PDS I (IDS 540)

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).

Office Hours

Adriane: Signup with this link

Please consult with the TAs on homework and reading questions first before signing up for my office hours. If you have conceptual questions or project questions please do meet with me.

Shambhavi (TA): Wednesday, 12-2pm in Gross Hall 230L

Bruce (TA): Tuesday, 2-4pm in Gross Hall 230N

Syllabus

The syllabus can be found here.

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Class will be Mondays and Wednesdays from 8:30-9:45am (EDT) in Gross Hall 230C.

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
Mon, Aug 24	Class Introduction Welcome to VS Code	SETUP 1: Installing Python and miniconda SETUP 2: Installing and Configuring VS Code Read and sign syllabus, submit on gradescope Register with IPUMS Register for DataCamp (duke.edu address required) Join Coursera IDS 540 Session of Python Programming Fundamentals
Wed, Aug 26	Setting Up An Algorithm 7 Steps	PPF1, Seven Steps for Algorithm Design PPF1, Working an Example of the Seven Steps Process PPF1, SKIP Creating an Algorithm	Legos
Mon, Aug 31	Legos Day 2	More time with legos
Wed, Sep 2	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
Mon, Sep 7	Testing Your Code	What are Tracebacks? Loops and Lists	Func Diagrams
Wed, Sep 9	List Practice	Debugging (principles) Debugging (tools)	Debugging
Mon, Sep 14	Buffer Day	PPF3, Validating Your Code. Do do Coursera exercises in this section!	Blackjack
Wed, Sep 16	Buffer Day	No new readings
Mon, Sep 21	List Practice	PPF4, Diving Deeper with Lists (all sub-modules)	List Max
Wed, Sep 23	Object Oriented Programming	Join class session for next Coursera course (DSwithNSD) with Duke email DSwithNSD1, Basics of Object Oriented Programming
Mon, Sep 28	Object Oriented Programming	More OOP
Wed, Sep 30	Object Oriented Programming	DSwithNSD1, Sets and Big-O Notation (and Dicts)
Mon, Oct 5	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
Wed, Oct 7	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
Mon, Oct 12 (Break)	FALL BREAK
Wed, Oct 14	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
Mon, Oct 19	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)
Wed, Oct 21	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
Mon, Oct 26	Pandas: DataFrames	Object Datatypes Pandas DataFrame readings from Working with tabular data through Dataframes through Subsetting Tricks and Gotchas Categorical Datatype PyArrow Backend
Wed, Oct 28	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
Mon, Nov 2	Pandas: Cleaning Regex	Data Cleaning from Identifying Problems through Cleaning Data Types Datacamp Class “Regular Expressions for Pattern Matching,” Chapter 3 (Introduction to regular expressions)
Wed, Nov 4	Defensive Programming Groupby / Split-Apply-Combine	Defensive Programming: from first reading through Iceberg Principle Groupby
Mon, Nov 9	Groupby Continued	Groupby Continued
Wed, Nov 11	Merging	Combining, Grouping and Querying Concatenating Merging/Joins Merging Part 2 Merging Example Merge Validation
Mon, Nov 16	Plotting	Plotting Principles Matplotlib Basics Matplotlib Vocabulary Explicit v Implict Saving Plots Using LLMs Well
Wed, Nov 18	More plotting	Plotting in Python through seaborn and Grammer of Graphics seaborn recipes From seaborn to matplotlib Customizing matplotlib Making Pretty Plots 1 Making Pretty Plots 2 Pandas Plotting
Wed Dec 9	Statistics with statsmodels	Read all readings under the Statistical Modelling heading, starting with Inference and Prediction through Beyond the Basic Model
	Final project due date