Course Schedule

Date	Event
Week 1
Aug. 23	What is Data Science? and Types of Data Lecture: What is Data Science? Lecture: Types of Data Homework 1: Party Words Survey (Due Tuesday, Aug. 24) In Your Lab Section: lab_intro (Due Monday, Aug. 30)
Aug. 25	Experimental Design and Blocking Lecture: Experimental Design and Blocking Homework 2: PrairieLearn Mastery (Due Thursday, Aug. 26)
Aug. 27	Python for Data Science: Introduction to DataFrames Lecture: Python for Data Science: Introduction to DataFrames Homework 3: PrairieLearn Mastery (Due Sunday, Aug. 29)
Week 2
Aug. 30	Row Selection with DataFrames Lecture: Row Selection with DataFrames Homework 4: PrairieLearn (Due Tuesday, Aug. 31) In Your Lab Section: lab_pandas (Due Monday, Sept. 6)
Sep. 1	Confounders and Stratification Lecture: Observational Studies, Confounders, and Stratification Homework 5: PrairieLearn (Due Thursday, Sept. 2)
Sep. 3	Simpson's Paradox Lecture: Simpson's Paradox Homework 6: PrairieLearn (Due Sunday, Sept. 5)
Week 3
Sep. 6	Holiday (No Lecture) -- Labor Day
Sep. 8	Conditionals on DataFrames Lecture: DataFrames with Conditionals Homework 7: PrairieLearn (Due Thursday, Sept. 9) In Your Lab Section: lab_exp_design (Due Monday, Sept. 13)
Sep. 10	Exploratory Data Analysis (EDA) Lecture: Software Version Control with git Lecture: Exploratory Data Analysis Overview Homework 8: PrairieLearn (Due Sunday, Sept. 12)
Week 4
Sep. 13	Descriptive Statistics Lecture: Descriptive Statistics Homework 9: PrairieLearn (Due Tuesday, Sept. 14) In Your Lab Section: lab_pandas (Due Monday, Sept. 20)
Sep. 15	Grouping Data Lecture: Grouping Data Homework 10: PrairieLearn (Due Tuesday, Sept. 16)
Sep. 17	Histograms and Box Plots Lecture: Histograms Lecture: Quartiles and Box Plots Homework 11: PrairieLearn (Due Tuesday, Sept. 19)
Week 5
You will take your Midterm 1 exam in the CBTF anytime from Sept. 20 - Sept. 25. (You can begin to sign up for a slot on Sept. 9.)
Sep. 20	Visualization using Python Lecture: Basic Data Visualization in Python Homework 12: PrairieLearn (Due Saturday, Sept. 25)
Sep. 22	Midterm Day Off (No Lecture)
Sep. 24	Probability Introduction Lecture: Probability Introduction w/ The Monde Hall Problem Homework 13: PrairieLearn (Due Sunday, Sept. 26)
Week 6
Sep. 27	Random Numbers in Python Lecture: Random Numbers in Python Homework 14: PrairieLearn (Due Tuesday, Sept. 28)
Sep. 29	Multiplication Rule Lecture: Multi-event Probability: Multiplication Rule Homework 15: PrairieLearn (Due Thursday, Sept. 30)
Oct. 1	Addition Rule Lecture: Multi-event Probability: Addition Rule Homework 16: PrairieLearn (Due Sunday, Oct. 3)
Week 7
Oct. 4	Conditional Probability Lecture: Conditional Probability Homework 17: PrairieLearn (Due Tuesday, Oct. 5)
Oct. 6	Bayes' Rule Lecture: Bayes' Theorem Homework 18: PrairieLearn (Due Thursday, Oct. 7)
Oct. 8	Simulation Introduction Lecture: Overview of Simulation Homework 19: PrairieLearn (Due Sunday, Oct. 10)
Week 8
Oct. 11	For-Loops and Simple Simulation in Python Lecture: For-Loops in Python Lecture: Simple Simulations in Python Homework 20: PrairieLearn (Due Tuesday, Oct. 12)
Oct. 13	Sample Space and State Space Lecture: Sample Space and State Space Homework 21: PrairieLearn (Due Tuesday, Oct. 14)
Oct. 15	Conditionals in Python Lecture: Conditionals in Python Homework 22: PrairieLearn (Due Tuesday, Oct. 17)
Week 9
Oct. 18	Functions in Python Lecture: Functions in Python Homework 23: PrairieLearn (Due Tuesday, Oct. 19) Lab: lab_similarity (Due Tuesday, Oct. 25)
Oct. 20	Project! Lecture: Project Introduction, Released LIVE on Zoom at 12:00noon on Wednesday (will be recorded) Project 1: PrairieLearn (Due Tuesday, Nov. 1)
Oct. 22	Normal Distribution Lecture: Normal Distribution
Week 10
You will take your Midterm 2 exam in the CBTF anytime from Oct. 25 - Oct. 30. (You can begin to sign up for a slot on Oct. 14.)
Oct. 25	Midterm 2 in CBTF (No Lecture) Homework 24: PrairieLearn (Due Tuesday, Oct. 19)
Oct. 27	Law of Large Numbers Lecture: Law of Large Numbers
Oct. 29	Random Variables in Statistics Lecture: Random Variables
Week 11
Nov. 1	Bernoulli and Binomial Distributions Lecture: Bernoulli and Binomial Distributions Homework 25: PrairieLearn (Due Tuesday, Nov. 02)
Nov. 3	Distributions in Python Lecture: Python Functions for Distributions Homework 26: PrairieLearn (Due Thursday, Nov. 04)
Nov. 5	Central Limit Theorem (CLT) Lecture: Central Limit Theorem Homework 27: PrairieLearn (Due Sunday, Nov. 07)
Week 12
Nov. 8	Polling and Sampling Lecture: Polling and Sampling Homework 28: PrairieLearn (Due Tuesday, Nov. 09)
Nov. 10	Confidence Intervals Lecture: Confidence Intervals Homework 29: PrairieLearn (Due Thursday, Nov. 11)
Nov. 12	Hypothesis Testing Lecture: Hypothesis Testing Homework 30: PrairieLearn (Due Sunday, Nov. 14)
Week 13
Nov. 15	Machine Learning Overview Lecture: Overview of Machine Learning Project 2: You and Data Science
Nov. 17	Correlation Lecture: Correlation Homework 31: PrairieLearn (Due Thursday, Nov. 18)
Nov. 19	Linear Regression Lecture: Linear Regression Homework 32: PrairieLearn (Due Sunday, Nov. 28)
Week 14
Nov. 22	Fall Break (No Lecture)
Nov. 24	Fall Break (No Lecture)
Nov. 26	Fall Break (No Lecture)
Week 15
Nov. 29	Machine Learning Lecture: Machine Learning Models in Python with sk-learn Homework 33: PrairieLearn (Due Sunday, Nov. 30)
Dec. 1	Clustering Lecture: Clustering Homework 34: PrairieLearn (Due Sunday, Dec. 2)
Dec. 3	Machine Learning in Python Lecture: Towards Machine Learning in Python Homework 35: PrairieLearn (Due Tuesday, Dec. 13)
Week 16
You will take your Final Exam exam in the CBTF anytime from Dec. 9 - Dec. 13. (You can begin to sign up for a slot on Nov. 4.)
Dec. 6	Project 2
Dec. 8	Study for Final Exam