Python for Social Science Syllabus

Course Outline

Linguistics 572

The table below gives an approximate schedule of classes, assignments, and lectures for this course.

Day

Reading

Assignment

Lecture

Background

Tue Aug 25 Book Draft: Table of Contents, 1. Preface, 2.1-2.3 What is this course about? What is Python? Why Python 2.4 Install Python (Anaconda) or run in the cloud? Assignment: Running Python Notebook. Introductory remarks Jupyter notebook Demo (notebook). Python Data Science HB (PDSHB): Chapter 1. IPython PDSHB Notebook Index. (Notebooks contain the text of PDSHB + executable text snippets)
Tue Sep 01 Book Draft: 3.1-3.3 Python types I, More Python types II. Running python assignment due. Notebook demo (notebook), Python types notebook, Python types I: Strings, numbers. Python types II: Sequences, Dictionaries, sets. VanDerPlas: Whirlwind Tour of Python: Chapter 7: Notebooks.
Tue Sep 08 Book Draft: If-then statements, Boolean results, loops, List comprehension, Assignment: Python types assignment due. Loops, conditional clauses notebook. VanDerPlas: Whirlwind Tour of Python: Chapter 8, 9, 12: Notebooks.
Tue Sep 15 Book Draft: 5.1: importing, 5.2 Namespaces, 5.3 block structure, 5.4 Functions and function parameters, import, namespaces, classes.   Notebooks: Functions.  
Tue Sep 22 Book Draft: Book Draft: 4.6 Functions. 4.7. Functions Functions assignment due. Notebooks: Functions. Sets, Set operations, Set example, Iterators and generators, Climate change problem (containers), DNA string (containers, coding), DNA translation(Dictionary codebook). VanDerPlas: Whirlwind Tour of Python: Chapter 9: Notebooks.
Tue Sep 29 Book Draft: Numpy: 6.1 - 6.4   Notebooks: Intro to numpy: arrays, tables, splicing, arithmetic with arrays, arrays versus lists, Boolean arrays and Boolean indexing, fancy indexing. More nitty gritty on Boolean arrays (from PDSHB), Numpy tools A broader survey of numpy capabilities (From Handson Machine Learning). In class Boolean ntebook. Python Data Science HB (PDSHB): Chapter 2. Numpy. PDSHB Notebook Index.
Tue Oct 06        
Tue Oct 13 Book Draft: Intro to pandas and pandas data frames 6.4 - 6.8., Pandas tutorial.
Numpy assignment due, Midterm Study notebook, Midterm Study answers. Lecture: Mid Semester Review. Tools: Pandas Intro (HOML). Pandas notebook I, Pandas notebook II Python Data Science HB (PDSHB): Chapter 3. Pandas. PDSHB Notebook Index.
Tue Oct 20   Midterm. (Midterm more study problems, Midterm more study problems.) Notebooks: Pivot tables and merges in Pandas, Census data example.  
Tue Oct 27 Book Draft: Introducing Regular Expressions, Reading in and tokenizing text data. Final project suggestions, Pandas assignment due . Regular expressions notebook. VanDerPlas: Whirlwind Tour of Python: Chapter 15: Notebooks. NLTK Book ch. 3
Tue Nov 03 Book Draft: Chap. 7: Classification of text. Regression, Chap. 7: Linear classifiers, SVM classification, Applying linear classifiers to text: Movie review example. Pandas quiz. Pandas quiz. Pandas assignment solution. Classifying insults. Support Vector Machines, Classifying movie reviews with Naive Bayes, Regression, Regression and classification. Python Data Science HB (PDSHB): Chapter 5. Machine learning. PDSHB Notebook Index.
Tue Nov 10 Book Draft: Chap. 9: Social networks intro, Gephi demo. Project suggestions revisited. Social Networks lectures slides, and Using networkx notebook, Networkx graphs with mousable nodes (using javascript), community detection, Community detection with a weighted graph.  
Tue Nov 17 Book Draft: Chap. 7: Regression. Regression,   Matplotlib Intro, 03_Classification (HOML). Regression, Regression and classification. NLTK Book ch. 3
Tue Nov 24 Book Draft: Chap. 8: Visualization. Either Regular expressions assignment or Anna Karenina Assignment or Classifying insults, or Regression and classification, or Census data example (Section 1.9 has an assignment). Color and color maps I: Parallel coordinates plots. Color and color maps II: Correlation heat maps.. Visualizing higher dimensional data with color, Boxplot notebook, Geographic visualization. Kernel density estimates. Projections.  
Tue Dec 01   Assignment solutions: Regular expressions, Anna Karenina, Regression and Classification, Census data. Dimensionality reduction with LSI slides, Dimensionality reduction with LSI notes, Python code for LSI example,