: DA:Competency 1: Cross Industry Standard Process for Data Mining (CRISP-DM)
Tertiary Competency: DA:Competency 3: Data Manipulation Skills
Intended Audience Level: Level I
Recommended Prerequisites: Some knowledge of R or Python helpful but not required.
Learning Objective #1: Attendees will gain an understanding of 3 of the most common machine learning algorithms , and when to apply them.
Learning Objective #2: Attendees will learn the general steps to implement 3 common machine learning algorithms in Python or R.
Shop Size: All Shop Sizes