Introduction to Data Science
Section 1
Data Matters 2015
Sponsored by the Odum Institute, RENCI, and
NCDS
Thomas M. Carsey
carsey@unc.edu
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Course Materials
• I used many sources in preparing for this course:
– Practical Data Science using R by Zumel and Mount
– http://www.manning.com/zumel/
– Data Mining with R: Learning with Case Studies, by Torgo
– http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR/
– An Introduction to Data Science, Version 3, by Stanton
– http://jsresearch.net/
– Monte Carlo Simulation and Resampling Methods for
Social Science, by Carsey and Harden
– http://www.sagepub.com/books/Book241131/reviews?course=Course14
&subject=J00&sortBy=defaultPubDate%20desc&fs=1#tabview=title
– Machine Learning with R by Lantz
– http://www.packtpub.com/machine-learning-with-r/book
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Additional Materials
• A Simple Introduction to Data Science, by
Burlingame and Nielsen
• http://newstreetcommunications.com/businesstechnical/a_
simple_introduction_to_data_science
• Ethics of Big Data, by Davis
• http://shop.oreilly.com/product/0636920021872.do
• Privacy and Big Data, by Craig and Ludloff
• http://shop.oreilly.com/product/0636920020103.do
• Doing Data Science: Straight Talk from the
Frontline, by O’Neil and Schutt
• http://shop.oreilly.com/product/0636920028529.do
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Learning R
• Lots of places to learn more about R
– All of the sources on the first slide have R code available
– Comprehensive R Archive Network (CRAN)
– http://cran.r-project.org/manuals.html
– Springer Textbooks Use R! Series
– http://www.springer.com/series/6991
– Online search tool Rseek
– http://www.rseek.org/
– The RStudio site
– http://www.rstudio.com/
– The Odum Institute’s online course
– http://www.odum.unc.edu/odum/contentSubpage.jsp?nodeid=670
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What is Data Science?
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What is Data Science?
• What words come to mind when you think of
Data Science?
• What experience do you have with Data
Science?
• Why are you taking an Introduction to Data
Science Class?
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The Data Science Revolution
• Data science is exploding in importance and
the attention it receives.
• It’s hard to sort through the substance and the
hype.
• There is real value in data science, but you
should have a purpose or goal in mind first.
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The Roots of Data Science
• Simple observation and recording those
observations dates back to the most ancient
civilizations
– The Greeks were the first western civilization to adopt
observation and measurement
• Some call Aristotle the first empirical scientist
– Muslim scholars between the 10th and 14th centuries
developed experimentation (Haytham)
– Roger Bacon (1214-1284) promoted inductive
reasoning (inference)
– Descartes (1596-1650) shifted focus to deductive
reasoning.
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What is Data Science?
• “How Companies Learn Your Secrets” NYT, by
Charles Duhigg, February 16, 2012
• http://www.nytimes.com/2012/02/19/magazine/shopp
ing-habits.html?pagewanted=1&_r=2&hp&
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What did Target Do?
• Mining of data on shopping patterns
– Specific products purchased
– Combination of products purchased
– Combined with demographic and other data
• Psychology and neuroscience
– Habits:
• Cue-routine-reward
• When are habits open to change?
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Lessons from Target
• Yes, Data Science is about mining data
• There are deeper theoretical issues involved in
understanding what you find
• Left out of that long article are most of the
critical steps that precede the analysis
• In short, Data Science > data mining
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Definition of Data Science
• There are many, but most say data science is:
– Broad – broader than any one existing discipline
– Interdisciplinary: Computer Science, Statistics,
Information Science, databases, mathematics
• Also substantive domains (environmental science, sociology,
public health, etc.)
– Applied focus on extracting knowledge from data to
inform decision making.
– Focuses on the skills needed to collect, manage, store,
distribute, analyze, visualize, and reuse data.
• There are many visual representations of Data
Science
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Some definitions link computational, statistical, and substantive expertise
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Other definitions focus more on technical skills alone
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Still other definitions are so broad as to include nearly everything
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There are many “Word Cloud” representations of Data Science as well
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Definition of Data Science
• The field is immature, cluttered by hype,
unfocused.
• But, key features should include:
– Data across its lifecycle
– Interdisciplinary skills
– Substantive knowledge
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Defining Some Terms
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MapReduce and Hadoop
– Designed to process large operations quickly
– Distributes the problem across multiple servers
– The Map part filters and sorts data into bins or
queues based on some share characteristic
– The Reduce part then executes some operation on
each bin of data.
– Results are then reassembled
– It is like parallel processes, but distributed across
servers rather than just processors
– Scalable and has a fault tolerance
– Hadoop is an open-source version
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More on MapReduce
• Pig
– Software platform used for creating MapRedce
programs used by Hadoop.
• Hive
– A date warehouse infrastructure built on top of
Hadoop. Used to query, summarize, or analyzed
data.
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Database Management
• SQL – Structured Query Language
– A programming language designed for management of
relational databases
• MySQL – Open source implementation of an SQL-like
system for management of relational databases (used by
Wikipedia, Google, Facebook, Twitter, Flickr, YouTube)
• NoSQL – (Not Only SQL)
– Used for databases where the data is in some form other than
tabular relations like those used in relational databases
– Cassandra (Apache)
• Distributed database management with not single node of failure
• Scalable with no down time
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Cloud Computing
• Standard client-server model where computing
operations don’t happen on the local (desktop)
machine.
• What’s new? Virtualization. You are not connecting to
a specific server.
– Servers are virtual
– One server can run multiple virtual machines
– One virtual machine can use multiple servers
• This makes the “machine” scalable, moveable,
configurable.
• Allows selling software, platforms, and even computing
infrastructure as a “service”
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Data Mining/Machine Learning
• Machine learning uses computer algorithms to
get a machine to learn and adapt to new
information.
• Data Mining more explicitly focuses on
discovering patterns or structure in a given set
of data.
• Often used as synonyms by non-experts
without much loss of information.
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Web Scraping
• This is a process of collecting information from
websites and then organizing it for some sort
of analysis.
• Scraping is just about getting the data; the
analysis comes later.
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Programming Tools
– R – Statistical programming (object oriented, scripting
language)
– Python – a scripting programming language that
supports object-oriented programming, structured
programming, functional programming
– SQL – Relational Database
– SAS – General purpose data analysis software
– Julia – Faster than R and more scalable than Python
– Kafka and Storm – Used for real-time streaming
analysis
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