PDS I (IDS 590)

Note: may appear as “Data Science 4 Social Science” on DukeHub, sorry — new class, some administrative changes pending.

Practical Data Science I is a flipped-classroom, exercise and project-focused course. It requires zero prior experience with programming and begins with an introduction to Python, computational thinking, and the principles of good programming using the 7 Steps method. The class focus then shifts to data analysis with an emphasis on the type of analyses of interest to social scientists and public policy students. The course provides students with experience manipulating and analyzing real (often messy, error-ridden, and poorly documented) data using the full range of Python data science tools (like the command line, git, VS Code, numpy, pandas, matplotlib, statsmodels, and more).

Syllabus

A preliminary syllabus can be found here.

Class Schedule

Class will be Tuesdays and Thursdays from 10:05am-11:25am (EDT).

Key:

• PPF is Python Programming Fundamentals, and the number that follows is the module number.

• DSwithNSD is Data Science with NumPy, Sets, and Dictionaries, and the number that follows is the module number.

Date, Rm	Topic	Do Before Class	In-Class Exercise
Tues, Aug 27	Class Introduction Welcome to VS Code	Read and sign syllabus, submit on gradescope Register with IPUMS Register for DataCamp PPF1, Seven Steps for Algorithm Design
Thurs, Aug 29	Setting Up An Algorithm 7 Steps	PPF1, Working an Example of the Seven Steps Process PPF1, Creating an Algorithm	Legos
Tues, Sep 3	Ideas Into Code	chatGPT and You PPF1, Data Types PPF2, Important Python Syntax PPF2, Translating an Algorithm Into Python f-strings	Using VS Code Max Triangle
Thurs, Sep 5	Testing Your Code	Debugging (principles) Debugging (tools) What are Tracebacks?	Debugging
Tues, Sep 10	Lists In Depth	PPF3, Approaches to Testing PPF4, Looking More into Lists.	List Max HR Peak
Thurs, Sep 12	Buffer Day	Buffer day to allow extra time for any topics needing additional attention
Tues, Sep 17	Object Oriented Programming	DSwithNSD1, Basics of Object Oriented Programming	Point Circle
Thurs, Sep 19	Object Oriented Programming	DSwithNSD1, Sets and Big-O Notation (and Dicts) SETUP 1: Installing Python and miniconda SETUP 2: Installing and Configuring VS Code
Tues, Sep 24	Command Line Basics Advanced Command Line Packages Jupyter	SETUP 3: Setup Augmented Command Line Command Line Basics Advanced Command Line Python packages SETUP 4: Jupyter in VS Code OPTIONAL SETUP: R Jupyter Notebooks	Ex 1 Ex 2 Ex 3
Thurs, Sep 26	Numpy Basics	Under the numpy heading, read everything under Vectors EXCEPT Speed/Ease of Use Tradeoff and Vectorization Under the numpy heading, also read everything under Subsetting Vectors Python v. R	Ex 1
Tues, Oct 1	Numpy Basics	Views and Copies When Do I Get a View Variables, Objects, and Numpy Views and Copies Recap Additional Concepts Numbers in Computers Writing Good Notebooks	Ex 1 (Finish other numpy)
Thurs, Oct 3	Numpy Arrays	Matrices — From Welcome through Review of Matrices (see navigation bar on left) Arrays — From Welcome through Color Images as Arrays (see navigation bar on left)
Tues, Oct 8	Pandas: Series	Speed / Ease of Use Tradeoff Vectorization Under Pandas, Read from From Working with Tabular Data through Subsetting and Indexing with Single Square Brackets	Link
Thurs, Oct 10	Pandas: DataFrames	Object Datatypes Pandas DataFrame readings from Working with tabular data through Dataframes through Subsetting Tricks and Gotchas Categorical Datatype PyArrow Backend	Link
Tues, Oct 15 (Break)	FALL BREAK
Thurs, Oct 17 (Break)	Pandas: Indices & Missing	Indices Views and Copies in Pandas through the View/Copy Headache without CoW Reading and Writing Data: Read from Ways To Store and Read Data through ways to Store and Read Binary Data Missing Data	Link 1 Link 2
Tues, Oct 22	Pandas: Cleaning Tracebacks	Data Cleaning from Identifying Problems through Cleaning Data Types Using AI Effectively Link forthcoming	Link
Thurs, Oct 24	Plotting	Plotting Principles Matplotlib Basics Matplotlib Vocabulary Explicit v Implict Saving Plots	Link
Tues, Oct 29	Plotting	Plotting in Python through seaborn and Grammer of Graphics seaborn recipes Customizing matplotlib Making Pretty Plots 1 Making Pretty Plots 2 Pandas Plotting	Link
Thurs, Oct 31	Statistics with statsmodels	Read all readings under the Statistical Modelling heading, starting with Inference and Prediction through Beyond the Basic Model	Link
Tues, Nov 5	Defensive Programming Groupby / Split-Apply-Combine	Defensive Programming: from first reading through Iceberg Principle Groupby	Link
Thurs, Nov 7	Pandas: Reshaping Pandas: Categoricals	Wes McKinney’s Python for Data Analysis, Third Edition, Chapter 8, Section 3. Available online through duke library free Tidy Data Format: the goal of reshaping Pandas reshaping (with pics!)	Link
Tues, Nov 12	Merging	Combining, Grouping and Querying Concatenating Merging/Joins Merging Part 2 Merging Example Merge Validation	Link
Thurs, Nov 14	Defensive Programming Workflow Backwards Design Getting Help Online Git and Github	Workflow Management Backwards Design Getting Help	Start git?
Tues, Nov 19	Git	Git and Github Create an ssh key and register with ssh-agent (you don’t need to create a passphrase) If you don’t have a github account, make one. Register a passkey or at least two factor authentication. Add your ssh key to your github account Visualize Git Branches	Ex 1
Thurs, Nov 21	Git	Git Day 2	Ex 2
Wed Dec 12	LAST CLASS	ROUGH DRAFT OF OPIOID PROJECT DUE, extendable to Morning of Mon Nov 26 but feedback may be later