Biostatistics Short Courses

A Biostatistics Short Course

A six-week short course for students and researchers interested in gaining a foundational understanding of artificial intelligence (AI) and deep learning, and their transformative impacts across various domains, including healthcare, natural language processing, computer vision and medical image analysis. The course covers core AI/deep learning concepts and their applications in different fields including the medical domain. Students will explore how deep learning models are designed, trained and evaluated, and will also explore recent advances in generative AI, including large language models (LLMs such as Llama and ChatGPT), diffusion models, and multimodal learning. This course does not require programming skills or machine learning background.

Schedule of Topics:

1. Introduction to AI, machine learning and deep learning.
2. Deep neural networks basics: neurons, layers, activation functions, etc.
3. Deep neural network training, testing and evaluation.
4. Deep learning in computer vision: from object recognition to medical imaging.
5. Deep learning for sequence data: natural language processing.
6. Generative AI (LLMs, generative neural networks, diffusion models, etc.) and multimodal learning.

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

A certificate of completion will be provided with 5 out of 6 in-class lectures attended and all online components completed.

A Biostatistics Short Course

This 6-week short course will help you build a working understanding of standard machine learning concepts, terminology, and model types so you can make informed decisions about study design and analysis and engage more effectively with data analysts. Emphasis is placed on understanding the components of a machine learning analysis and how to think critically about its role in the research process. No coding or implementation is required.

Schedule of Topics:

1. Introduction to machine learning
2. Supervised learning
3. Unsupervised learning 
4. Model development
5. Understanding model performance
6. Pairing methods to research questions and data
   

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

A certificate of completion will be provided with 5 out of 6 in-class lectures attended and all online components completed.

A Biostatistics Short Course

The Fundamentals of Statistical Literacy Short Course will help you improve your ability to critically assess literature and understand and communicate biostatistical issues. Topics are introduced and discussed in the context of the research process and tied back to phases of research that investigators will understand.

The class is structured as an hour interactive lecture followed by a discussion and practice session. Students may opt to attend an interactive lecture before the discussion session or watch a pre-recording. A certificate of completion will be provided with 5 out of 6 in-class lectures attended and all online components completed.

This introductory short course does not replace formal education in biostatistics.

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.

A Biostatistics Short Course

A six-week short course for those wanting to become acquainted with basic clinical trial methodology, either to broaden their knowledge of public health and clinical research, or to obtain a basis for more advanced study of the design and conduct of clinical trials. Introduces clinical trial design in the context of epidemiological concepts, covers various topics in the design and conduct of clinical trials, and profiles clinical trials that illustrate these issues.  

Schedule of Topics:

1. Study Design History and Comparisons
2. Identifying the Research Hypotheses and Study Population, Collecting Data
3. Exploration of Clinical Trial Designs I
4. Exploration of Clinical Trial Designs II and Analyzing the Data
5. Sample Size, Power, and Sequential Monitoring
6. Novel Study Designs and Reporting Results