PDS I (IDS 590)

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 1:25pm-2:40pm (EDT).

Date, Rm	Topic	Do Before Class	In-Class Exercise
Tues, Aug 27	Class Introduction Welcome to VS Code	SETUP 1: Installing Python and miniconda SETUP 2: Installing and Configuring VS Code chatGPT and You Read and sign syllabus, submit on gradescope Register with IPUMS Register for DataCamp
Thurs, Aug 29	Setting Up An Algorithm 7 Steps	Python Programming Fundamentals, Module 1
Tues, Sep 3	Ideas Into Code	Python Programming Fundamentals, Module 2
Thurs, Sep 5	Testing Your Code	Python Programming Fundamentals, Module 3
Tues, Sep 10	Lists In Depth	Python Programming Fundamentals, Module 4
Thurs, Sep 12	Sets and Dictionaries	Data Science with NumPy, Sets, and Dictionaries, Module 1
Tues, Sep 17	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 19	Python Debugger R / Python Differences Packages	What are Tracebacks? Debugging Tools Python Debugger in VS Code variables v objects Ergonomics Python v. R	Ex 1 Ex 2
Tues, Sep 24	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, 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 Numbers in Computers Writing Good Notebooks	Ex 1 Ex 2
Tues, Oct 1	Numpy Arrays	More Numpy Concepts: Views and Copies When Do I Get a View Variables, Objects, and Numpy Views and Copies Recap Matrices: Matrices — From Welcome through Review of Matrices (see navigation bar on left) ND Arrays: Arrays — From Welcome through Color Images as Arrays (see navigation bar on left)	Ex 1 (Finish other numpy)
Thurs, Oct 3	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
Tues, Oct 8	Pandas: DataFrames	Object Datatypes Pandas DataFrame readings from Working with tabular data through Dataframes through Subsetting Tricks and Gotchas Categorical Datatype PyArrow Backend	Link
Thurs, Oct 10	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 15	FALL BREAK
Thurs, Oct 17	FALL BREAK (Not technically, but take the week)
Tues, Oct 22	Pandas: Cleaning Tracebacks	Data Cleaning from Identifying Problems through Cleaning Data Types f-strings Python Strings (string section only!) 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	Merging	Combining, Grouping and Querying Concatenating Merging/Joins Merging Part 2 Merging Example Merge Validation	Link
Tues, Nov 5	Statistics with statsmodels	Understanding Performance Improving Performance Code Reviews Read all readings under the Statistical Modelling heading, starting with Inference and Prediction through Beyond the Basic Model	Link
Thurs, Nov 7	Defensive Programming Workflow Backwards Design Getting Help Online Git and Github	Workflow Management Backwards Design Getting Help	Discuss mid-semester project in class - Opioid Project
Tues, Nov 12	Defensive Programming Groupby / Split-Apply-Combine	Defensive Programming: from first reading through Iceberg Principle Groupby	Link
Thurs, Nov 14	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 19	Machine Learning with sckikit-learn	JVP Chapter 5 up to but not including Hyperparameters and Model Validation Section	Link
Thurs, Nov 21	LAST CLASS	ROUGH DRAFT OF OPIOID PROJECT DUE, extendable to Morning of Mon Nov 26 but feedback may be later
Wed Dec 12		Final Project Report Due