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Department of Biostatistics

Key Data Science Classes

Key Data Science Classes

Below is a curated list of key data science classes taught in the Department of Biostatistics. For a full list of biostatistics classes offered at the Bloomberg School of Public Health, visit the course directory. Many are available to all students at the School regardless of degree, as well as for non-degree learners from outside the School.

The courses below are listed approximately from introductory to more advanced. Click on the titles to see full class details.

Introductory

Statistical Computing

Statistical Computing (140.776.01)

Covers the basics of practical issues in programming and other computer skills required for the research and application of statistical methods.

Prerequisite: For most classes we will be writing code in R. If you know the basics of programming in another language or the basics of R, you will be equipped to handle the material in the course, though if you haven't written code in any language in a few years you will face a steeper learning curve early on.
Consent: No consent required
Term(s): First Term

Data Science for Public Health I

Data Science for Public Health I (140.628.01)

Presents the basics of data science primarily using the python programming language.  This course covers basic tools and techniques for data science such as git and github, python, R, sql, web app development and developing interactive graphics.

Prerequisite: Prior programming experience, pre-calculus mathematics.
Consent: No consent required
Term(s): Third Term

Introduction to Data Management

Introduction to Data Management: Synchronous Online (140.630.41) & In-Person (140.630.01)

Introduces students to the principles and skills required to collect and manage research data in a public health setting. Focuses on tools for collecting data that range from spreadsheets to web-based systems, database fundamentals, data collection form design, data entry screen design, proper coding of data, strategies for quality control and data cleaning, protection and sharing of data, and integrating data from external sources. 

Prerequisite: None
Consent: Consent required for non-Biostatistics students
Term(s): Third Term

Introduction to the SAS Statistical Package

Introduction to the SAS Statistical Package (140.632.01; 140.632.02; 140.632.41; 140.605.11; 140.605.49)

Introduces students with no experience with SAS. Familiarizes them with the skills needed for effective data management and data analysis. Covers performing exploratory analysis on data including the creation of tables and graphs.

Prerequisite: 140.622 or 140.652 (may be taken concurrently), or former 140.602
Consent: No consent required
Term(s): Fourth Term/Summer Institute

Introduction to R for Public Health Researchers

Introduction to R for Public Health Researchers Synchronous Online (140.604.73) & In-Person (140.604.13)

For those who have little to no familiarity with the R programming language and want to learn more about how to use R to import, wrangle, analyze, and visualize data.

Prerequisite: Previous exposure to hypothesis testing and statistical modeling
Consent: Consent required for degree students
Term(s): Summer Institute/Winter Institute

Intermediate

Statistical Machine Learning: Methods, Theory, and Application

Statistical Machine Learning: Methods, Theory, and Application

Introduces statistical and computational foundations of modern statistical machine learning.

Prerequisite: Students are expected to be familiar with the following topics to comfortably complete this class: Linear Algebra, Intermediate Statistics, and Basic R. If you do not know these topics, it is your responsibility to do background reading to make sure you understand these concepts.
Consent: No consent required
Term Offered: First Term

Statistical Programming Paradigms and Workflows

Statistical Programming Paradigms and Workflows  

Covers the basics of statistical programming and other workflow skills required for the research and application of statistical methods.

Prerequisite: 140.776 (Statistical Computing)
Consent: No consent required
Term Offered: Second Term 

Data Science for Public Health II

Data Science for Public Health II 

Presents the basics of data science using the python programming language. This course covers supervised and unsupervised machine learning models specifically focusing on artificial intelligence, deep learning and deep neural networks.

Prerequisite: Prior programming experience, precalculus mathematics; 140.628 (Data Science for Public Health I).
Consent: No consent required
Term Offered: Fourth Term

Advanced

Advanced Data Science

Advanced Data Science  (140.711.01)

Teaches how to organize the components of a data analysis – statistics, data manipulation, and visualization. Teaches how to produce a complete data analysis to answer a targeted scientific question.

Prerequisite: The course is designed for PhD students in the Johns Hopkins Biostatistics Masters and PhD programs and assumes significant background in statistics. Specifically it is assumed you know the basics of statistics through generalized linear models, you know how to fit and interpret models, you know the basics of R and Python, and you can use version control with Github.
Consent: Consent required for anyone who is not a Biostatistics 2nd-year PhD or 2nd-year master's student.
Term(s) Offered: First Term

Advanced Statistical Computing

Advanced Statistical Computing (140.779.01)

Covers the theory and application of common algorithms used in statistical computing.

Prerequisite: Prior programming experience; at least one year of doctoral-level statistics/biostatistics theory and methods courses; 140.776 (Statistical Computing).
Consent: No consent required
Term(s) Offered: Fourth Term

AI Programming in Python for Public Health

AI Programming in Python for Public Health: Synchronous Online (140.618.49) & In-Person (140.618.11)

This course explores the transformative potential of Artificial Intelligence (AI) in public health covering the basics of python programming and building to python AI development. Students demonstrate their knowledge by downloading and implementing pretrained AI language models. 

Prerequisite: Prior programming experience.
Consent: Consent required for some students. Students must have prior programming experience to be in this course.
Term(s) Offered: Summer Institute