This document provides an agenda and information for an information meeting of a Data Science club. It begins with an agenda that will discuss what data science is, the goals and changes for the club that quarter, membership, and a Q&A session. It then defines data science as being interdisciplinary and involving many areas of study. It discusses the proliferation of machine learning and some industries where data science is applied. It also outlines some of the common tools used in data science like programming languages and data analysis/visualization software. Finally, it discusses the goals of the club to help members build skills through group projects, speakers, and providing opportunities to learn and present.

2. Information Meeting
Winter Quarter

3. Agenda
• Data Science – What is it?
• Where this club comes in
• Goals and changes for this quarter
• Membership
• Q & A
• Cookie time! Meet other students and the officers

4. Data Science
• Very interdisciplinary
• mathematics, statistics, econometrics, computer science, including
signal processing, probability models, machine learning, statistical learning, data mining, database, data
engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data
warehousing, data compression, computer programming, artificial intelligence, and high performance computing.
• Machine learning proliferation into companies and
governments
• Comes into play in… agriculture, marketing optimization, fraud
detection, risk management, marketing analytics, public policy, speech recognition, health care,
bioinformatics
• A data scientist could be a data feature engineer, algorithm
developer, implementer, data warehouse engineer, etc.

5. Data Science -- The Tools of the Trade
• With all these disciplines come a wide array of tools
• Exploratory data analysis: R, Python, SQL
• Data Visualization: d3.js, Tablue, R
• Data Computation: Apache Spark/Hadoop, MapReducing
• Predictive Modeling/ML: Python, R, SAS

6. Goals for DS@UCSB
“To prepare students for successful careers in
data science and analytics”
…Okay that’s great… but how?
Help members build their confidence and intuition with data
science tools through group lead projects, peer education, and
industry speakers.

7. Group Projects & Peer Education
• Projects for all skills levels!
• Beginners: tutorial sessions, learn to master new programming
languages
• Intermediate: data visualization, feature engineering
• Advanced: Kaggle competitions, UCLA DataFest
• Anything could be a project if it has data and you’re learning
• Most projects will conclude with a short group presentation
• Even mastering a new language—present it to the club!

8. Company Guest Speakers
• Hear from companies that work with data in a bunch
of different fields
• Graphiq – Data visualization, journalism and research
• HG Data – Data Consulting
• Google – Machine Learning and Quantitative Analysis
• LinkedIn – User habits and marketing

9. Membership
• Alternating general and members-only meetings
• Membership: $5.00 per Quarter
• Retention rate, keep groups together
• Goes towards: food, operating costs, club advertising, bringing
in company guest speakers
• Priority entrance to guest speaker talks, ability to attend
member meetings, and access to recruiting events, publicity of
your projects
• Future meetings for this quarter will be held at this time