PHIT4DC Up-Skilling District of Columbia public health professionals!

Week 2 | Module 2A | Introduction to Python I
2025-02-06 17:00 - 20:00 (America/New_York)
Instructor: Moussa Doumbia, Ph.D.
This introductory course will be your guide to learning how to set up the working environment and use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms. In this module, you will be introduced to Python programming skills and the related libraries for accessing from multiple sources. You will learn how to create amazing data visualizations. Topical areas will include:

Setting the working environment
Programming with Python
NumPy with Python
Using pandas Data Frames to solve complex tasks.
Use pandas to handle Excel Files

Week 3 | Module 2B | Introduction to Python II
2025-02-12 17:00 - 20:00, 2025-02-13 17:00 - 20:00
Instructor: Moussa Doumbia, Ph.D.
The module builds upon Introduction to Python I. Topics we include:

Web scraping with python
Use matplotlib and seaborn for data visualizations
Use plotly for interactive visualizations

Week 4 | Module 3A | Experimentation in Data Science (A/B Testing and Statistical Analyses) I
2025-02-19 17:00 - 20:00, 2025-02-20 17:00 - 20:00
Instructor: Ronald Doku, Ph.D.
This topic covers the principles and applications of A/B testing, a foundational technique for making data-driven decisions in healthcare, business and various other industries. The session will focus on hypothesis testing, including the formulation of null and alternate hypotheses, and how they apply to experimental design. Attendees will learn about statistical concepts such as the central limit theorem, the distribution of sample means, and the importance of standard error in determining statistical significance. We will also explore how to calculate and interpret z-scores and t-scores, with a focus on understanding effect size, statistical power, and their relationship to sample size.

Week 5 | Module 3B | Experimentation in Data Science (A/B Testing and Statistical Analyses) II
2025-02-26 17:00 - 20:00, 2025-02-27 17:00 - 20:00
Instructor: Ronald Doku, Ph.D.
Part II will build on concepts discuss in Part I. We will go through examples of how to conduct an A/B test on a real-world use case, covering all necessary processes from designing the experiment to interpreting the results. Discuss situations when A/B testing doesn’t work. By the end of the session, participants will be able to design and analyze A/B tests, ensuring reliable conclusions from their experiments to support data driven decision making.

Week 6 | Spring Break
2025-03-01 - 2025-03-08

Week 7 | Module 4 | Seminal Presentation on Current Development in Data Science
2025-03-12 17:00 - 20:00, 2025-03-13 17:00 - 20:00
Instructor: TBD.
Topics to be determined.

Week 8 | Module 5 | Introduction to Machine Learning I
2025-03-19 17:00 - 20:00, 2025-03-20 17:00 - 20:00
Instructor: Ebelechukwu Nwafor, PhD
This two-part module delves into the core principles of machine learning. In Part I, students will learn about supervised learning techniques, covering linear regression, classification, and model evaluation metrics. They will explore foundational algorithms, including decision trees, support vector machines, and k-nearest neighbors, while focusing on applications in real-world scenarios.

Week 9 | Module 6 | Machine Learning II
2025-03-21 17:00 - 20:00, 2025-03-22 17:00 - 20:00
Instructor: Ebelechukwu Nwafor, PhD
Part II builds on these basics from Part I, introducing unsupervised learning techniques such as clustering and dimensionality reduction. The module will also cover key concepts like overfitting, and model selection. By the end, students will understand both theoretical and practical aspects of machine learning, with hands-on experience in building and evaluating models using tools like Scikit-Learn.

Data for Decision-Making in Healthcare
2025-07-21 17:30 - 2025-09-05 18:45
Transform Healthcare Decisions Through Data Literacy
In today’s healthcare environment, the ability to interpret and leverage data is no longer optional—it’s essential. This 8-week intensive course equips professionals with practical data literacy skills that can be immediately applied to improve decision-making in clinical and operational settings.
Apply Today
Duration
8 weeks
(July 21 – September 5)
Schedule
Tuesdays and Thursdays, 5:30 – 6:45 PM
Course Overview
Led by Professor Michael Marcus, this hands-on program bridges the gap between healthcare expertise and data fluency. Through a balanced combination of weekly lectures and laboratory sessions, you’ll progress from understanding basic healthcare data types to creating professional dashboards that drive meaningful decisions.
What makes this course unique:

Work with real-world healthcare datasets relevant to your professional context
Develop practical skills in data visualization and dashboard creation
Learn to communicate data insights effectively to various stakeholders
Gain exposure to google sheets and Gemini AI for simple, AI-assisted formula generation
Complete a personalized capstone project addressing a real healthcare challenge

Who Should Attend
This course is designed for professionals who need to make or influence data-informed decisions, including:

Anyone seeking to enhance their data literacy
Clinical leaders and department managers
Healthcare administrators and operations staff
Quality improvement specialists
Health information professionals

What You’ll Learn

Identify and work with different types of healthcare data (clinical, operational, financial)
Evaluate data quality and relevance for healthcare decision-making
Create effective dashboards and visualizations that tell a compelling story
Develop data-informed recommendations that drive action
Present data insights with confidence and clarity
Utilize Google Sheets and Gemini AI to enhance data analysis capabilities

By the end of this course, you’ll possess the skills to transform raw healthcare data into actionable insights, positioning yourself as a more valuable contributor to your organization’s success in an increasingly data-driven healthcare landscape.
All necessary software licenses will be provided, including Excel and Power BI. Students should bring laptops to each session.
