[Data, the Humanist's New Best Friend](index.ipynb)
Calendar

Instructor: Javier de la Rosa
E-mail: jdelaro@uwo.ca
Office: UC 114
Office Hours: Mondays 3:00pm-5:00pm
Meets: Fall 2014, Mondays 12:30pm-2:30pm, Wednesdays 12:30pm-2:30pm
Room: AB 148

Block 0: What is This Course About?

September $4^{th}$: Class 01.
- Presentation
- Initial setup

Block 1: How to Think Like a Computer Scientist (Review)

September $9^{th}$: Class 02.
- Topics:
  - Python syntax
  - Variables and values
  - Statements and expressions
- Readings:
  - Chapter 1 of How to Think Like a Computer Scientist
  - Sections 2.1-2.9 of chapter 2 of How to Think Like a Computer Scientist
September $11^{th}$: Class 03.
- Topics:
  - Boolean logic
  - Define functions
  - Debug problems
  - Get data from the keyboard
- Readings:
September $16^{th}$: Class 04.
- Topics:
  - Import and reuse code
  - Format, comment and document your code
  - Control flow execution
  - Complex data types like lists and dictionaries
- Readings:

Block 2: Dealing with Data

September $18^{th}$: Class 05.
- Topics:
  - Getting data into Python
  - Objects
  - NumPy
  - Arrays and Matrices
  - SciPy
- Readings:
  - Section 1.3. NumPy: creating and manipulating numerical data, from Scipy lecture notes.
  - Section 1.5.6. Statistics and random numbers: scipy.stats, from Scipy lecture notes.
September $23^{th}$: Class 06.
- Topics:
  - Pandas
  - Cleaning data
  - Summary statistics
  - Indexing
  - Merging, joining
  - Group-by
- Readings:
  - Read 10 Minutes to Pandas, from pydata.
  - Read Series, from pydata.
  - Read DataFrame, from pydata.
  - Read Chapters 1-5, from Julia Evans pandas-cookbok.
September $25^{th}$: Class 07.
- Topics:
  - Split-Apply-Combine
  - Cross-tabulation and pivoting
  - String manipulation
  - matplotlib
  - Visualizations
  - Histograms
- Readings:
  - Read The Split-Apply-Combine Strategy for Data Analysis
  - Read Section String Manipulation, 205-212, from Python for Data Analysis.
  - Read Section Pivot Tables and Cross-Tabulation, 275-278, from Python for Data Analysis.
  - Read Section 1.4. Matplotlib: plotting, from Scipy lecture notes
September $30^{th}$: Class 08.
- Topics:
  - Statistical modeling
  - statmodels
  - Correlation
  - Regression
  - Distributions
- Rreadings:
  - Read Chapters 7-9 and 13-14, from Statistics for Humanities.
  - Read Ordinary Least Squares in Python, from datarobot.
October $2^{nd}$: Class 09.
- Topics:
  - Machine Learning
  - scikit-learn
  - Supervised learning: kNN
  - Unsupervised learning: K-means
  - Cross-validation
- Readings:
October $7^{th}$: Class 10.
- Work on Assignment 1.

Block 3: Text Analysis

October $9^{th}$: Class 11.
- Topics:
  - NLTK
  - Tokenization
  - Concordance
  - Co-Occurrence and similarity
  - Word and phrase frequencies
  - Dispersion plots
  - TextBlob
- Readings:
  - Read Chapter 1, from Natural Language Processing with Python.
- Deadlines:
  - Assignment 1. Note: Re-submissions are allowed until this day.
October $14^{th}$: Class 12.
- Topics:
  - Corpora
  - Conditional Frequency Distributions
  - Sources of data
  - Language detection
  - Machine translation
- Readings:
  - Read Chapters 2-3, from Natural Language Processing with Python.
October $16^{th}$: Class 13.
- Topics:
  - Regular expressions
  - Word inflection and lemmatization
  - Parsing
  - n-grams
  - Part-of-speech Tagging
- Readings:
  - Read Chapter 5, from Natural Language Processing with Python.
October $21^{st}$: Class 15.
- Topics
  - Information extraction
  - Trees
  - Named Entity Recognition
  - Mining HTML, Twitter and Facebook
- Readings:
  - Read Chapter 7, from Natural Language Processing with Python.
  - Read Chapters 1 and 5, from Mining the Social Web.
October $23^{rd}$: Class 16.
- Topics:
  - Sentiment Analysis
  - Classifiers
  - Generative Writing
- Readings:
  - Read Chapters 6-7, from Natural Language Processing with Python.
October $28^{th}$: Class 17.
- Work on Assignment 2.
October $30^{th}$: No class. Fall Study Break.

Block 4: Network Science

November $4^{th}$: Class 18.
- Topics:
  - Graph Theory
  - Vertices and relationships
  - Networks
  - Network Analysis
  - NetworkX
- Readings:
  - Read Sections 2.1-2.3, from Networks, Crowds, and Markets: Reasoning About a Highly Connected World.
  - Read Chapter 3 and 4, from Algorithms.
- Deadlines:
  - Assignment 2. Note: Re-submissions are allowed until this day.
November $6^{th}$: Class 19.
- Topics:
  - Centrality
  - Degree
  - Betweenness
  - Closeness
  - Eigenvector
  - Current flow betweenness
  - Ego networks
- Readings:
  - Read Chapter 3, from Networks, Crowds, and Markets: Reasoning About a Highly Connected World.
November $11^{th}$: Class 20.
- Topics:
  - Random vs Scale Free
  - Small Worlds
  - Network Dynamics
  - Social Network Analysis
  - Modularity and Community Structure
- Readings:
  - Read Python for Social Science.
  - Read Chapter 4, from Social Networks for Startups.
November $13^{th}$: Class 21.
- Topics:
  - Multi-partite networks
  - Meta-netoworks
  - Modeling
  - The Property Graph
- Readings:
  - Read Sections 3.1-3.2 and Chapter 7, from Graph Databases.
November $18^{th}$: Class 22.
- Topics:
  - Network Content Analysis
  - Plot Analysis
- Readings:
  - On Social Networks in Plays and Novels, from The International Journal of Sciences.
  - Network Theory, Plot Analysis.
- Deadlines:
  - Final Project proposal.
November $20^{th}$: Class 23.
- Work on Assignment 3.

Block 5: Final Projects

November $25^{th}$: Class 24.
- Work on Final Projects.
- Deadlines:
  - Assignment 3. Note: Re-submissions are allowed until this day.
November $27^{th}$: Class 25.
- Final Project presentations
December $2^{nd}$: Class 26. Last day fo class.
- Final Project presentations

Classes End

December $3^{rd}$:
- All asignments and final project must be handed in by this date.
- It is not allow to re-submit graded assignments.
- Deadlines:
  - Assignment 1.
  - Assignment 2.
  - Assignment 3.
  - Final Project.

[Data, the Humanist's New Best Friend](index.ipynb)*Calendar*