




Wed Aug 26 
Book Draft: Table of Contents, 1. Preface, 2.12.3 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) 
Wed Sep 02 
Book Draft: 3.13.3 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. 
Wed Sep 09 
Book Draft: Ifthen 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. 
Wed Sep 16 
Book Draft: 5.1: importing, 5.2 Namespaces, 5.3 block structure, 5.4 Functions and function parameters, import, namespaces, classes. 

Notebooks: Functions. 

Wed Sep 23 
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. 
Wed Sep 30 
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. 
Wed Oct 07 




Wed Oct 14 
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. 
Wed Oct 21 

Midterm. (Midterm more study problems, Midterm more study problems.) 
Notebooks: Pivot tables and merges in Pandas, Census data example. 

Wed Oct 28 
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 
Wed Nov 04 
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. 
Wed Nov 11 
H'day 
H'day 
H'day 
H'day 
Wed Nov 18 
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. 

Wed Nov 25 
H'day 
H'day 
H'day 
H'day 
Wed Dec 02 
Book Draft: Chap. 7: Regression. Regression, 

Matplotlib Intro, 03_Classification (HOML). Regression, Regression and classification. 
NLTK Book ch. 3 
Wed Dec 09 
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. 

Wed Dec 16 

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, 
