BST 260 Introduction to Data Science

Course Information

• Instructor: Rafael A.Irizarry
• Teaching fellows: Corri Sept, Nikhil Vytla, and Yuan Wang
• Location: Kresge 202A and 202B, Harvard School of Public Health
• Date and time: Mon & Wed 9.45 - 11:15am
• Textbooks: https://github.com/rafalab/dsbook-part-1, https://github.com/rafalab/dsbook-part-2
• Slack: https://bst260fall2024.slack.com/
• Canvas: https://canvas.harvard.edu/courses/143922/pages/Course%20Information
• GitHub repo: https://github.com/datasciencelabs/2024
• Remember to read the syllabus, listen to SD.

Lectures

Lecture slides, class notes, and problem sets are linked below. New material is added approximately on a weekly basis.

Dates	Topic	Slides	Reading
Sep 04	Productivity Tools	Intro, Unix	Installing R and RStudio on Windows or Mac, Getting Started, Unix
Sep 09, Sep 11	Productivity Tools	RStudio, Quarto, Git and GitHub	RStudio Projects, Quarto, Git
Sep 16, Sep 19	R	R basics, Vectorization	R Basics, Vectorization
Sep 23	R	Tidyverse, ggplot2	dplyr, ggplot2
Sep 25	R	Tyding data	Reshaping Data
Sep 30, Oct 02	Wrangling	Intro, Data Importing, Dates and Times, Locales, Data APIs, Web scraping, Joining tables	Importing data, dates and times, Locales, Joining Tables, Extracting data from the web
Oct 07, Oct 09	Data visualization	Data Viz Principles, Distributions, Dataviz in practice	Distributions, Dataviz Principles
Oct 16	Midterm 1		Covers material from Sep 04-Oct 11
Oct 21	Probability	Intro, Foundations for Inference	Monte Carlo, Random Variables & CLT
Oct 23	Inference	Intro, Parameter and estimates, Confidence Intervals	Parameters & Estimates, Confidence Intervals
Oct 28, Oct 30	Statistical Models	Models, Bayes, Hierarchical Models	Data-driven Models, Bayesian Statistics, Hierarchical Models
Nov 04, Nov 06	Linear models	Intro, Regression	Regression, Multivariate Regression
Nov 13, Nov 18	Linear models	Multivariate regression, Treatment effect models	Measurement Error Models, Treatment Effect Models, Association Tests, Association Not Causation
Nov 20	High dimensional data	Intro to Linear Algebra, Matrices in R	Matrices in R, Applied Linear Algebra,
Nov 25	Midterm 2		Midterm 2: cover material from Sep 04-Nov 22
Dec 02	High dimensional data	Distance, Dimension reduction	Dimension Reduction
Dec 04	Machine Learning	Intro, Metrics, Conditionals, Smoothing	Notation and terminology, Evaluation Metrics, conditional probabilities, smoothing
Dec 09, Dec 11	Machine Learning	kNN, Resampling methods, caret package, Algorithms, ML in practice	Resampling methods, ML algorithms, ML in practice
Dec 16, Dec 18	Other topics	Association is not causation, Shiny, Shiny example code	Association is not causation

Problem sets

Problem set	Topic	Due Date	Difficulty
01	Unix, Quarto	Sep 11	easy
02	R	Sep 19	easy
03	Tidyverse	Sep 27	medium
04	Wrangling	Oct 4	medium
05	Covid 19 data visualization	Oct 11	medium
06	Probability	Oct 25	easy
07	Predict the election	Nov 04	hard
08	Excess mortality after Hurricane María	Nov 15	medium
09	Matrices	Nov 22	easy
10	Digit reading	Dec 16	hard
Final Project	Your choice	Dec 20	hard

Office hour times

Meeting	Time	Location
Rafael Irizarry	Mon 8:45-9:45AM	Kresge 203
Corri Sept	Tue 3:00-4:00PM	Kresge 201
Nikhil Vytla	Wed 2:00-3:00PM	Kresge 201
Yuan Wang	Fri 1:00-2:00PM	Zoom

Acknowledgments

We thank Maria Tackett and Mine Çetinkaya-Rundel for sharing their web page template, which we used in creating this website.