Course materials

Introductory module

Objectives: set expectations; explore data science raison d’etre; introduce systems and design thinking; introduce software tools and collaborative coding; conduct exploratory/descriptive analysis of class background and interests.

Week 0

• Thursday meeting: Course orientation [slides]

• Assignments:

  • install course software and create GitHub account

  • submit GitHub username in Canvas assignment (due by 11:59 PM, Monday, September 28th)

  • fill out intake form (due by 11:59 PM, Sunday, October 4th)

  • read Peng and Parker (2022);

  • prepare a reading response (due by 11:59 PM, Monday, September 28th)

Week 1

• Tuesday meeting: On projects in(volving) data science [slides]

• Section meeting: software and technology overview [activity]

• Thursday meeting: basic GitHub actions [activity] [slides]

• Assignments due by next class meeting:

  • read MDSR 9.1 – 9.2

  • prepare a reading response (due by 11:59 PM Monday, October 5th)

  • read (and follow along with) the [tutorial for connecting Git to RStudio]

Week 2

• Tuesday meeting: Introducing class intake survey data [slides]

• Section meeting: tidyverse basics [activity]

• Thursday meeting: planning group work for analysis of survey data [slides]

• Assignments:

  • first team assignment due Friday, October 16, 11:59 PM PST. [Click here to see group memberships.] The [assignment module is available here.].

Module 1: biomarker identification

Objectives: introduce variable selection, classification, and multiple testing problems; discuss classification accuracy metrics and data partitioning; fit logistic regression and random forest classifiers in R; learn to implement multiple testing corrections for FDR control (Benjamini-Hochberg and Benjamini-Yekutieli); discuss selection via penalized estimation. Data from Hewitson et al. (2021) .

Week 3

• Tuesday meeting: introducing biomarker data; multiple testing [slides]

• Section meeting: iteration strategies [activity]

• Thursday meeting: correlation analysis; random forests [slides] [activity]

• Assignments due by next class meeting:

  • read MDSR 10.1 - 10.2

  • read Hewitson et al. (2021)

  • fill out [self/peer evaluation form about group work on Module 0], due Tuesday, October 20th, 11:59 PM PST.

  • second team assignment will be due Friday, October 30th, 11:59 PM PST. [Click here to see group memberships.]

Week 4

• Tuesday meeting: review of random forests, logistic regression [slides]; cloud computing and virtual machines [slides]

• Section meeting: logistic regression and classification metrics [activity]

References

Hewitson, Laura, Jeremy A Mathews, Morgan Devlin, Claire Schutte, Jeon Lee, and Dwight C German. 2021. “Blood Biomarker Discovery for Autism Spectrum Disorder: A Proteomic Analysis.” PLoS One 16 (2): e0246581.

Peng, Roger D, and Hilary S Parker. 2022. “Perspective on Data Science.” Annual Review of Statistics and Its Application 9: 1–20.