| Key | Detail |
|---|---|
| Title | Foundations of Health Data Science (FHDS) |
| Session | 2024-24 (Cohort 01) |
| Credit | University 4 Credit Hour Equivalent |
| Level | Beginners to Advance |
| Number of Classes | 25 |
| Course Type | Online Live Session (Zoom) – Recording will be provided |
| Mode of Instruction | Bangla |
| Class Start | July 1, 2024 |
| Class Time | 9:00 pm – 10:30 pm (complete schedule will be available soon) |
| Role | Name | Position & Affiliation |
|---|---|---|
| Lead Organizer & Instructor | Md. Jubayer Hossain | Founder & Executive Director, CHIRAL Bangladesh |
| Instructor | Tasmim Rahman Adib | Backend Engineer, Health Data Science Lab, CHIRAL; Software Engineer, Penta Global Limited; Computer Science and Engineering, University of Dhaka |
| Instructor | Muhibullah Shahjahan | Research Assistant, Bioinformatics Division, CHIRAL Bangladesh; Department of Microbiology, Jagannath University |
| Instructor | Md. Mahadi Hassan | Research Assistant, Health Data Science Lab, CHIRAL; Public Health & Informatics, Jahangirnagar University |
| Teaching Assistant | Md Kaif Ibn Zaman | Computer Science and Engineering, Jahangirnagar University; Junior Research Assistant, Health Data Science Lab, CHIRAL |
| Teaching Assistant | Rakibul Hasan | Research Assistant, Health Data Science Lab, CHIRAL |
Foundations of Health Data Science (FHDS) is an interdisciplinary course designed to introduce students to the fundamental concepts, methodologies, and applications of data science in the context of healthcare and public health. This course provides a comprehensive understanding of how data science techniques can be leveraged to extract valuable insights, improve patient outcomes, and inform decision making in healthcare settings.
This course is suitable for individuals with varying backgrounds and interests, including:
- Healthcare professionals seeking to enhance their data-analysis skills.
- Data analysts specializing in the healthcare domain.
- Students interested in pursuing careers in health informatics and data science.
- Researchers exploring data-driven approaches in healthcare.
- Public health professionals interested in utilizing data for policy development and epidemiological studies.
By the end of this course, participants will:
- Gain a foundational understanding of key concepts in health data science.
- Develop proficiency in data collection, preprocessing, and cleaning techniques specific to healthcare datasets.
- Learn various statistical methods and machine learning algorithms that are commonly used in health data analyses.
- Understand ethical considerations and privacy concerns related to health data usage.
- Explore real-world applications of health data science in clinical settings, public health surveillance, and biomedical research.
- Acquire practical skills through hands-on exercises and projects using relevant software tools and programming languages.
No prior experience in data science was required. Basic knowledge of statistics and programming (e.g., Python, R) would be beneficial, but not mandatory.
| No. | Lecture | Lab/Quiz |
|---|---|---|
| 1 | Introduction to Health Data Science | Quiz: Foundations of Health Data Science |
| 2 | Machine Learning Basics | Quiz: Introduction to Machine Learning Concepts |
| 3 | Deep Learning Basics | Quiz: Fundamentals of Deep Learning |
| 4 | Fundamentals of Python: Part 1 | Lab: Introduction to Python Programming |
| 5 | Fundamentals of Python: Part 2 | Lab: Intermediate Python Programming |
| 6 | Scientific Computing with NumPy: Linear Algebra | Lab: NumPy Basics for Linear Algebra Operations |
| 7 | Scientific Computing with NumPy: Calculus | Lab: Applying Calculus in Data Analysis with NumPy |
| 8 | Scientific Computing with NumPy: Statistics | Lab: Exploring Statistical Functions in NumPy |
| 9 | Scientific Computing with NumPy: Probability | Lab: Probability Distributions and Sampling in NumPy |
| 10 | Data Wrangling with Pandas: Part 1 | Lab: Data Cleaning and Manipulation with Pandas |
| 11 | Data Wrangling with Pandas: Part 2 | Lab: Handling Missing Data and Group Operations in Pandas |
| 12 | Data Visualization with Matplotlib and Seaborn: Part 1 | Lab: Creating Basic Plots with Matplotlib |
| 13 | Data Visualization with Matplotlib and Seaborn: Part 2 | Lab: Enhancing Plots with Seaborn |
| 14 | Classification Models in Machine Learning | Lab: Implementing Classification Models in Scikit-learn |
| 15 | Regression Models in Machine Learning | Lab: Building Regression Models with Scikit-learn |
| 16 | Clustering Models in Machine Learning | Lab: Clustering Analysis with Scikit-learn |
| 17 | Deep Learning Models: Convolutional Neural Networks (CNNs) | Lab: Building and Training CNNs with TensorFlow/Keras |
| 18 | Deep Learning Models: Recurrent Neural Networks (RNNs) | Lab: Sequence Modeling with RNNs using TensorFlow/Keras |
| 19 | Deep Learning Models: Long Short Term Memory Networks (LSTMs) | Lab: Implementing LSTMs for Time Series Prediction |
| 20 | Project #01: Classification (Text Data) | Lab: Text Classification Project Implementation |
| 21 | Project #02: Regression (Text Data) | Lab: Regression Analysis on Textual Data |
| 22 | Project #03: Deep Learning (Image Data) | Lab: Image Classification Project with Deep Learning |
| 23 | Project #04: Deep Learning (Time Series) | Lab: Time Series Forecasting using Deep Learning Models |
| 24 | Project Discussion | Quiz: Project Discussion and Reflection |
| 25 | Interview Preparation: Health Data Science Interview Questions | Quiz: Interview Question Practice |
- Lectures: Interactive lectures delivered by experienced instructors.
