introduction to data science · 2011-01-18 · introduction to data science week 1, lecture 1 jeff...
TRANSCRIPT
Introduction to Data ScienceWeek 1, Lecture 1
Jeff HammerbacherJanuary 18, 2011
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Wednesday, January 19, 2011
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Lecture Outline▪ Course Content
▪ What we’ll cover▪ What we won’t cover
▪ Course Logistics
▪ Meeting time and location▪ Prerequisites
▪ Course Motivations
▪ Personal▪ Putting data to work▪ The emergence of Data Science
▪ Homework!
Wednesday, January 19, 2011
Course ContentWhat We’ll Cover
▪ Data Collection and Integration
▪ Data Presentation
▪ Experimentation
▪ Longitudinal Analysis
▪ Data Products
▪ Final Project
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Course ContentWhat We Won’t Cover
▪ Data Mining
▪ Artificial Intelligence
▪ Statistics
▪ Machine Learning
▪ Knowledge Discovery in Databases
▪ Big Data
▪ Relational Databases
▪ NoSQL
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Course Logistics▪ Course Website: http://datascienc.es
▪ Instructors: Jeff Hammerbacher, Mike Franklin
▪ Course Times: 12:30 pm - 2:00 pm, Tuesday and Thursday (Citris 240)
▪ Office Hours: 2:00 pm - 4:00 pm, Thursday (Soda Hall 449)
▪ Mailing List: [email protected]
▪ Prerequisites
▪ Python▪ Web Programming▪ Statistics
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Course MotivationsPersonal
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Course MotivationsPersonal
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Course MotivationsPersonal
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Course MotivationsPersonal
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Course MotivationsPersonal
“Information Platforms and the Rise of the Data Scientist”
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Course MotivationsPu!ing Data to Work
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1935: “The Design of Experiments”
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Course MotivationsPu!ing Data to Work
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1955: “Artificial Intelligence”
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Course MotivationsPu!ing Data to Work
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1958: “A Business Intelligence System”
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Course MotivationsPu!ing Data to Work
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1977: “Exploratory Data Analysis”
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Course MotivationsPu!ing Data to Work
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1989: “Business Intelligence”
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Course MotivationsPu!ing Data to Work
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1995: TDWI
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Course MotivationsPu!ing Data to Work
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1996: “From Data Mining to Knowledge Discovery in Databases”
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Course MotivationsPu!ing Data to Work
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1997: “Machine Learning”
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Course MotivationsThe Emergence of Data Science
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1994: “Managing Gigabytes”
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Course MotivationsThe Emergence of Data Science
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1996: Google
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Course MotivationsThe Emergence of Data Science
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2007: “The Fourth Paradigm”
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Course MotivationsThe Emergence of Data Science
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2007: “The Case for DISC”
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Course MotivationsThe Emergence of Data Science
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2008: “More Data Usually Beats Better Algorithms”
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Course MotivationsThe Emergence of Data Science
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2009: “The Unreasonable Effectiveness of Data”
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Course MotivationsThe Emergence of Data Science
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2007: “Competing on Analytics”
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Course MotivationsThe Emergence of Data Science
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2007: “Super Crunchers”
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Course MotivationsThe Emergence of Data Science
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2007: “The Coming Exaflood”
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Course MotivationsThe Emergence of Data Science
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2008: “The End of Science”
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Course MotivationsThe Emergence of Data Science
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2010: “The Data Deluge”
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Course MotivationsThe Emergence of Data Science
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2010: “What is Data Science?”
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Homework!▪ 1. How does X work?
▪ 2. How would you build X if you had to start from scratch?
▪ 3. Why is X useful?
▪ Where X can be:
▪ Google Analytics▪ 23 and Me▪ Standard and Poor’s bond ratings
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Wednesday, January 19, 2011