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).

Office Hours

Adriane: Signup with this link

Nick (Zoom or in Gross 231): Thursday 8:45 am - 9:45 am. Please email me in advance if you plan to attend. I’ll usually be there regardless, but knowing people are coming is useful if conflicts arise.

Diego (Zoom or Gross Hall 2nd Floor, 230K): Wednesday 2:00 - 3:00 pm

Meron (Zoom or Gross Hall 2nd Floor, 230N): Tuesday 12:15 - 1:15pm.

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 26	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 Join Slack Group Register for DataCamp (duke.edu address required) Join Coursera IDS 590 Session of Python Programming Fundamentals
Thurs, Aug 28	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
Tues, 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
Thurs, Sep 4	Testing Your Code	What are Tracebacks? Loops and Lists	Func Diagrams
Tues, Sep 9	Lists In Depth	Debugging (principles) Debugging (tools)	Debugging
Thurs, Sep 11	Buffer Day	PPF3, Approaches to Testing. Do do Coursera exercises in this section!	Blackjack
Tues, Sep 16	List Practice	PPF4, Diving Deeper with Lists (both Looking More into Lists and Heart Rate: An Example)	List Max
Thurs, Sep 18	More Lists		HR Peak
Tues, Sep 23	Object Oriented Programming	Join class session for next Coursera course with Duke email DSwithNSD1, Basics of Object Oriented Programming	Point Circle
Thurs, Sep 25	Object Oriented Programming	More OOP
Tues, Sep 30	Object Oriented Programming	DSwithNSD1, Sets and Big-O Notation (and Dicts)	Count Words
Thurs, Oct 2	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
Tues, 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
Thurs, Oct 9	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
Tues, Oct 14 (Break)	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)
Thurs, Oct 16 (Break)	FALL BREAK
Tues, 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
Thurs, Oct 23	Pandas: DataFrames	Object Datatypes Pandas DataFrame readings from Working with tabular data through Dataframes through Subsetting Tricks and Gotchas Categorical Datatype PyArrow Backend
Tues, 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
Thurs, Oct 30	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)
Tues, Nov 4	Defensive Programming Groupby / Split-Apply-Combine	Defensive Programming: from first reading through Iceberg Principle Groupby
Thurs, Nov 6	Merging	Combining, Grouping and Querying Concatenating Merging/Joins Merging Part 2 Merging Example Merge Validation
Tues, Nov 11	Statistics with statsmodels	Read all readings under the Statistical Modelling heading, starting with Inference and Prediction through Beyond the Basic Model
Thurs, Nov 13	Machine Learning with sckikit-learn	JVP Chapter 5 up to but not including Hyperparameters and Model Validation Section
Tues, Nov 18	Plotting	Plotting Principles Matplotlib Basics Matplotlib Vocabulary Explicit v Implict Saving Plots
Thurs, Nov 20	More plotting
Wed Dec 12	Final project due date