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Introduction to Data Science for Biology
Jarrett Byrnes UMass Boston Spring 2016
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We Are Awash in Data
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We Are Awash in Data
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Data Takes Many Forms
• Athletic performance • Timeseries of polls • Sequence Data • Measurements of physical properties • Maps (often with many layers) with
information • Timings of events • Images • Network descriptions • Plain text
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Data is at the Center of Biology
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Classical Tools Not Up to the Task
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Classical Tools Not Up to the Task
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Introduction to Data Science for Biology
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Who Are You?
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Our Semester
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Learn how to create efficient understandable datasets for
biological research YEAR MONTH DAY DATE SITE TRANSECT SP_CODE 0-20 IN 20-40 IN 40-20 OFF 20-0 OFF OBSERVER
2013 7 24 7/24/2013 BAKER 1 HOAM 1 1 2 1 LYMAN
2013 7 24 7/24/2013 BAKER 1 CAIR 1 1 1 0 LYMAN
2013 7 24 7/24/2013 BAKER 1 CABO 0 1 1 1 LYMAN
2013 7 24 7/24/2013 BAKER 1 ASFO 0 6 7 0 LYMAN
2013 7 24 7/24/2013 BAKER 2 HOAM 1 3 2 2 LYMAN
2013 7 24 7/24/2013 BAKER 2 CAIR 2 8 11 2 LYMAN
2013 7 24 7/24/2013 BAKER 2 CABO 0 1 2 0 LYMAN
2013 7 24 7/24/2013 BAKER 2 CAMA 1 9 5 2 LYMAN
2013 7 24 7/24/2013 BAKER 2 ASFO 0 4 6 0 LYMAN
2013 7 24 7/24/2013 BAKER 2 ASRU 0 0 1 0 LYMAN
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Learn common programming language(s) associated with data
science
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Build a vocabulary of visualization tools that enable students to see
what their data means
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This is How I Know I Failed You
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Develop an understanding of how to manipulate data for the
purposes of seeing useful patterns
h"p://swcarpentry.github.io/r-novice-gapminder/
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Understand how to unify data from disparate sources to build a larger picture of biological phenomena
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Understand how to unify data from disparate sources to build a larger picture of biological phenomena
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Learn basic analytical tools for deriving statistical inference from
data
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This Class
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Course Web Page
http://byrneslab.net/teaching/data_science/
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“Text”book & Weekly Readings
http://r4ds.had.co.nz/
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Lab
• Coding!
• TA: Jillian Dunic
• Guided examples and then challenge problems
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Assignments
• Weekly problem sets
– Variable in scope! – May involve elements of your final project
• Can be started in lab – Will highlight concepts from that week
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Final Project
• Analysis of a data set of your choosing – From your own work – Found data
• Data mashups encouraged! – Bring together multiple public sources of data
• Proposals due in two weeks – What data will you be using? – What question do you want to answer?
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Next Time: Data Collection, Entry, and How to Make Your
Data Usable
(and have future you avoid wanting to kill now you)
(And listed to the Not So Standard Deviations Podcast)