- Hands-on Labs: Practical sessions to apply concepts learned in lectures.
- Case Studies: Analysis of real-world healthcare datasets and scenarios.
- Capstone Project: A culminating project in which participants apply learned skills to solve a relevant health data problem.
- Jain, V., & Chatterjee, J. M. (2020). Machine learning with health care perspective. Cham: Springer, 1-415.
- Xiao, C., & Sun, J. (2021). Introduction to deep learning for healthcare. Springer Nature.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.
| No. | Item | Percentage (%) | Counts |
|---|---|---|---|
| 1 | Lab assignments | 50 | 20 |
| 2 | Final Project Presentation | 25 | 1 |
| 3 | Quizzes | 20 | 5 |
| 4 | Attendance | 5 | |
| Total | 100 |
The grading scale for this course consists of the standard scale, including minus grades, below: 94 - 100 = A 90 – 93.9 = A- 87 – 89.9 = B+ 83 – 86.9 = B 80 – 82.9 = B = 77 – 79.9 = C+ 73 – 76.9 = C 70 – 72.9 = C- 67 – 69.9 = D+ 63 – 66.9 = D 60 – 62.9 = D- Below 60 = F NB: Participants who will carry F grade she/he will not be certified.
There were 20 lab assignments for the lab component, with 100 points for each. The average score was 50% of the final grade. Owing to the size of the class, the assignments should be handed in by the due date. Otherwise, 15 points will be AUTOMATICALLY deducted per day after the due date. Assignments received after the 7-day late period will not be graded, and AUTOMATICALLY will receive 0 points. This timing allows you to finish with one week’s material before starting the next unit’s material.
There are 5 quizzes that occur each with 100 points. The average score was 20% of the final grade. This will test you on lecture materials covered within the five most recent units. Quizzes can be completed only if the instructor receives a prior notification of absence.
Students were asked to select a specific topic based on their interests and complete a data analysis of individual projects using the methods covered in class, summarized in a formal report in R. The final project and presentation accounted for 25% of the final grade. Students were expected to give a maximum of seven minutes about their projects. After the final presentation, they were required to hand in the R-script project along with the dataset in a designated format. The final report is due by [Comming!]
The university’s policy is that all students should attend all classes. Of course, not all students will, or can, attend all classes due to intercollegiate athletic events, university-sanctioned co-curricular activities, course field trips, religious holidays, family emergencies, and personal illness. As a result, students are allowed to miss a maximum of 10% of the class meeting time without a grade penalty. The student is responsible for all course materials and graded work because of absence. It is at the discretion of the professor to decide if, when, where, and how the missed work is completed. If something unexpected occurs, please inform the instructor in advance. The absence of such notice prevents students from completing assignments.
Participants need to bring their headphones to listen to on the Zoom session during class time.
Emails (training.chiralbd@gmail.com) will be answered as quickly as possible (typically within 24 h) but may not generate an immediate response. If more than 48 hours passed without a response, please resend the message. Please let us know if you have problems that interfere with your progress during this course. We will do all we can help you. Please see us as soon as possible if you experience difficulty at any point in the course.
(The) non-discrimination policy of CHIRAL Bangladesh affirms a commitment to diversity in a variety of forms, believing that diversity is more than an all-inclusive list of demographics. The CHIRAL Bangladesh prohibits discrimination and harassment based on race or ethnicity, marital status, sex, age, gender expression or identity, sexual orientation, religion, national origin, disability, or veteran status. The discrimination or harassment of members of the CHIRAL Bangladesh falls short of our community standards and will not be tolerated.
Academic honesty is expected at all times. Academic dishonesty includes claiming someone else’s work as your own (e.g., plagiarism), seeking an unfair advantage over other students in taking a test or fulfilling an assignment, and fraud. Any offense will result in a zero or grade of F for the exam or assignment in question and may result in failure of the course. Infractions will be reported to the student’s advisor and to the Associate Academic Coordinator.
Participants are allowed to make up assignments ONLY as the result of illness or other unanticipated circumstances warranting a medical excuse and resulting in the student missing homework or exam, consistent with CHIRAL policy. Documentation from a health care provider is required. For any other non-medical reason, assignments missed will receive a penalty of 10 points per day after the due date, and the mid-exam missed will receive a zero point.
© 2024 Md. Jubayer Hossain and CHIRAL Bangladesh. All rights reserved.
No part of this course materials, including lectures, text, images, and videos, may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, write to the course publisher, addressed “Attention: Permissions Coordinator,” at the address below.
Md. Jubayer Hossain Founder & Executive Director CHIRAL Bangladesh Email: contact.jubayerhossain@gmail.com