Faculty and staff at LSU, Southern Univ. and Southeastern Univ. are working together on promoting Computing and Data Science Education. Our focus is on enhancing the preparation of students from the early grades throughout graduate school in the essential skills and dispositions needed to succeed in a computationally-driven world. Under the umbrella of computing, we plan to connect faculty from computer science, computational science, mathematics, engineering, data science, cybersecurity, information systems, and education. This interdisciplinary interinstitutional Working Group will support computing literacy across all disciplines, as all fields need basic computational thinking and programming to advance knowledge. The Working Group will engage faculty and students in the adoption of computational techniques for real-world problem solving, including the use of modeling, abstraction, and the management of complexity.
List of faculty and staff participating in the group. If you are interested to be included in the list please email Juana Moreno (email@example.com). Our next meeting is Thursday, April 25th, 11:15 to 12:15, at the Digital Media Center, room 1034.
HPC@LSU is planning to offer a JupyterHub server to LONI HPC users. In the meantime here are the instruction on how to run Jupyter Notebook files (*.ipynb) on the Google Colab.
- Open a browser and go to https://colab.research.google.com;
- The welcome screen itself is a Jupyter Notebook, so you can start experimenting right away. The other options are:
- Create a new Python2 or Python3 notebook by clicking on "File" then "New notebook";
- Click on "File" then "Open notebook", where you will have the options of opening examples provided by Google, loading notebooks in a Github repo, loading notebooks from your Google Drive, and uploading a notebook file from your computer. Note that the last two options require that you have a Google account and log in.
Introduction to Computing for Biologists by Jeremy Brown (Fall 2018)
Computational Science by Juana Moreno (Fall 2017)
Introduction to Data Science by John Burris (Approved April 23, 2018)
Computational Biology by April Wright (Fall 2018)
Computer Science Teaching Methods by Fernando Alegre (Fall 2019)
Simulation and Modeling by Lisa Kuhn (Spring 2018)
Digital Agriculture: From Big Data to Actions by Thanos Gentimis (Spring 2019)
Basic Intro to Pandas by Siddharth Soni (Summer 2018)
This course provides students interested in biology with practical computing skills including working in a Linux environment, programming in Python, and using version control. These skills should allow students to participate in computationally oriented research, begin writing their own code, and explore new ideas through simulation.
The course covers numerical methods used to solve, analyze, and simulate a broad range of science problems. Topics include: Error, accuracy, and stability, Integration of functions, Differentiation, Root finding, Solution of linear algebraic equations: Matrix computation, Random number generation, Monte Carlo methods, Thermodynamic simulations: Ising model, Differential equations.
Topics include basics of programming for data science, data science libraries, random variables, descriptive statistics, basic linear algebra, scaling and plotting data, model assessment, introduction to supervised and unsupervised learning.
This course explore the fundamentals of managing data and performing analyses computationally. The course is intended for biologists who do not have experience with programming or computational sciences.
This course is specifically created to support the U.S. Department of Education Supporting Effective Educator Development award and its goal of recruiting CS majors to teach secondary education. The course serves four different student groups: CS majors seeking initial teacher certification; STEM majors seeking add-on CS certification; CS majors who want to learn how to design courses for corporate training or online classes; and students seeking basic computing/coding literacy.
Construction and use of computer and mathematical models, parameter estimation, simulation techniques, applications of simulation, examples, and cases and studies taken from physical, social and life sciences, engineering, business and information sciences.
The main goal of this course is to introduce ideas from machine learning, artificial intelligence, data management and image processing and show how they can be applied to data sets coming from all areas related to the environment and agriculture.
Class notes in Google Drive.
Videos from some of the classes: 9 videos
A group of graduate students at LSU Physics & Astronomy prepared a series of lessons to teach each other basic data analysis. Here is an example of basic use of the Python Data Analysis Library Pandas.