DS4UX (Spring 2016)/Schedule: Difference between revisions

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* [[DS4UX_(Spring_2016)/Day_1_exercise|Installation and setup]] — You'll install software including the Python programming language and run through a series of exercises.
* [[DS4UX_(Spring_2016)/Day_1_exercise|Installation and setup]] — You'll install software including the Python programming language and run through a series of exercises.
* [[DS4UX_(Spring_2016)/Day_1_lecture#Part 2: Basic Python concepts|Interactive lecture]]: ''programming concepts 1''
* [[DS4UX_(Spring_2016)/Day_1_lecture#Part 2: Basic Python concepts|Interactive lecture]]: ''programming concepts 1''
* [[DS4UX (Spring 2016)/Day 1 tutorial|Self-guided tutorial and exercises]] — You'll work through a self-guided tutorial to practice the basic concepts we introduced in the lecture.


;Homework
;Homework
* [[DS4UX (Spring 2016)/Day 1 tutorial|Self-guided tutorial and exercises]] — You'll work through a self-guided tutorial to practice the basic concepts we introduced in the lecture.
* Complete [[DS4UX (Spring 2016)/Day 1 tutorial|Self-guided tutorial and exercises]] (if you didn't finish this in class).
* Complete [[DS4UX_(Spring_2016)/Day_1_exercise#Goal_7:_Practice_Python_using_Codecademy|CodeAcademy lessons]]
 


;Resources
;Resources

Revision as of 02:48, 26 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.
  • Lecture: Why Programming and Data Science — 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

Class schedule
  • flow control
  • modules
  • user-defined functions
Exercises
  • Reading data from a flat file (interactively & from a script)
Homework
Resources


Week 3: April 11

Day 3 plan

Class schedule
  • Day 3 lecture - working with web data 1 (APIs)
  • programming concepts 3
  • APIs
  • JSON


Exercises
  • Practice in API sandboxes
  • Requesting data from an API using Python
Homework
Resources
  • go here


Week 4: April 18

Day 4 plan

Class schedule
  • Day 4 lecture - working with web data 2 (SQL)
  • introduction to the Wikipedia database
  • programming concepts 4
  • 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
Resources
  • go here


Week 5: April 25

Day 5 plan

Assignments due
Class schedule
  • Day 5 lecture - visualizing data
  • Introduction to Jupyter notebooks
  • Jupyter notebooks 1
  • importing data with SQL and API queries
  • data manipulation with Jupyter
Exercises
  • visualize Seattle building permit data
Homework
Resources
  • go here


Week 6: May 2

Day 6 plan

Class schedule
  • basic regular expressions
  • graphing data with MatPlotLib
Exercises
  • counting mentions and welcomes in the Teahouse corpus
  • plotting trends over time in the Teahouse corpus
Homework
Resources
  • go here


Week 7: May 9

Day 7 plan

Assignments due
Class schedule
  • Day 7 lecture - describing data with statistics
  • Jupyter notebooks 3
  • running statistics with SciPy
Exercises
  • plotting Burke-Gilman bike traffic on rainy days
Coding challenges
Resources
  • go here


Week 8: May 16

Day 8 plan

Class schedule
Exercises
  • Replicate Teahouse invite A/B test
Coding challenges
Resources
  • go here


Week 9: May 23

Day 9 plan

Class schedule
  • Day 9 lecture - communicating your findings
  • review of key concepts and tools
  • presentation practice
Homework
  • goes here
Resources
  • go here


Week 10: May 30

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


Week 11: June 6

Assignments due
Class schedule
  • Finals week - No class!