Teaching Assistant

The TA for this course is Yuhan Hao. Please email Yuhan.Hao at nyumc.org for any clarifications regarding homeworks or classworks.

Office Hours

Tuesday: 2:30 pm to 4 pm and by appointment

Course Information

Official Course Name: Prgramming for Data Analysis (BMIN-GA 1005/BMSC-GA 4486)

Meeting Schedule: Every Tuesday and Thursday starting Sept. 7th through Nov. 16 between 1 pm - 2.30 pm

Meeting Location: Translational Research Building (TRB) @ 227, E.30th St. conference room 618 except:

September 28th: the class will be held in the Smilow Seminar Room on the 1st floor (Yellow Pathway) at Tisch Hospital - 550 1st Avenue.

October 3rd: the class will be held at The Alexandria Center for Life Science in the Alexandria East conference room ERSP 901 - 430 E 29th St.

Important room notice for entry at the Alexandria building! Available 9am-5pm. Pre-approved security clearance is needed to access the Alexandria Building. Seating: 18 extra chairs in room. Room can be split down the middle. (This becomes a giant white board) Kitchen/pantry inside of room. Two exit doors. A point person should arrive 20 minutes prior to retrieve a guest pass from the front desk. Guests need to be swiped into our space to gain access, so we suggest the point person swipe other guests in at the elevator lobby level and guide them to the space.
Either I or someone on my behalf will be the point person.

October 24th: the class will be held at 650 First Avenue, 5th Floor, conference room 525.

Learning Objectives

Towards the end of this course the student will exhibit in-depth understanding of data science and analysis methods as well as proficiency in R. The student will produce a portfolio of data analysis projects from the course that demonstrates mastery of analysis and visualization methods. He /She will be equipped for analysis of biomedical and genomic data sets. Another main objective of the course is to communicate statistical results correctly and effectively.

Course Overview

This course is designed to empower students to learn R programming language to conduct data science. We will study a wide range of topics, including, handing and querying databases, exploratory/confirmatory analysis and visualization in R. We will closely follow the book R for Data Science, however the emphasis will be given to working with biomedical data than datasets illustrated and used in the textbook.
This course does not have any pre-requisites.

General Policies

Late/missed work: You must adhere to the due dates for all required submissions. If you miss a deadline, then you will not get credit for that assignment/post.
Incompletes: No "Incompletes" will be assigned for this course unless we are at the very end of the course and you have an emergency.
Responding to Messages: I will check e-mails daily during the week, and I will respond to course related questions within 48 hours.
Announcements: I will make announcements throughout the semester by e-mail.
Make sure that your email address is updated; otherwise you may miss important emails from me.
Safeguards: Always back up your work on a safe place (electronic file with a backup is recommended) and make a hard copy. Do not wait for the last minute to do your work. Allow time for deadlines.
Plagiarism: Plagiarism, the presentation of someone else's words or ideas as your own, is a serious offense and will not be tolerated in this class. The first time you plagiarize someone else's work, you will receive a zero for that assignment. The second time you plagiarize, you will fail the course with a notation of academic dishonesty on your official record.

Course Assessment (see the project grading rubric below)

Programming Assignments (40%)
Directed Insights (25%)
Final Project (35%)

Recommended Reading

1. R for Data Science by Garrett Grolemund & Hadley Wickham (available here)
2. R in Action by Robert I. Kabacoff
3. Several online tutorials (just type "R tutorial" in google and follow the lead)

Project Grading Rubric (20% each)

You need to document and demonstrate all aspects of data science foundations discussed in the class.

1. Correctly apply tools and techniques of data preparation and wrangling
    a. Missing data handling, joining, or other transformations, removing outliers etc.
    b. Gathering, spreading data (if needed)

2. Use Exploratory Data Analysis and `dplyr` transformation methods to identify structure and correlations in the data

3. Formulate questions and possible ways of analysis and visualization
    a. Identify appropriate visualization methods for analysis of your data set
    b. Choose the right geoms for the questions at hand

4. Correctly interpret results of analysis (clinical/biological significance)
    a. Demonstrate domain specific knowledge of clinical data
    b. Propose an hypothesis based on visualization and results
    c. Compare the usefulness of the obtained results/conclusions

5. Formulate appropriate plans for validation, further analysis, or to collect additional data needed.