Computational Tools for Research in Biology

BSC4452 and BSC6451 Registration information for Fall 2024

• BSC 4452: Class 19032

  

• BSC 6451: Class 22340

Course Description

The early 2000’s were characterized as the era of Big Data, with researchers across disciplines finding that large volumes of diverse data transformed research. In the past decade, this has again been transformed by the re-emergence of artificial intelligence and machine learning systems aiding in the interpretation of Big Data. As data types and volumes continue to grow, knowledge of scripting, database management, and advanced computing, including AI fundamentals, is critical for researchers across disciplines.

This course introduces students to the tools needed to be proficient, computationally enabled researchers, providing a foundation in Linux and Bash scripting for data management, Python programming, basic SQL database fundamentals, and an introduction to artificial intelligence methods.

The course assumes no prior coding or command-line skills and covers concepts that enable students to apply new technologies to a wide array of research questions. A foundation in data management and analysis concepts opens doors for well-trained researchers and allows them to work in multidisciplinary fields.

The course assumes no prior coding or command line skills, and covers concepts that will provide the ability for students to apply new technologies to a wide array of research questions. A foundation in data management and analysis concepts opens doors for well-trained researchers and allows them to work in multidisciplinary fields.

Course Organization

The course is divided into four main sections:

• Section 1: Linux command line and Bash scripting; version control using Git and GitHub; using high-performance computing resources
• Section 2: Python scripting
• Section 3: SQL database introduction and integration with Python
• Section 4: A brief introduction to Artificial Intelligence

Course Objectives

• Demonstrate how technology infrastructure can improve research and open new avenues of investigation.
• Competently navigate the Unix/Linux command line interface.
• Effectively and efficiently manipulate text files, performing complex regular expression replacements, reformatting and merging files in various ways.
• Raise and address current issues through class participation and discussion.
• Use AI-assisted tools to generate and debug code.
• Use High Performance Computing resources such as the UFIT Research Computing for cluster-based analyses. Including batch scripting and running multi-processor applications (threaded and MPI).
• Explain the basic anatomy of computer scripts/programs, with particular focus on Python scripting.
• Construct analytical pipelines to accomplish complex tasks.
• Describe basic database design, creation and manipulation. Perform scripted database operations for information discovery, data exploration and research data curation.
• Have a basic understanding of research graphics formats, preparation and manipulation
• Have a basic understanding of artificial intelligence and gain hands-on experience with computer vision.

Course Textbooks

The main texts for the course are:

• The Linux Command Line: http://linuxcommand.org (my notes and referenced page numbers will be based on the 25.12 version of the PDF, Seventh Internet Edition, from Jan. 5, 2026.)
• Python For Everyone: https://www.py4e.com/book.php

Each of these is available as a free PDF download or for purchase in print. Because there are no textbook costs for this course, it has been recognized by Affordable UF as an affordable course.

Course Syllabus

The full course syllabus is available on the UF SimpleSyllabus site.

Course Calendar

For readings, there may be links to pages with my notes and additional explanations on the content from the texts. The texts are abbreviated as TLCL = The Linux Command Line; Py4E = Python for Everyone.