Course Description

Save a bundle and receive 30% off the individual course rate when you register for BIOF 309 and BIOF 475!

Bundled Courses: 
BIOF 309 | Introduction to Python
BIOF 475| Introduction to Data Science

BIOF 309  is designed for non-programmers looking to enhance their biomedical science research capabilities through coding. Through special focus given to real world applications and data analysis in the biomedical field, you will learn to work with Python to translate complex biomedical data into clear and insightful data analyses and visualizations. You will develop a comprehensive foundation to Python as the course guides you through the essentials of syntax and semantics, data structures, data cleaning, and operational frameworks. Practical sessions will introduce you to using Google Colaboratory notebooks for interactive programming and managing virtual environments for sophisticated biomedical computing tasks using reusable codes. By the end of the course, you will be equipped with the tools to harness Python's full potential, bridging the gap between computational skills and biomedical research excellence.

In BIOF 475, students will explore the power of computational analyses in discovering trends/patterns in large datasets. Learning from data in order to make useful predictions or obtain insights is a cornerstone of modern science. The goal of this course is to introduce students to the basic tools and workflows for doing this, with a focus on biological- and health-related data. In this course, students will learn how to use Python-based tools, particularly Numpy, SciKit-learn, Pandas, and Matplotlib.

Learner Outcomes

• Demonstrate basic knowledge of object-oriented programming in Python
• Create transparent and reproducible notebooks in Google Colaboratory
• Identify foundational principles in writing high-quality code
• Perform hands-on exercises with entry-level concepts in scientific data analysis using Python.
• Run a program of your own design in the terminal or a cloud-based environment.
• Load and prepare data for downstream analysis, including text-based data
• Describe features of the input dataset via summary statistics and informative data visualizations
• Utilize supervised (ex: linear, logistic, and multinomial regression) and unsupervised (ex: clustering) machine learning approaches
• Compare & contrast different machine learning models
• Critique model fit and suggest methods for refining model accuracy
• Effectively communicate the results of an in-depth data science analysis

Microcredential(s)

These courses apply toward the Bioinformatics Endeavor digital badge.

What FAES Learners are Saying

BIOF 309: "The guided code before each assignment was really great - that was almost sufficient to understand and complete each work. Extra reading material was nice as it (for me) worked as recommendations of where to find more info." - FAES Learner

BIOF 475:  "I was a fan of the lecture to lab format, which made the cross from theoretical and practical material seamless. I also appreciated the weekly discussions, which promoted thinking in a practical sense about how the material we learned can be applied in real life." - FAES Learner
 

Textbook Information