DS4UX (Spring 2016)/Schedule: Difference between revisions

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;Agenda
;Agenda
*[[DS4UX_(Spring_2016)/Day_4_lecture|Day 4 lecture]] - working with web data 2 (SQL)
* Review: We'll walk through answers to the assignment and code challenges from last week as a group.
*introduction to the Wikipedia database
*[[DS4UX_(Spring_2016)/Day_4_lecture|Day 4 lecture]] - background of web APIs; requesting web pages with <code>requests</code>, JSON, and writing to files.
*programming concepts 4
* We'll begin working on [[Community Data Science Course (Spring 2015)/Wikipedia API projects|a series of projects using the Wikipedia API]].
:*SQL queries
:*advanced API queries
* final project discussion 1
:* data sources
:* research questions
:* outline of project idea and project plan deliverables
 
;Exercises
*MYSQL queries with Quarry
*SOQL queries with Hurl.it and Python


;Homework
;Homework
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;Agenda
;Agenda
*[[DS4UX_(Spring_2016)/Day_5_lecture|Day 5 lecture]] - visualizing data
*[[DS4UX_(Spring_2016)/Day_5_lecture|Day 5 lecture]] - visualizing web data
*Introduction to Jupyter notebooks
*Introduction to Jupyter notebooks
*Jupyter notebooks 1
* graphing data with matplotlib
:* importing data with SQL and API queries
<!--
:* data manipulation with Jupyter
 
;Exercises
;Exercises
* visualize Seattle building permit data
* visualize Seattle building permit data
-->


;Homework
;Homework
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;Agenda
;Agenda
* [[DS4UX_(Spring_2016)/Day_6_lecture|Day 6 lecture]] - corpus analysis
* [[DS4UX_(Spring_2016)/Day_6_lecture|Day 6 lecture]] - querying databases
 
<!--
<!-- * Jupyter notebooks 2
*MYSQL queries with Quarry
:* basic regular expressions
*SOQL queries with Hurl.it and Python
:* graphing data with MatPlotLib
-->


;Exercises
* counting mentions and welcomes in the Teahouse corpus
* plotting trends over time in the Teahouse corpus
-->
;Homework
;Homework
*[[DS4UX_(Spring_2016)/Day_6_coding_challenge|Day 6 coding challenges]]
*[[DS4UX_(Spring_2016)/Day_6_coding_challenge|Day 6 coding challenges]]
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;Resources
;Resources
*''go here''
*''go here''


=== Week 7: May 9 ===
=== Week 7: May 9 ===
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;Agenda
;Agenda
* [[DS4UX_(Spring_2016)/Day_7_lecture|Day 7 lecture]] - statistics with scipy
* [[DS4UX_(Spring_2016)/Day_7_lecture|Day 7 lecture]] - corpus analysis
<!--* Jupyter notebooks 3
<!--
:* running statistics with SciPy
* counting mentions and welcomes in the Teahouse corpus
* plotting trends over time in the Teahouse corpus
-->


;Exercises
* plotting Burke-Gilman bike traffic on rainy days
-->
;Coding challenges
;Coding challenges
*[[DS4UX_(Spring_2016)/Day_7_coding_challenge|Day 7 coding challenges]]
*[[DS4UX_(Spring_2016)/Day_7_coding_challenge|Day 7 coding challenges]]
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;Resources
;Resources
*''go here''
*''go here''


=== Week 8: May 16 ===
=== Week 8: May 16 ===
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;Agenda
;Agenda
* [[DS4UX_(Spring_2016)/Day_8_lecture|Day 8 lecture]] - research study design
* [[DS4UX_(Spring_2016)/Day_8_lecture|Day 8 lecture]] - statistics with scipy
<!--
* plotting Burke-Gilman bike traffic on rainy days
-->


<!--;Exercises
* Replicate Teahouse invite A/B test
-->
;Coding challenges
;Coding challenges
*[[DS4UX_(Spring_2016)/Day_8_coding_challenge|Day 8 coding challenges]]
*[[DS4UX_(Spring_2016)/Day_8_coding_challenge|Day 8 coding challenges]]
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;Resources
;Resources
*''go here''
*''go here''


=== Week 9: May 23 ===
=== Week 9: May 23 ===
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;Agenda
;Agenda
* [[DS4UX_(Spring_2016)/Day_9_lecture|Day 9 lecture]] - funnel analysis
* [[DS4UX_(Spring_2016)/Day_9_lecture|Day 9 lecture]] - research study design
* review of key concepts and tools from this course
* review of key concepts and tools from this course
* presentation workshop
* presentation workshop
<!--
* Replicate Teahouse invite A/B test
-->


;Resources
;Resources
* ''go here''
* ''go here''


=== Week 10: May 30 ===
=== Week 10: May 30 ===

Revision as of 01:18, 27 March 2016

Week 1: March 28

Day 1 plan

Assignments due
  • fill out the pre-course survey
Agenda
  • Quick introductions — Be ready to introduce yourself and describe your interest and goals in the class.
  • Why Programming and Data Science for UX Research? — What this course is about
  • Class overview and expectations — We'll walk through this syllabus.
  • Group formation — We'll assemble in our peer programming groups for the first time.
  • Installation and setup — You'll install software including the Python programming language and run through a series of exercises.
  • Interactive lecture: programming concepts 1
  • Self-guided tutorial and exercises — You'll work through a self-guided tutorial to practice the basic concepts we introduced in the lecture.
Homework
Resources


Week 2: April 4

Day 2 plan

Agenda
Homework
Resources


Week 3: April 11

Day 3 plan

Class schedule
  • Interactive lecture: creating your own functions
  • Day 3 lecture - working with web data 1 (APIs)
  • Peer programming: Practice with API sandboxes
  • Interactive lecture: requesting data from an API using Python
Homework
Resources
  • go here


Week 4: April 18

Day 4 plan

Agenda
Homework
Resources


Week 5: April 25

Day 5 plan

Assignments due
Agenda
  • Day 5 lecture - visualizing web data
  • Introduction to Jupyter notebooks
  • graphing data with matplotlib
Homework
Resources
  • go here


Week 6: May 2

Day 6 plan

Agenda
Homework
Resources
  • go here


Week 7: May 9

Day 7 plan

Assignments due
Agenda
Coding challenges
Resources
  • go here


Week 8: May 16

Day 8 plan

Agenda
Coding challenges
Resources
  • go here


Week 9: May 23

Day 9 plan

Agenda
  • Day 9 lecture - research study design
  • review of key concepts and tools from this course
  • presentation workshop
Resources
  • go here


Week 10: May 30

Assignments due
Agenda
  • Day 10 lecture - Final project report review, next steps for Data Science
  • Final project presentations


Week 11: June 6

FINALS WEEK - NO CLASS

Assignments due